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外⽂参考⽂献翻译-中⽂基于4G LTE技术的⾼速铁路移动通信系统KS Solanki教授，Kratika ChouhanUjjain⼯程学院，印度Madhya Pradesh的Ujjain摘要：随着时间发展，⾼速铁路（HSR）要求可靠的，安全的列车运⾏和乘客通信。

为了实现这个⽬标，HSR的系统需要更⾼的带宽和更短的响应时间，⽽且HSR的旧技术需要进⾏发展，开发新技术，改进现有的架构和控制成本。

为了满⾜这⼀要求，HSR采⽤了GSM的演进GSM-R技术，但它并不能满⾜客户的需求。

因此采⽤了新技术LTE-R，它提供了更⾼的带宽，并且在⾼速下提供了更⾼的客户满意度。

本⽂介绍了LTE-R，给出GSM-R与LTE-R之间的⽐较结果，并描述了在⾼速下哪种铁路移动通信系统更好。

关键词：⾼速铁路，LTE，GSM，通信和信令系统⼀介绍⾼速铁路需要提⾼对移动通信系统的要求。

随着这种改进，其⽹络架构和硬件设备必须适应⾼达500公⾥/⼩时的列车速度。

HSR还需要快速切换功能。

因此，为了解决这些问题，HSR 需要⼀种名为LTE-R的新技术，基于LTE-R的HSR提供⾼数据传输速率，更⾼带宽和低延迟。

LTE-R能够处理⽇益增长的业务量，确保乘客安全并提供实时多媒体信息。

随着列车速度的不断提⾼，可靠的宽带通信系统对于⾼铁移动通信⾄关重要。

HSR的应⽤服务质量（QOS）测量，包括如数据速率，误码率（BER）和传输延迟。

为了实现HSR的运营需求，需要⼀个能够与 LTE保持⼀致的能⼒的新系统，提供新的业务，但仍能够与GSM-R长时间共存。

HSR系统选择合适的⽆线通信系统时，需要考虑性能，服务，属性，频段和⼯业⽀持等问题。

4G LTE系统与第三代（3G）系统相⽐，它具有简单的扁平架构，⾼数据速率和低延迟。

在LTE的性能和成熟度⽔平上，LTE- railway（LTE-R）将可能成为下⼀代HSR通信系统。

⼆ LTE-R系统描述考虑LTE-R的频率和频谱使⽤，对为⾼速铁路（HSR）通信提供更⾼效的数据传输⾮常重要。

毕业论文英文参考文献与译文

Inventory managementInventory ControlOn the so-called "inventory control", many people will interpret it as a "storage management", which is actually a big distortion.The traditional narrow view, mainly for warehouse inventory control of materials for inventory, data processing, storage, distribution, etc., through the implementation of anti-corrosion, temperature and humidity control means, to make the custody of the physical inventory to maintain optimum purposes. This is just a form of inventory control, or can be defined as the physical inventory control. How, then, from a broad perspective to understand inventory control? Inventory control should be related to the company's financial and operational objectives, in particular operating cash flow by optimizing the entire demand and supply chain management processes (DSCM), a reasonable set of ERP control strategy, and supported by appropriate information processing tools, tools to achieved in ensuring the timely delivery of the premise, as far as possible to reduce inventory levels, reducing inventory and obsolescence, the risk of devaluation. In this sense, the physical inventory control to achieve financial goals is just a means to control the entire inventory or just a necessary part; from the perspective of organizational functions, physical inventory control, warehouse management is mainly the responsibility of The broad inventory control is the demand and supply chain management, and the whole company's responsibility.Why until now many people's understanding of inventory control, limited physical inventory control? The following two reasons can not be ignored:First, our enterprises do not attach importance to inventory control. Especially those who benefit relatively good business, as long as there is money on the few people to consider the problem of inventory turnover. Inventory control is simply interpreted as warehouse management, unless the time to spend money, it may have been to see the inventory problem, and see the results are often very simple procurement to buy more, or did not do warehouse departments .Second, ERP misleading. Invoicing software is simple audacity to call it ERP, companies on their so-called ERP can reduce the number of inventory, inventory control, seems to rely on their small software can get. Even as SAP, BAAN ERP world, the field ofthese big boys, but also their simple modules inside the warehouse management functionality is defined as "inventory management" or "inventory control." This makes the already not quite understand what our inventory control, but not sure what is inventory control.In fact, from the perspective of broadly understood, inventory control, shouldinclude the following:First, the fundamental purpose of inventory control. We know that the so-called world-class manufacturing, two key assessment indicators (KPI) is, customer satisfaction and inventory turns, inventory turns and this is actually the fundamental objective of inventory control.Second, inventory control means. Increase inventory turns, relying solely on the so-called physical inventory control is not enough, it should be the demand and supply chain management process flow of this large output, and this big warehouse management processes in addition to including this link, the more important The section also includes: forecasting and order processing, production planning and control, materials planning and purchasing control, inventory planning and forecasting in itself, as well as finished products, raw materials, distribution and delivery of the strategy, and even customs management processes.And with the demand and supply chain management processes throughout the process, it is the information flow and capital flow management. In other words, inventory itself is across the entire demand and supply management processes in all aspects of inventory control in order to achieve the fundamental purpose, it must control all aspects of inventory, rather than just manage the physical inventory at hand.Third, inventory control, organizational structure and assessment.Since inventory control is the demand and supply chain management processes, output, inventory control to achieve the fundamental purpose of this process must be compatible with a rational organizational structure. Until now, we can see that many companies have only one purchasing department, purchasing department following pipe warehouse. This is far short of inventory control requirements. From the demand and supply chain management process analysis, we know that purchasing and warehouse management is the executive arm of the typical, and inventory control should focus on prevention, the executive branch is very difficult to "prevent inventory" for the simple reason that they assessment indicatorsin large part to ensure supply (production, customer). How the actual situation, a reasonable demand and supply chain management processes, and thus set the corresponding rational organizational structure and is a question many of our enterprisesto exploreThe role of inventory controlInventory management is an important part of business management. In the production and operation activities, inventory management must ensure that both the production plant for raw materials, spare parts demand, but also directly affect the purchasing, sales of share, sales activities. To make an inventory of corporate liquidity, accelerate cash flow, the security of supply under the premise of minimizing Yaku funds, directly affects the operational efficiency. Ensure the production and operation needs of the premise, so keep inventories at a reasonable level; dynamic inventory control, timely, appropriate proposed order to avoid over storage or out of stock; reduce inventory footprint, lower total cost of inventory; control stock funds used to accelerate cash flow.Problems arising from excessive inventory: increased warehouse space andinventory storage costs, thereby increasing product costs; take a lot of liquidity, resultingin sluggish capital, not only increased the burden of payment of interest, etc., would affect the time value of money and opportunity income; finished products and raw materials caused by physical loss and intangible losses; a large number of enterprise resource idle, affecting their rational allocation and optimization; cover the production, operation of the whole process of the various contradictions and problems, is not conducive to improve the management level.Inventory is too small the resulting problems: service levels caused a decline in the profit impact of marketing and corporate reputation; production system caused by inadequate supply of raw materials or other materials, affecting the normal production process; to shorten lead times, increase the number of orders, so order (production) costs; affect the balance of production and assembly of complete sets.NotesInventory management should particularly consider the following two questions:First, according to sales plans, according to the planned production of the goods circulated in the market, we should consider where, how much storage.Second, starting from the level of service and economic benefits to determine howto ensure inventories and supplementary questions.The two problems with the inventory in the logistics process functions.In general, the inventory function:(1)to prevent interrupted. Received orders to shorten the delivery of goods fromthe time in order to ensure quality service, at the same time to prevent out of stock.(2)to ensure proper inventory levels, saving inventory costs.(3)to reduce logistics costs. Supplement with the appropriate time interval compatible with the reasonable demand of the cargo in order to reduce logistics costs, eliminate or avoid sales fluctuations.(4)ensure the production planning, smooth to eliminate or avoid sales fluctuations.(5)display function.(6)reserve. Mass storage when the price falls, reduce losses, to respond to disasters and other contingencies.About the warehouse (inventory) on what the question, we must consider the number and location. If the distribution center, it should be possible according to customer needs, set at an appropriate place; if it is stored in central places to minimize the complementary principle to the distribution centers, there is no place certain requirements. When the stock base is established, will have to take into account are stored in various locations in what commodities.库存管理库存控制在谈到所谓“库存控制”的时候，很多人将其理解为“仓储管理”，这实际上是个很大的曲解。

外文文献及翻译

（（英文参考文献及译文）二〇一六年六月本科毕业论文题目：STATISTICAL SAMPLING METHOD, USED INTHE AUDIT学生姓名：王雪琴学院：管理学院系别：会计系专业：财务管理班级：财管12-2班学校代码： 10128 学号： 201210707016Statistics and AuditRomanian Statistical Review nr. 5 / 2010STATISTICAL SAMPLING METHOD, USED IN THE AUDIT - views, recommendations, fi ndingsPhD Candidate Gabriela-Felicia UNGUREANUAbstractThe rapid increase in the size of U.S. companies from the earlytwentieth century created the need for audit procedures based on the selectionof a part of the total population audited to obtain reliable audit evidence, tocharacterize the entire population consists of account balances or classes oftransactions. Sampling is not used only in audit – is used in sampling surveys,market analysis and medical research in which someone wants to reach aconclusion about a large number of data by examining only a part of thesedata. The difference is the “population” from which the sample is selected, iethat set of data which is intended to draw a conclusion. Audit sampling appliesonly to certain types of audit procedures.Key words: sampling, sample risk, population, sampling unit, tests ofcontrols, substantive procedures.Statistical samplingCommittee statistical sampling of American Institute of CertifiedPublic Accountants of (AICPA) issued in 1962 a special report, titled“Statistical sampling and independent auditors’ which allowed the use ofstatistical sampling method, in accordance with Generally Accepted AuditingStandards (GAAS). During 1962-1974, the AICPA published a series of paperson statistical sampling, “Auditor’s Approach to Statistical Sampling”, foruse in continuing professional education of accountants. During 1962-1974,the AICPA published a series of papers on statistical sampling, “Auditor’sApproach to Statistical Sampling”, for use in continuing professional educationof accountants. In 1981, AICPA issued the professional standard, “AuditSampling”, which provides general guidelines for both sampling methods,statistical and non-statistical.Earlier audits included checks of all transactions in the period coveredby the audited financial statements. At that time, the literature has not givenparticular attention to this subject. Only in 1971, an audit procedures programprinted in the “Federal Reserve Bulletin (Federal Bulletin Stocks)” includedseveral references to sampling such as selecting the “few items” of inventory.Statistics and Audit The program was developed by a special committee, which later became the AICPA, that of Certified Public Accountants American Institute.In the first decades of last century, the auditors often applied sampling, but sample size was not in related to the efficiency of internal control of the entity. In 1955, American Institute of Accountants has published a study case of extending the audit sampling, summarizing audit program developed by certified public accountants, to show why sampling is necessary to extend the audit. The study was important because is one of the leading journal on sampling which recognize a relationship of dependency between detail and reliability testing of internal control.In 1964, the AICPA’s Auditing Standards Board has issued a report entitled “The relationship between statistical sampling and Generally Accepted Auditing Standards (GAAS)” which illustrated the relationship between the accuracy and reliability in sampling and provisions of GAAS.In 1978, the AICPA published the work of Donald M. Roberts,“Statistical Auditing”which explains the underlying theory of statistical sampling in auditing.In 1981, AICPA issued the professional standard, named “Audit Sampling”, which provides guidelines for both sampling methods, statistical and non-statistical.An auditor does not rely solely on the results of a single procedure to reach a conclusion on an account balance, class of transactions or operational effectiveness of the controls. Rather, the audit findings are based on combined evidence from several sources, as a consequence of a number of different audit procedures. When an auditor selects a sample of a population, his objective is to obtain a representative sample, ie sample whose characteristics are identical with the population’s characteristics. This means that selected items are identical with those remaining outside the sample.In practice, auditors do not know for sure if a sample is representative, even after completion the test, but they “may increase the probability that a sample is representative by accuracy of activities made related to design, sample selection and evaluation” [1]. Lack of specificity of the sample results may be given by observation errors and sampling errors. Risks to produce these errors can be controlled.Observation error (risk of observation) appears when the audit test did not identify existing deviations in the sample or using an inadequate audit technique or by negligence of the auditor.Sampling error (sampling risk) is an inherent characteristic of the survey, which results from the fact that they tested only a fraction of the total population. Sampling error occurs due to the fact that it is possible for Revista Română de Statistică nr. 5 / 2010Statistics and Auditthe auditor to reach a conclusion, based on a sample that is different from the conclusion which would be reached if the entire population would have been subject to audit procedures identical. Sampling risk can be reduced by adjusting the sample size, depending on the size and population characteristics and using an appropriate method of selection. Increasing sample size will reduce the risk of sampling; a sample of the all population will present a null risk of sampling.Audit Sampling is a method of testing for gather sufficient and appropriate audit evidence, for the purposes of audit. The auditor may decide to apply audit sampling on an account balance or class of transactions. Sampling audit includes audit procedures to less than 100% of the items within an account balance or class of transactions, so all the sample able to be selected. Auditor is required to determine appropriate ways of selecting items for testing. Audit sampling can be used as a statistical approach and a non- statistical.Statistical sampling is a method by which the sample is made so that each unit consists of the total population has an equal probability of being included in the sample, method of sample selection is random, allowed to assess the results based on probability theory and risk quantification of sampling. Choosing the appropriate population make that auditor’ findings can be extended to the entire population.Non-statistical sampling is a method of sampling, when the auditor uses professional judgment to select elements of a sample. Since the purpose of sampling is to draw conclusions about the entire population, the auditor should select a representative sample by choosing sample units which have characteristics typical of that population. Results will not extrapolate the entire population as the sample selected is representative.Audit tests can be applied on the all elements of the population, where is a small population or on an unrepresentative sample, where the auditor knows the particularities of the population to be tested and is able to identify a small number of items of interest to audit. If the sample has not similar characteristics for the elements of the entire population, the errors found in the tested sample can not extrapolate.Decision of statistical or non-statistical approach depends on the auditor’s professional judgment which seeking sufficient appropriate audits evidence on which to completion its findings about the audit opinion.As a statistical sampling method refer to the random selection that any possible combination of elements of the community is equally likely to enter the sample. Simple random sampling is used when stratification was not to audit. Using random selection involves using random numbers generated byRomanian Statistical Review nr. 5 / 2010Statistics and Audit a computer. After selecting a random starting point, the auditor found the first random number that falls within the test document numbers. Only when the approach has the characteristics of statistical sampling, statistical assessments of risk are valid sampling.In another variant of the sampling probability, namely the systematic selection (also called random mechanical) elements naturally succeed in office space or time; the auditor has a preliminary listing of the population and made the decision on sample size. “The auditor calculated a counting step, and selects the sample element method based on step size. Step counting is determined by dividing the volume of the community to sample the number of units desired. Advantages of systematic screening are its usability. In most cases, a systematic sample can be extracted quickly and method automatically arranges numbers in successive series.”[2].Selection by probability proportional to size - is a method which emphasizes those population units’recorded higher values. The sample is constituted so that the probability of selecting any given element of the population is equal to the recorded value of the item;Stratifi ed selection - is a method of emphasis of units with higher values and is registered in the stratification of the population in subpopulations. Stratification provides a complete picture of the auditor, when population (data table to be analyzed) is not homogeneous. In this case, the auditor stratifies a population by dividing them into distinct subpopulations, which have common characteristics, pre-defined. “The objective of stratification is to reduce the variability of elements in each layer and therefore allow a reduction in sample size without a proportionate increase in the risk of sampling.” [3] If population stratification is done properly, the amount of sample size to come layers will be less than the sample size that would be obtained at the same level of risk given sample with a sample extracted from the entire population. Audit results applied to a layer can be designed only on items that are part of that layer.I appreciated as useful some views on non-statistical sampling methods, which implies that guided the selection of the sample selecting each element according to certain criteria determined by the auditor. The method is subjective; because the auditor selects intentionally items containing set features him.The selection of the series is done by selecting multiple elements series (successive). Using sampling the series is recommended only if a reasonable number of sets used. Using just a few series there is a risk that the sample is not representative. This type of sampling can be used in addition to other samples, where there is a high probability of occurrence of errors. At the arbitrary selection, no items are selected preferably from the auditor, Revista Română de Statistică nr. 5 / 2010Statistics and Auditthat regardless of size or source or characteristics. Is not the recommended method, because is not objective.That sampling is based on the auditor’s professional judgment, which may decide which items can be part or not sampled. Because is not a statistical method, it can not calculate the standard error. Although the sample structure can be constructed to reproduce the population, there is no guarantee that the sample is representative. If omitted a feature that would be relevant in a particular situation, the sample is not representative.Sampling applies when the auditor plans to make conclusions about population, based on a selection. The auditor considers the audit program and determines audit procedures which may apply random research. Sampling is used by auditors an internal control systems testing, and substantive testing of operations. The general objectives of tests of control system and operations substantive tests are to verify the application of pre-defined control procedures, and to determine whether operations contain material errors.Control tests are intended to provide evidence of operational efficiency and controls design or operation of a control system to prevent or detect material misstatements in financial statements. Control tests are necessary if the auditor plans to assess control risk for assertions of management.Controls are generally expected to be similarly applied to all transactions covered by the records, regardless of transaction value. Therefore, if the auditor uses sampling, it is not advisable to select only high value transactions. Samples must be chosen so as to be representative population sample.An auditor must be aware that an entity may change a special control during the course of the audit. If the control is replaced by another, which is designed to achieve the same specific objective, the auditor must decide whether to design a sample of all transactions made during or just a sample of transactions controlled again. Appropriate decision depends on the overall objective of the audit test.Verification of internal control system of an entity is intended to provide guidance on the identification of relevant controls and design evaluation tests of controls.Other tests:In testing internal control system and testing operations, audit sample is used to estimate the proportion of elements of a population containing a characteristic or attribute analysis. This proportion is called the frequency of occurrence or percentage of deviation and is equal to the ratio of elements containing attribute specific and total number of population elements. WeightRomanian Statistical Review nr. 5 / 2010Statistics and Audit deviations in a sample are determined to calculate an estimate of the proportion of the total population deviations.Risk associated with sampling - refers to a sample selection which can not be representative of the population tested. In other words, the sample itself may contain material errors or deviations from the line. However, issuing a conclusion based on a sample may be different from the conclusion which would be reached if the entire population would be subject to audit.Types of risk associated with sampling:Controls are more effective than they actually are or that there are not significant errors when they exist - which means an inappropriate audit opinion. Controls are less effective than they actually are that there are significant errors when in fact they are not - this calls for additional activities to establish that initial conclusions were incorrect.Attributes testing - the auditor should be defining the characteristics to test and conditions for misconduct. Attributes testing will make when required objective statistical projections on various characteristics of the population. The auditor may decide to select items from a population based on its knowledge about the entity and its environment control based on risk analysis and the specific characteristics of the population to be tested.Population is the mass of data on which the auditor wishes to generalize the findings obtained on a sample. Population will be defined compliance audit objectives and will be complete and consistent, because results of the sample can be designed only for the population from which the sample was selected.Sampling unit - a unit of sampling may be, for example, an invoice, an entry or a line item. Each sample unit is an element of the population. The auditor will define the sampling unit based on its compliance with the objectives of audit tests.Sample size - to determine the sample size should be considered whether sampling risk is reduced to an acceptable minimum level. Sample size is affected by the risk associated with sampling that the auditor is willing to accept it. The risk that the auditor is willing to accept lower, the sample will be higher.Error - for detailed testing, the auditor should project monetary errors found in the sample population and should take into account the projected error on the specific objective of the audit and other audit areas. The auditor projects the total error on the population to get a broad perspective on the size of the error and comparing it with tolerable error.For detailed testing, tolerable error is tolerable and misrepresentations Revista Română de Statistică nr. 5 / 2010Statistics and Auditwill be a value less than or equal to materiality used by the auditor for the individual classes of transactions or balances audited. If a class of transactions or account balances has been divided into layers error is designed separately for each layer. Design errors and inconsistent errors for each stratum are then combined when considering the possible effect on the total classes of transactions and account balances.Evaluation of sample results - the auditor should evaluate the sample results to determine whether assessing relevant characteristics of the population is confirmed or needs to be revised.When testing controls, an unexpectedly high rate of sample error may lead to an increase in the risk assessment of significant misrepresentation unless it obtained additional audit evidence to support the initial assessment. For control tests, an error is a deviation from the performance of control procedures prescribed. The auditor should obtain evidence about the nature and extent of any significant changes in internal control system, including the staff establishment.If significant changes occur, the auditor should review the understanding of internal control environment and consider testing the controls changed. Alternatively, the auditor may consider performing substantive analytical procedures or tests of details covering the audit period.In some cases, the auditor might not need to wait until the end audit to form a conclusion about the effectiveness of operational control, to support the control risk assessment. In this case, the auditor might decide to modify the planned substantive tests accordingly.If testing details, an unexpectedly large amount of error in a sample may cause the auditor to believe that a class of transactions or account balances is given significantly wrong in the absence of additional audit evidence to show that there are not material misrepresentations.When the best estimate of error is very close to the tolerable error, the auditor recognizes the risk that another sample have different best estimate that could exceed the tolerable error.ConclusionsFollowing analysis of sampling methods conclude that all methods have advantages and disadvantages. But the auditor is important in choosing the sampling method is based on professional judgment and take into account the cost / benefit ratio. Thus, if a sampling method proves to be costly auditor should seek the most efficient method in view of the main and specific objectives of the audit.Romanian Statistical Review nr. 5 / 2010Statistics and Audit The auditor should evaluate the sample results to determine whether the preliminary assessment of relevant characteristics of the population must be confirmed or revised. If the evaluation sample results indicate that the relevant characteristics of the population needs assessment review, the auditor may: require management to investigate identified errors and likelihood of future errors and make necessary adjustments to change the nature, timing and extent of further procedures to take into account the effect on the audit report.Selective bibliography:[1] Law no. 672/2002 updated, on public internal audit[2] Arens, A şi Loebbecke J - Controve …Audit– An integrate approach”, 8th edition, Arc Publishing House[3] ISA 530 - Financial Audit 2008 - International Standards on Auditing, IRECSON Publishing House, 2009- Dictionary of macroeconomics, Ed C.H. Beck, Bucharest, 2008Revista Română de Statistică nr. 5 / 2010Statistics and Audit摘要美国公司的规模迅速增加，从第二十世纪初创造了必要的审计程序，根据选定的部分总人口的审计，以获得可靠的审计证据，以描述整个人口组成的帐户余额或类别的交易。

外文文献翻译(图片版)

本科毕业论文外文参考文献译文及原文学院经济与贸易学院专业经济学（贸易方向）年级班别2007级 1 班学号3207004154学生姓名欧阳倩指导教师童雪晖2010 年 6 月 3 日目录1 外文文献译文（一）中国银行业的改革和盈利能力（第1、2、4部分） (1)2 外文文献原文（一）CHINA’S BANKING REFORM AND PROFITABILITY（Part 1、2、4） (9)1概述世界银行（1997年）曾声称，中国的金融业是其经济的软肋。

当一国的经济增长的可持续性岌岌可危的时候，金融业的改革一直被认为是提高资金使用效率和消费型经济增长重新走向平衡的必要（Lardy，1998年，Prasad，2007年）。

事实上，不久前，中国的国有银行被视为“技术上破产”，它们的生存需要依靠充裕的国家流动资金。

但是，在银行改革开展以来，最近，强劲的盈利能力已恢复到国有商业银行的水平。

但自从中国的国有银行在不久之前已经走上了改革的道路，它可能过早宣布银行业的改革尚未取得完全的胜利。

此外，其坚实的财务表现虽然强劲，但不可持续增长。

随着经济增长在2008年全球经济衰退得带动下已经开始软化，银行预计将在一个比以前更加困难的经济形势下探索。

本文的目的不是要评价银行业改革对银行业绩的影响，这在一个完整的信贷周期后更好解决。

相反，我们的目标是通过审查改革的进展和银行改革战略，并分析其近期改革后的强劲的财务表现，但是这不能完全从迄今所进行的改革努力分离。

本文有三个部分。

在第二节中，我们回顾了中国的大型国有银行改革的战略，以及其执行情况，这是中国银行业改革的主要目标。

第三节中分析了2007年的财务表现集中在那些在市场上拥有浮动股份的四大国有商业银行：中国工商银行（工商银行），中国建设银行（建行），对中国银行（中银）和交通银行（交通银行）。

引人注目的是中国农业银行，它仍然处于重组上市过程中得适当时候的后期。

第四节总结一个对银行绩效评估。

机械手设计英文参考文献原文翻译

翻译人：王墨墨山东科技大学文献题目：Automated Calibration of Robot Coordinatesfor Reconfigurable Assembly Systems翻译正文如下：针对可重构装配系统的机器人协调性的自动校准T.艾利，Y.米达，H.菊地，M.雪松日本东京大学，机械研究院，精密工程部摘要为了实现流水工作线更高的可重构性，以必要设备如机器人的快速插入插出为研究目的。

当一种新的设备被装配到流水工作线时，应使其具备校准系统。

该研究使用两台电荷耦合摄像机，基于直接线性变换法，致力于研究一种相对位置/相对方位的自动化校准系统。

摄像机被随机放置，然后对每一个机械手执行一组动作。

通过摄像机检测机械手动作，就能捕捉到两台机器人的相对位置。

最佳的结果精度为均方根值0.16毫米。

关键词：装配，校准，机器人1 介绍21世纪新的制造系统需要具备新的生产能力，如可重用性，可拓展性，敏捷性以及可重构性[1]。

系统配置的低成本转变，能够使系统应对可预见的以及不可预见的市场波动。

关于组装系统，许多研究者提出了分散的方法来实现可重构性[2][3]。

他们中的大多数都是基于主体的系统，主体逐一协同以建立一种新的配置。

然而，协同只是目的的一部分。

在现实生产系统中，例如工作空间这类物理问题应当被有效解决。

为了实现更高的可重构性，一些研究人员不顾昂贵的造价，开发出了特殊的均匀单元[4][5][6]。

作者为装配单元提出了一种自律分散型机器人系统，包含多样化的传统设备[7][8]。

该系统可以从一个系统添加/删除装配设备，亦或是添加/删除装配设备到另一个系统；它通过协同作用，合理地解决了工作空间的冲突问题。

我们可以把该功能称为“插入与生产”。

在重构过程中，校准的装配机器人是非常重要的。

这是因为，需要用它们来测量相关主体的特征，以便在物理主体之间建立良好的协作关系。

这一调整必须要达到表1中所列到的多种标准要求。

云计算外文翻译参考文献

云计算外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)原文:Technical Issues of Forensic Investigations in Cloud Computing EnvironmentsDominik BirkRuhr-University BochumHorst Goertz Institute for IT SecurityBochum, GermanyRuhr-University BochumHorst Goertz Institute for IT SecurityBochum, GermanyAbstract—Cloud Computing is arguably one of the most discussedinformation technologies today. It presents many promising technological and economical opportunities. However, many customers remain reluctant to move their business IT infrastructure completely to the cloud. One of their main concerns is Cloud Security and the threat of the unknown. Cloud Service Providers(CSP) encourage this perception by not letting their customers see what is behind their virtual curtain. A seldomly discussed, but in this regard highly relevant open issue is the ability to perform digital investigations. This continues to fuel insecurity on the sides of both providers and customers. Cloud Forensics constitutes a new and disruptive challenge for investigators. Due to the decentralized nature of data processing in the cloud, traditional approaches to evidence collection and recovery are no longer practical. This paper focuses on the technical aspects of digital forensics in distributed cloud environments. We contribute by assessing whether it is possible for the customer of cloud computing services to perform a traditional digital investigation from a technical point of view. Furthermore we discuss possible solutions and possible new methodologies helping customers to perform such investigations.I. INTRODUCTIONAlthough the cloud might appear attractive to small as well as to large companies, it does not come along without its own unique problems. Outsourcing sensitive corporate data into the cloud raises concerns regarding the privacy and security of data. Security policies, companies main pillar concerning security, cannot be easily deployed into distributed, virtualized cloud environments. This situation is further complicated by the unknown physical location of the companie’s assets. Normally,if a security incident occurs, the corporate security team wants to be able to perform their own investigation without dependency on third parties. In the cloud, this is not possible anymore: The CSP obtains all the power over the environmentand thus controls the sources of evidence. In the best case, a trusted third party acts as a trustee and guarantees for the trustworthiness of the CSP. Furthermore, the implementation of the technical architecture and circumstances within cloud computing environments bias the way an investigation may be processed. In detail, evidence data has to be interpreted by an investigator in a We would like to thank the reviewers for the helpful comments and Dennis Heinson (Center for Advanced Security Research Darmstadt - CASED) for the profound discussions regarding the legal aspects of cloud forensics. proper manner which is hardly be possible due to the lackof circumstantial information. For auditors, this situation does not change: Questions who accessed specific data and information cannot be answered by the customers, if no corresponding logs are available. With the increasing demand for using the power of the cloud for processing also sensible information and data, enterprises face the issue of Data and Process Provenance in the cloud [10]. Digital provenance, meaning meta-data that describes the ancestry or history of a digital object, is a crucial feature for forensic investigations. In combination with a suitable authentication scheme, it provides information about who created and who modified what kind of data in the cloud. These are crucial aspects for digital investigations in distributed environments such as the cloud. Unfortunately, the aspects of forensic investigations in distributed environment have so far been mostly neglected by the research community. Current discussion centers mostly around security, privacy and data protection issues [35], [9], [12]. The impact of forensic investigations on cloud environments was little noticed albeit mentioned by the authors of [1] in 2009: ”[...] to our knowledge, no research has been published on how cloud computing environments affect digital artifacts,and on acquisition logistics and legal issues related to cloud computing env ironments.” This statement is also confirmed by other authors [34], [36], [40] stressing that further research on incident handling, evidence tracking and accountability in cloud environments has to be done. At the same time, massive investments are being made in cloud technology. Combined with the fact that information technology increasingly transcendents peoples’ private and professional life, thus mirroring more and more of peoples’actions, it becomes apparent that evidence gathered from cloud environments will be of high significance to litigation or criminal proceedings in the future. Within this work, we focus the notion of cloud forensics by addressing the technical issues of forensics in all three major cloud service models and consider cross-disciplinary aspects. Moreover, we address the usability of various sources of evidence for investigative purposes and propose potential solutions to the issues from a practical standpoint. This work should be considered as a surveying discussion of an almost unexplored research area. The paper is organized as follows: We discuss the related work and the fundamental technical background information of digital forensics, cloud computing and the fault model in section II and III. In section IV, we focus on the technical issues of cloud forensics and discuss the potential sources and nature of digital evidence as well as investigations in XaaS environments including thecross-disciplinary aspects. We conclude in section V.II. RELATED WORKVarious works have been published in the field of cloud security and privacy [9], [35], [30] focussing on aspects for protecting data in multi-tenant, virtualized environments. Desired security characteristics for current cloud infrastructures mainly revolve around isolation of multi-tenant platforms [12], security of hypervisors in order to protect virtualized guest systems and secure network infrastructures [32]. Albeit digital provenance, describing the ancestry of digital objects, still remains a challenging issue for cloud environments, several works have already been published in this field [8], [10] contributing to the issues of cloud forensis. Within this context, cryptographic proofs for verifying data integrity mainly in cloud storage offers have been proposed,yet lacking of practical implementations [24], [37], [23]. Traditional computer forensics has already well researched methods for various fields of application [4], [5], [6], [11], [13]. Also the aspects of forensics in virtual systems have been addressed by several works [2], [3], [20] including the notionof virtual introspection [25]. In addition, the NIST already addressed Web Service Forensics [22] which has a huge impact on investigation processes in cloud computing environments. In contrast, the aspects of forensic investigations in cloud environments have mostly been neglected by both the industry and the research community. One of the first papers focusing on this topic was published by Wolthusen [40] after Bebee et al already introduced problems within cloud environments [1]. Wolthusen stressed that there is an inherent strong need for interdisciplinary work linking the requirements and concepts of evidence arising from the legal field to what can be feasibly reconstructed and inferred algorithmically or in an exploratory manner. In 2010, Grobauer et al [36] published a paper discussing the issues of incident response in cloud environments - unfortunately no specific issues and solutions of cloud forensics have been proposed which will be done within this work.III. TECHNICAL BACKGROUNDA. Traditional Digital ForensicsThe notion of Digital Forensics is widely known as the practice of identifying, extracting and considering evidence from digital media. Unfortunately, digital evidence is both fragile and volatile and therefore requires the attention of special personnel and methods in order to ensure that evidence data can be proper isolated and evaluated. Normally, the process of a digital investigation can be separated into three different steps each having its own specificpurpose:1) In the Securing Phase, the major intention is the preservation of evidence for analysis. The data has to be collected in a manner that maximizes its integrity. This is normally done by a bitwise copy of the original media. As can be imagined, this represents a huge problem in the field of cloud computing where you never know exactly where your data is and additionallydo not have access to any physical hardware. However, the snapshot technology, discussed in section IV-B3, provides a powerful tool to freeze system states and thus makes digital investigations, at least in IaaS scenarios, theoretically possible.2) We refer to the Analyzing Phase as the stage in which the data is sifted and combined. It is in this phase that the data from multiple systems or sources is pulled together to create as complete a picture and event reconstruction as possible. Especially in distributed system infrastructures, this means that bits and pieces of data are pulled together for deciphering the real story of what happened and for providing a deeper look into the data.3) Finally, at the end of the examination and analysis of the data, the results of the previous phases will be reprocessed in the Presentation Phase. The report, created in this phase, is a compilation of all the documentation and evidence from the analysis stage. The main intention of such a report is that it contains all results, it is complete and clear to understand. Apparently, the success of these three steps strongly depends on the first stage. If it is not possible to secure the complete set of evidence data, no exhaustive analysis will be possible. However, in real world scenarios often only a subset of the evidence data can be secured by the investigator. In addition, an important definition in the general context of forensics is the notion of a Chain of Custody. This chain clarifies how and where evidence is stored and who takes possession of it. Especially for cases which are brought to court it is crucial that the chain of custody is preserved.B. Cloud ComputingAccording to the NIST [16], cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal CSP interaction. The new raw definition of cloud computing brought several new characteristics such as multi-tenancy, elasticity, pay-as-you-go and reliability. Within this work, the following three models are used: In the Infrastructure asa Service (IaaS) model, the customer is using the virtual machine provided by the CSP for installing his own system on it. The system can be used like any other physical computer with a few limitations. However, the additive customer power over the system comes along with additional security obligations. Platform as a Service (PaaS) offerings provide the capability to deploy application packages created using the virtual development environment supported by the CSP. For the efficiency of software development process this service model can be propellent. In the Software as a Service (SaaS) model, the customer makes use of a service run by the CSP on a cloud infrastructure. In most of the cases this service can be accessed through an API for a thin client interface such as a web browser. Closed-source public SaaS offers such as Amazon S3 and GoogleMail can only be used in the public deployment model leading to further issues concerning security, privacy and the gathering of suitable evidences. Furthermore, two main deployment models, private and public cloud have to be distinguished. Common public clouds are made available to the general public. The corresponding infrastructure is owned by one organization acting as a CSP and offering services to its customers. In contrast, the private cloud is exclusively operated for an organization but may not provide the scalability and agility of public offers. The additional notions of community and hybrid cloud are not exclusively covered within this work. However, independently from the specific model used, the movement of applications and data to the cloud comes along with limited control for the customer about the application itself, the data pushed into the applications and also about the underlying technical infrastructure.C. Fault ModelBe it an account for a SaaS application, a development environment (PaaS) or a virtual image of an IaaS environment, systems in the cloud can be affected by inconsistencies. Hence, for both customer and CSP it is crucial to have the ability to assign faults to the causing party, even in the presence of Byzantine behavior [33]. Generally, inconsistencies can be caused by the following two reasons:1) Maliciously Intended FaultsInternal or external adversaries with specific malicious intentions can cause faults on cloud instances or applications. Economic rivals as well as former employees can be the reason for these faults and state a constant threat to customers and CSP. In this model, also a malicious CSP is included albeit he isassumed to be rare in real world scenarios. Additionally, from the technical point of view, the movement of computing power to a virtualized, multi-tenant environment can pose further threads and risks to the systems. One reason for this is that if a single system or service in the cloud is compromised, all other guest systems and even the host system are at risk. Hence, besides the need for further security measures, precautions for potential forensic investigations have to be taken into consideration.2) Unintentional FaultsInconsistencies in technical systems or processes in the cloud do not have implicitly to be caused by malicious intent. Internal communication errors or human failures can lead to issues in the services offered to the costumer(i.e. loss or modification of data). Although these failures are not caused intentionally, both the CSP and the customer have a strong intention to discover the reasons and deploy corresponding fixes.IV. TECHNICAL ISSUESDigital investigations are about control of forensic evidence data. From the technical standpoint, this data can be available in three different states: at rest, in motion or in execution. Data at rest is represented by allocated disk space. Whether the data is stored in a database or in a specific file format, it allocates disk space. Furthermore, if a file is deleted, the disk space is de-allocated for the operating system but the data is still accessible since the disk space has not been re-allocated and overwritten. This fact is often exploited by investigators which explore these de-allocated disk space on harddisks. In case the data is in motion, data is transferred from one entity to another e.g. a typical file transfer over a network can be seen as a data in motion scenario. Several encapsulated protocols contain the data each leaving specific traces on systems and network devices which can in return be used by investigators. Data can be loaded into memory and executed as a process. In this case, the data is neither at rest or in motion but in execution. On the executing system, process information, machine instruction and allocated/de-allocated data can be analyzed by creating a snapshot of the current system state. In the following sections, we point out the potential sources for evidential data in cloud environments and discuss the technical issues of digital investigations in XaaS environmentsas well as suggest several solutions to these problems.A. Sources and Nature of EvidenceConcerning the technical aspects of forensic investigations, the amount of potential evidence available to the investigator strongly diverges between thedifferent cloud service and deployment models. The virtual machine (VM), hosting in most of the cases the server application, provides several pieces of information that could be used by investigators. On the network level, network components can provide information about possible communication channels between different parties involved. The browser on the client, acting often as the user agent for communicating with the cloud, also contains a lot of information that could be used as evidence in a forensic investigation. Independently from the used model, the following three components could act as sources for potential evidential data.1) Virtual Cloud Instance: The VM within the cloud, where i.e. data is stored or processes are handled, contains potential evidence [2], [3]. In most of the cases, it is the place where an incident happened and hence provides a good starting point for a forensic investigation. The VM instance can be accessed by both, the CSP and the customer who is running the instance. Furthermore, virtual introspection techniques [25] provide access to the runtime state of the VM via the hypervisor and snapshot technology supplies a powerful technique for the customer to freeze specific states of the VM. Therefore, virtual instances can be still running during analysis which leads to the case of live investigations [41] or can be turned off leading to static image analysis. In SaaS and PaaS scenarios, the ability to access the virtual instance for gathering evidential information is highly limited or simply not possible.2) Network Layer: Traditional network forensics is knownas the analysis of network traffic logs for tracing events that have occurred in the past. Since the different ISO/OSI network layers provide several information on protocols and communication between instances within as well as with instances outside the cloud [4], [5], [6], network forensics is theoretically also feasible in cloud environments. However in practice, ordinary CSP currently do not provide any log data from the network components used by the customer’s instances or applications. For instance, in case of a malware infection of an IaaS VM, it will be difficult for the investigator to get any form of routing information and network log datain general which is crucial for further investigative steps. This situation gets even more complicated in case of PaaS or SaaS. So again, the situation of gathering forensic evidence is strongly affected by the support the investigator receives from the customer and the CSP.3) Client System: On the system layer of the client, it completely depends on the used model (IaaS, PaaS, SaaS) if and where potential evidence could beextracted. In most of the scenarios, the user agent (e.g. the web browser) on the client system is the only application that communicates with the service in the cloud. This especially holds for SaaS applications which are used and controlled by the web browser. But also in IaaS scenarios, the administration interface is often controlled via the browser. Hence, in an exhaustive forensic investigation, the evidence data gathered from the browser environment [7] should not be omitted.a) Browser Forensics: Generally, the circumstances leading to an investigation have to be differentiated: In ordinary scenarios, the main goal of an investigation of the web browser is to determine if a user has been victim of a crime. In complex SaaS scenarios with high client-server interaction, this constitutes a difficult task. Additionally, customers strongly make use of third-party extensions [17] which can be abused for malicious purposes. Hence, the investigator might want to look for malicious extensions, searches performed, websites visited, files downloaded, information entered in forms or stored in local HTML5 stores, web-based email contents and persistent browser cookies for gathering potential evidence data. Within this context, it is inevitable to investigate the appearance of malicious JavaScript [18] leading to e.g. unintended AJAX requests and hence modified usage of administration interfaces. Generally, the web browser contains a lot of electronic evidence data that could be used to give an answer to both of the above questions - even if the private mode is switched on [19].B. Investigations in XaaS EnvironmentsTraditional digital forensic methodologies permit investigators to seize equipment and perform detailed analysis on the media and data recovered [11]. In a distributed infrastructure organization like the cloud computing environment, investigators are confronted with an entirely different situation. They have no longer the option of seizing physical data storage. Data and processes of the customer are dispensed over an undisclosed amount of virtual instances, applications and network elements. Hence, it is in question whether preliminary findings of the computer forensic community in the field of digital forensics apparently have to be revised and adapted to the new environment. Within this section, specific issues of investigations in SaaS, PaaS and IaaS environments will be discussed. In addition, cross-disciplinary issues which affect several environments uniformly, will be taken into consideration. We also suggest potential solutions to the mentioned problems.1) SaaS Environments: Especially in the SaaS model, the customer does notobtain any control of the underlying operating infrastructure such as network, servers, operating systems or the application that is used. This means that no deeper view into the system and its underlying infrastructure is provided to the customer. Only limited userspecific application configuration settings can be controlled contributing to the evidences which can be extracted fromthe client (see section IV-A3). In a lot of cases this urges the investigator to rely on high-level logs which are eventually provided by the CSP. Given the case that the CSP does not run any logging application, the customer has no opportunity to create any useful evidence through the installation of any toolkit or logging tool. These circumstances do not allow a valid forensic investigation and lead to the assumption that customers of SaaS offers do not have any chance to analyze potential incidences.a) Data Provenance: The notion of Digital Provenance is known as meta-data that describes the ancestry or history of digital objects. Secure provenance that records ownership and process history of data objects is vital to the success of data forensics in cloud environments, yet it is still a challenging issue today [8]. Albeit data provenance is of high significance also for IaaS and PaaS, it states a huge problem specifically for SaaS-based applications: Current global acting public SaaS CSP offer Single Sign-On (SSO) access control to the set of their services. Unfortunately in case of an account compromise, most of the CSP do not offer any possibility for the customer to figure out which data and information has been accessed by the adversary. For the victim, this situation can have tremendous impact: If sensitive data has been compromised, it is unclear which data has been leaked and which has not been accessed by the adversary. Additionally, data could be modified or deleted by an external adversary or even by the CSP e.g. due to storage reasons. The customer has no ability to proof otherwise. Secure provenance mechanisms for distributed environments can improve this situation but have not been practically implemented by CSP [10]. Suggested Solution: In private SaaS scenarios this situation is improved by the fact that the customer and the CSP are probably under the same authority. Hence, logging and provenance mechanisms could be implemented which contribute to potential investigations. Additionally, the exact location of the servers and the data is known at any time. Public SaaS CSP should offer additional interfaces for the purpose of compliance, forensics, operations and security matters to their customers. Through an API, the customers should have the ability to receive specific information suchas access, error and event logs that could improve their situation in case of aninvestigation. Furthermore, due to the limited ability of receiving forensic information from the server and proofing integrity of stored data in SaaS scenarios, the client has to contribute to this process. This could be achieved by implementing Proofs of Retrievability (POR) in which a verifier (client) is enabled to determine that a prover (server) possesses a file or data object and it can be retrieved unmodified [24]. Provable Data Possession (PDP) techniques [37] could be used to verify that an untrusted server possesses the original data without the need for the client to retrieve it. Although these cryptographic proofs have not been implemented by any CSP, the authors of [23] introduced a new data integrity verification mechanism for SaaS scenarios which could also be used for forensic purposes.2) PaaS Environments: One of the main advantages of the PaaS model is that the developed software application is under the control of the customer and except for some CSP, the source code of the application does not have to leave the local development environment. Given these circumstances, the customer obtains theoretically the power to dictate how the application interacts with other dependencies such as databases, storage entities etc. CSP normally claim this transfer is encrypted but this statement can hardly be verified by the customer. Since the customer has the ability to interact with the platform over a prepared API, system states and specific application logs can be extracted. However potential adversaries, which can compromise the application during runtime, should not be able to alter these log files afterwards. Suggested Solution:Depending on the runtime environment, logging mechanisms could be implemented which automatically sign and encrypt the log information before its transfer to a central logging server under the control of the customer. Additional signing and encrypting could prevent potential eavesdroppers from being able to view and alter log data information on the way to the logging server. Runtime compromise of an PaaS application by adversaries could be monitored by push-only mechanisms for log data presupposing that the needed information to detect such an attack are logged. Increasingly, CSP offering PaaS solutions give developers the ability to collect and store a variety of diagnostics data in a highly configurable way with the help of runtime feature sets [38].3) IaaS Environments: As expected, even virtual instances in the cloud get compromised by adversaries. Hence, the ability to determine how defenses in the virtual environment failed and to what extent the affected systems havebeen compromised is crucial not only for recovering from an incident. Also forensic investigations gain leverage from such information and contribute to resilience against future attacks on the systems. From the forensic point of view, IaaS instances do provide much more evidence data usable for potential forensics than PaaS and SaaS models do. This fact is caused throughthe ability of the customer to install and set up the image for forensic purposes before an incident occurs. Hence, as proposed for PaaS environments, log data and other forensic evidence information could be signed and encrypted before itis transferred to third-party hosts mitigating the chance that a maliciously motivated shutdown process destroys the volatile data. Although, IaaS environments provide plenty of potential evidence, it has to be emphasized that the customer VM is in the end still under the control of the CSP. He controls the hypervisor which is e.g. responsible for enforcing hardware boundaries and routing hardware requests among different VM. Hence, besides the security responsibilities of the hypervisor, he exerts tremendous control over how customer’s VM communicate with the hardware and theoretically can intervene executed processes on the hosted virtual instance through virtual introspection [25]. This could also affect encryption or signing processes executed on the VM and therefore leading to the leakage of the secret key. Although this risk can be disregarded in most of the cases, the impact on the security of high security environments is tremendous.a) Snapshot Analysis: Traditional forensics expect target machines to be powered down to collect an image (dead virtual instance). This situation completely changed with the advent of the snapshot technology which is supported by all popular hypervisors such as Xen, VMware ESX and Hyper-V.A snapshot, also referred to as the forensic image of a VM, providesa powerful tool with which a virtual instance can be clonedby one click including also the running system’s mem ory. Due to the invention of the snapshot technology, systems hosting crucial business processes do not have to be powered down for forensic investigation purposes. The investigator simply creates and loads a snapshot of the target VM for analysis(live virtual instance). This behavior is especially important for scenarios in which a downtime of a system is not feasible or practical due to existing SLA. However the information whether the machine is running or has been properly powered down is crucial [3] for the investigation. Live investigations of running virtual instances become more common providing evidence data that。

外文参考文献(带中文翻译)

外文资料原文涂敏之会计学 8051208076Title:Future of SME finance（c）Background – the environment for SME finance has changedFuture economic recovery will depend on the possibility of Crafts, Trades and SMEs to exploit their potential for growth and employment creation.SMEs make a major contribution to growth and employment in the EU and are at the heart of the Lisbon Strategy, whose main objective is to turn Europe into the most competitive and dynamic knowledge-based economy in the world. However, the ability of SMEs to grow depends highly on their potential to invest in restructuring, innovation and qualification. All of these investments need capital and therefore access to finance.Against this background the consistently repeated complaint of SMEs about their problems regarding access to finance is a highly relevant constraint that endangers the economic recovery of Europe.Changes in the finance sector influence the behavior of credit institutes towards Crafts, Trades and SMEs. Recent and ongoing developments in the banking sector add to the concerns of SMEs and will further endanger their access to finance. The main changes in the banking sector which influence SME finance are:•Globalization and internationalization have increased the competition and the profit orientation in the sector;•worsening of the economic situations in some institutes (burst of the ITC bubble, insolvencies) strengthen the focus on profitability further;•Mergers and restructuring created larger structures and many local branches, which had direct and personalized contacts with small enterprises, were closed;•up-coming implementation of new capital adequacy rules (Basel II) will also change SME business of the credit sector and will increase its administrative costs;•Stricter interpretation of State-Aide Rules by the European Commission eliminates the support of banks by public guarantees; many of the effected banks are very active in SME finance.All these changes result in a higher sensitivity for risks and profits in the financesector.The changes in the finance sector affect the accessibility of SMEs to finance.Higher risk awareness in the credit sector, a stronger focus on profitability and the ongoing restructuring in the finance sector change the framework for SME finance and influence the accessibility of SMEs to finance. The most important changes are: •In order to make the higher risk awareness operational, the credit sector introduces new rating systems and instruments for credit scoring;•Risk assessment of SMEs by banks will force the enterprises to present more and better quality information on their businesses;•Banks will try to pass through their additional costs for implementing and running the new capital regulations (Basel II) to their business clients;•due to the increase of competition on interest rates, the bank sector demands more and higher fees for its services (administration of accounts, payments systems, etc.), which are not only additional costs for SMEs but also limit their liquidity;•Small enterprises will lose their personal relationship with decision-makers in local branches –the credit application process will become more formal and anonymous and will probably lose longer;•the credit sector will lose more and more i ts “public function” to provide access to finance for a wide range of economic actors, which it has in a number of countries, in order to support and facilitate economic growth; the profitability of lending becomes the main focus of private credit institutions.All of these developments will make access to finance for SMEs even more difficult and / or will increase the cost of external finance. Business start-ups and SMEs, which want to enter new markets, may especially suffer from shortages regarding finance. A European Code of Conduct between Banks and SMEs would have allowed at least more transparency in the relations between Banks and SMEs and UEAPME regrets that the bank sector was not able to agree on such a commitment.Towards an encompassing policy approach to improve the access of Crafts, Trades and SMEs to financeAll analyses show that credits and loans will stay the main source of finance for the SME sector in Europe. Access to finance was always a main concern for SMEs, but the recent developments in the finance sector worsen the situation even more.Shortage of finance is already a relevant factor, which hinders economic recovery in Europe. Many SMEs are not able to finance their needs for investment.Therefore, UEAPME expects the new European Commission and the new European Parliament to strengthen their efforts to improve the framework conditions for SME finance. Europe’s Crafts, Trades and SMEs ask for an encompassing policy approach, which includes not only the conditions for SMEs’ access to l ending, but will also strengthen their capacity for internal finance and their access to external risk capital.From UEAPME’s point of view such an encompassing approach should be based on three guiding principles:•Risk-sharing between private investors, financial institutes, SMEs and public sector;•Increase of transparency of SMEs towards their external investors and lenders;•improving the regulatory environment for SME finance.Based on these principles and against the background of the changing environment for SME finance, UEAPME proposes policy measures in the following areas:1. New Capital Requirement Directive: SME friendly implementation of Basel IIDue to intensive lobbying activities, UEAPME, together with other Business Associations in Europe, has achieved some improvements in favour of SMEs regarding the new Basel Agreement on regulatory capital (Basel II). The final agreement from the Basel Committee contains a much more realistic approach toward the real risk situation of SME lending for the finance market and will allow the necessary room for adaptations, which respect the different regional traditions and institutional structures.However, the new regulatory system will influence the relations between Banks and SMEs and it will depend very much on the way it will be implemented into European law, whether Basel II becomes burdensome for SMEs and if it will reduce access to finance for them.The new Capital Accord form the Basel Committee gives the financial market authorities and herewith the European Institutions, a lot of flexibility. In about 70 areas they have room to adapt the Accord to their specific needs when implementing itinto EU law. Some of them will have important effects on the costs and the accessibility of finance for SMEs.UEAPME expects therefore from the new European Commission and the new European Parliament:•The implementation of the new Capital Requirement Directive will be costly for the Finance Sector (up to 30 Billion Euro till 2006) and its clients will have to pay for it. Therefore, the implementation – especially for smaller banks, which are often very active in SME finance –has to be carried out with as little administrative burdensome as possible (reporting obligations, statistics, etc.).•The European Regulators must recognize traditional instruments for collaterals (guarantees, etc.) as far as possible.•The European Commission and later the Member States should take over the recommendations from the European Parliament with regard to granularity, access to retail portfolio, maturity, partial use, adaptation of thresholds, etc., which will ease the burden on SME finance.2. SMEs need transparent rating proceduresDue to higher risk awareness of the finance sector and the needs of Basel II, many SMEs will be confronted for the first time with internal rating procedures or credit scoring systems by their banks. The bank will require more and better quality information from their clients and will assess them in a new way. Both up-coming developments are already causing increasing uncertainty amongst SMEs.In order to reduce this uncertainty and to allow SMEs to understand the principles of the new risk assessment, UEAPME demands transparent rating procedures –rating procedures may not become a “Black Box” for SMEs: •The bank should communicate the relevant criteria affecting the rating of SMEs.•The bank should inform SMEs about its assessment in order to allow SMEs to improve.The negotiations on a European Code of Conduct between Banks and SMEs , which would have included a self-commitment for transparent rating procedures by Banks, failed. Therefore, UEAPME expects from the new European Commission and the new European Parliament support for:•binding rules in the framework of the new Capital Adequacy Directive,which ensure the transparency of rating procedures and credit scoring systems for SMEs;•Elaboration of national Codes of Conduct in order to improve the relations between Banks and SMEs and to support the adaptation of SMEs to the new financial environment.3. SMEs need an extension of credit guarantee systems with a special focus on Micro-LendingBusiness start-ups, the transfer of businesses and innovative fast growth SMEs also depended in the past very often on public support to get access to finance. Increasing risk awareness by banks and the stricter interpretation of State Aid Rules will further increase the need for public support.Already now, there are credit guarantee schemes in many countries on the limit of their capacity and too many investment projects cannot be realized by SMEs.Experiences show that Public money, spent for supporting credit guarantees systems, is a very efficient instrument and has a much higher multiplying effect than other instruments. One Euro form the European Investment Funds can stimulate 30 Euro investments in SMEs (for venture capital funds the relation is only 1:2).Therefore, UEAPME expects the new European Commission and the new European Parliament to support:•The extension of funds for national credit guarantees schemes in the framework of the new Multi-Annual Programmed for Enterprises;•The development of new instruments for securitizations of SME portfolios;•The recognition of existing and well functioning credit guarantees schemes as collateral;•More flexibility within the European Instruments, because of national differences in the situation of SME finance;•The development of credit guarantees schemes in the new Member States;•The development of an SBIC-like scheme in the Member States to close the equity gap (0.2 – 2.5 Mio Euro, according to the expert meeting on PACE on April 27 in Luxemburg).•the development of a financial support scheme to encourage the internalizations of SMEs (currently there is no scheme available at EU level: termination of JOP, fading out of JEV).4. SMEs need company and income taxation systems, whichstrengthen their capacity for self-financingMany EU Member States have company and income taxation systems with negative incentives to build-up capital within the company by re-investing their profits. This is especially true for companies, which have to pay income taxes. Already in the past tax-regimes was one of the reasons for the higher dependence of Europe’s SMEs on bank lending. In future, the result of rating w ill also depend on the amount of capital in the company; the high dependence on lending will influence the access to lending. This is a vicious cycle, which has to be broken.Even though company and income taxation falls under the competence of Member States, UEAPME asks the new European Commission and the new European Parliament to publicly support tax-reforms, which will strengthen the capacity of Crafts, Trades and SME for self-financing. Thereby, a special focus on non-corporate companies is needed.5. Risk Capital – equity financingExternal equity financing does not have a real tradition in the SME sector. On the one hand, small enterprises and family business in general have traditionally not been very open towards external equity financing and are not used to informing transparently about their business.On the other hand, many investors of venture capital and similar forms of equity finance are very reluctant regarding investing their funds in smaller companies, which is more costly than investing bigger amounts in larger companies. Furthermore it is much more difficult to set out of such investments in smaller companies.Even though equity financing will never become the main source of financing for SMEs, it is an important instrument for highly innovative start-ups and fast growing companies and it has therefore to be further developed. UEAPME sees three pillars for such an approach where policy support is needed:Availability of venture capital•The Member States should review their taxation systems in order to create incentives to invest private money in all forms of venture capital.•Guarantee instruments for equity financing should be further developed.Improve the conditions for investing venture capital into SMEs•The development of secondary markets for venture capital investments in SMEs should be supported.•Accounting Standards for SMEs should be revised in order to easetransparent exchange of information between investor and owner-manager.Owner-managers must become more aware about the need for transparency towards investors•SME owners will have to realise that in future access to external finance (venture capital or lending) will depend much more on a transparent and open exchange of information about the situation and the perspectives of their companies.•In order to fulfil the new needs for transparency, SMEs will have to use new information instruments (business plans, financial reporting, etc.) and new management instruments (risk-management, financial management, etc.).外文资料翻译涂敏之会计学 8051208076题目：未来的中小企业融资背景：中小企业融资已经改变未来的经济复苏将取决于能否工艺品，贸易和中小企业利用其潜在的增长和创造就业。

数据库外文参考文献及翻译

数据库外文参考文献及翻译数据库外文参考文献及翻译SQL ALL-IN-ONE DESK REFERENCE FOR DUMMIESData Files and DatabasesI. Irreducible complexityAny software system that performs a useful function is going to be complex. The more valuable the function, the more complex its implementation will be. Regardless of how the data is stored, the complexity remains. The only question is where that complexity resides. Any non-trivial computer application has two major components: the program the data. Although an application’s level of complexity depends on the task to be performed, developers have some control over the location of that complexity. The complexity may reside primarily in the program part of the overall system, or it may reside in the data part.Operations on the data can be fast. Because the programinteracts directly with the data, with no DBMS in the middle, well-designed applications can run as fast as the hardware permits. What could be better? A data organization that minimizes storage requirements and at the same time maximizes speed of operation seems like the best of all possible worlds. But wait a minute . Flat file systems came into use in the 1940s. We have known about them for a long time, and yet today they have been almost entirely replaced by database s ystems. What’s up with that? Perhaps it is the not-so-beneficial consequences。

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会计舞弊的预警信号的潜在蓝筹股–应用自适应共振理论摘要：随着策略的不断变化，欺骗性可以隐藏在财务报表的复杂、传统分析手段是无法提前发现这些会计欺诈。

为了检测新的会计欺诈和发现资产负债表外安排的真正意义，我们提出了一个使用一种无监督的学习系统的简单易行的方法。

在无监督学习，网络的训练完全是数据驱动的，没有目标的结果。

这样不利于聚类中的特征可以被删除。

与无监督学习可以学到更大和更比监督学习复杂的关系。

关键字：预警信号；无监督学习；会计舞弊；自适应共振理论1、引言虽然有一些公司的投资者认为是非常安全的，信心受到了一些在许多美国大公司会计造假案的动摇，从医院供应商水下光缆的安全系统。

大型投资银行和独立的公共会计师也参与了。

为了确保业务相关的公司，投资银行向公众提出忠告。

当咨询服务成为非常有利可图的，有越来越多的利益冲突的审计作用和咨询作用。

因为财务报表是衡量公司业绩的基础上，会计造假行为不仅损害了投资者的信心，也伤害了所有相关的绩效管理分析。

2002萨班斯-奥克斯利法案实施应对这些严重的欺诈行为，但建议改革仍未能解决，首席执行官（CEO）的主要问题，首席财务官（CFO），和其他董事及高级管理人员为他们的个人利益损害股东的权益。

我们使用因素是否显著的动机或压力作为补充编码存在。

即使有很多复杂的资产负债表的安排，涉及数百万或数十亿的损失欺诈，一些项目必须有异常变化或有严重的流动性问题。

基于这些猜测和无监督学习系统，我们勾勒出四个预警信号。

与无监督学习可以学到更大和更比监督学习复杂的关系。

训练完全是数据驱动的，没有目标的结果。

这样不利于聚类中的特征可以被删除。

基于定性匹配自适应共振理论（ART）允许无监督学习记忆改变，只有当输入从外部世界是足够接近的内部期望，或完全新的东西出现时。

这个特性使得非常适合于需要大的和不断变化的数据库在线学习问题的艺术系统，可用于会计舞弊的分析。

2、神经网络与会计舞弊神经网络程序已经被用于一些金融应用–欺诈检测，风险管理，经济预测，并建立信用评级。

然而，仍有极少数的论文，分析会计舞弊的神经网络。

大多数应用程序使用的BP模型具有一个隐藏层。

Lin等人。

检测20个实际欺诈案件只有7。

校正率比低得多的猜测。

他们的预测性能不佳可能来自滥用传统的财务指标作为预警信号，因此对模糊函数的错误。

根据彼得森的说法，这是三角诈骗，鼓励犯罪。

首先，必须有一个显着的动机或压力欺诈。

第二，感知的机会能够发现必须存在欺诈。

第三元是合理的，或找到一个可以接受的借口。

2002萨班斯-奥克斯利法案禁止大多数的舞弊审计，分析师，和业主借款。

它还提高了报告的透明度。

但只要有动机或压力的存在，欺诈可能会永远。

会计舞弊使传统的财务分析方法无效。

例如，注册舞弊审核师协会（ACFE）2005和2006欺诈调查[ 23 ]也显示有40%的欺诈检测技巧。

因此，内部审计，内部控制，外部审计是相对无效的，所以它是提出计划预警信号来检测潜在的欺诈行为非常重要的投资者或分析师。

监督和无监督学习的区别仅在模型的因果结构。

多层感知器（MLP）和径向基函数（RBF）是监督学习的类型，而自组织映射（SOM）和自适应共振理论（ART）是无监督学习类型。

在监督学习中，该模型定义了输入对输出的影响。

相反，与无监督学习，网络的训练完全是数据驱动的，没有目标的结果为输入数据提供。

与无监督学习有可能找到更大，更复杂，更灵活的欺诈比监督学习。

在SOM，距离函数是必不可少的，它是定量分析部分。

因为距离不再是重要的欺诈模式识别，艺术的定性分析更适合。

用艺术，许多不重要的功能，不利于聚类可以被删除。

大多数聚类方法需要聚类数目K是预先指定或选定后从一组集群解决方案，在一个范围内的K艺术也可以解决问题。

提取重要的特征后，一个模糊的艺术可能更适合对未来通用会计舞弊，如非均匀的编码器的基于模糊集和信原等人提出了一种模糊均衡重叠度的模糊模型。

3、萨班斯-奥克斯利法案自2002萨班斯-奥克斯利法案对会计监管的重大影响，我们总结从SEC特别研究[ 21 ]一些重要的文章，显示出一定的差异。

改革的重要内容如下。

（1）对审计人员。

103节：董事会必须需要二伙伴审查和审计报告的批准；201节：它应是非法注册的会计公司对发行人的同时提供非审计服务的审计。

203节：领导审核或协调合作伙伴和考察合作伙伴必须每五年轮换审计。

（2）分析师。

501节：国家证券交易所和证券业协会的注册必须通过研究分析师在研究报告中推荐的股票利益冲突规则。

（3）董事会。

401节：财务报告应当披露所有的松散实体，包括特殊目的实体的使用。

402节：这将是非法的发行人提供信贷经理或执行官。

407节：发行人需要披露其审计委员会中至少一个成员是一位金融专家。

行为有助于审计师和分析师防止欺诈，并规定更严格的控制，提高财务报表的透明度。

然而，它已被证明有缺陷由于欺诈是通用的，这是不可能的专业属性stomeet 信任水平。

有几篇文章讨论改革的效果，例如，gorik托马谢夫斯基和MC麦卡锡[ 7 ]。

还有几个是自语句已受到欺诈，如注意力，他报告的依据对2002萨班斯-奥克斯利法案704条款。

（1）不当的收入确认。

这是确认收入，虚构收入和收入的时机不当，不当的评价。

（2）不当的费用确认。

有不正当的资本/递延–故障记录，费用或损失的，夸大的结局发—保守党的价值观，重组和其他责任准备金使用不当，低估了坏账准备金贷款损失和失败，记录资产减值。

（3）与业务组合连接不当会计。

有不正当的资产评估，合并储备使用不当，不适当的应用收购/合并的方法。

（4）不当会计其他领域。

在管理层讨论与分析不充分的披露和其他发行人申请，未披露关联方交易，非货币往返不恰当的会计交易，在外国腐败行为违反外债不当会计法案（FCPA），非使用不当造成的一般公认会计原则（非GAAP）财务措施，以及资产负债表外安排不当。

安然事件后的改革成效是由Agrawal和查达1实证检验。

他们研究159美国公共公司，重申了收入和行业的大小相匹配的样本的对照样本公司。

他们发现，传统的公司治理机制仍然是对概率无关一个公司的盈余重述。

4、ART算法自1976 [ 8 ]介绍了一类艺术，理论已经演变为一个系列的实时神经网络模型进行无监督和监督学习，模式识别，预测。

详细的 e.chung-chiung日元/信息科学177（2007）4515 4525 4517插图艺术–由weenink [ 24 ]，陈等人。

[ 4 ]，文[ 13 ]，和木匠和Grossberg [ 3 ]。

所有艺术系统的核心功能是一个模式匹配的过程，将外部输入的有源代码和内存间。

艺术匹配要么导致谐振状态，或一个并行的内存搜索。

如果搜索结束在一个既定的程序，内存中的表示可以保持不变或将匹配当前输入的部分新信息。

如果搜索结束在一个新的编码，存储表示学习的电流输入。

这场比赛为基础的学习过程是艺术的基础代码的稳定性。

最初，一个输入模式寄存器本身作为短期记忆活动模式在一场的节点F 1。

会聚和发散的途径，导致从F 1到F 2编码领域，每个由一个自适应加权长期记忆痕迹。

具有很强的竞争，激活集中在F 2节点接收到最大的F 1！F 2信号；在这个赢家通吃的模式，只有一个代码的部分仍然是积极的。

艺术的匹配准则的参数，Q，确定称为警觉，它指定的输入，必须保持在匹配模式为共振发生的最低分数。

低的警惕，允许广泛的推广，粗分类，抽象的回忆。

警惕性很高，导致狭窄的通用化，精细的分类，并详细的回忆。

而警惕的是无监督的艺术网的一个自由参数，在监督网络警惕成为内部控制的变量。

5、经验设计对于艺术结构如图1所示。

为了证明我们使用经验设计，安然和世通的自上而下的学习原型和五大半导体–宽线COM公司自下而上的输入。

他们是英特尔（Intel），德克萨斯仪器（TI），先进微设备公司（AMD），意法半导体（STM），和模拟设备（ADI）。

能源交易巨头安然似乎已经打破了骆驼的背在煽动在审计监管气候变化的稻草，和世通公司的破产是美国历史上最大的。

因此我们选择这两家公司作为示范自上而下的原型。

有两件艺术。

艺术1是系统获得会计舞弊或重要特征预警信号。

它是从数据仓库的在线学习，如重述文件，行政程序进行文件，悬挂档案，诉讼档案。

由于训练完全是数据驱动的，没有目标e.chung-chiung 日元/信息科学177（2007）提供4515 4525 4519–结果，这样不利于聚类中的特征可以被删除。

从艺术1，我们可以得到自上而下的共同特征学习原型。

2艺术是一种模式匹配过程的系统自下而上与自上而下的比较投入学习原型。

自下而上的输入，投资者和分析师可能涉及案件。

图1 艺术建筑5.1预警信号我们使用因素是否显著的动机或压力作为补充编码存在。

COM的实现编码双打输入组件的数量，提交给网络的原始特征向量及其补充。

这允许识别系统在其内存中的表示特点，始终没有编码，以及那些始终存在的。

安然的重大压力来自汽油价格的急剧增加。

每加仑汽油的汽油美元上升到近1美元2美元1999。

世通公司的重大压力是1996电信法案旨在竞争开门。

在比赛过程中去成长，电信公司过度的能力。

例如，只有10%的纤维39000000英里—在美国铺设光纤电缆是目前使用的。

即使有很多复杂的资产负债表的安排，涉及数百万或数十亿的损失欺诈，一些项目必须有异常变化或有严重的流动性问题。

基于在这些猜测和艺术模型，我们从以下四个简单可行的警告signals.fig 1。

艺术建筑。

4520日元/信息科学e.chung-chiung–4525 4515 177（2007）（1）项的变化率超过50%的不可接受的原因。

蓝筹股，不应该有一个急剧增加或减少会计价值无特殊原因。

我们任意地设置阈值的异常变化率为50%，与先前的年度报告相比。

所有的阈值设置在我们的预警系统可以通过学习过程的改进。

因为我们的目的是检测是否赢利/绩效是充气的，我们跳，有显着减少的收入/资产/股权或增加成本/负债项目。

（2）现金比率小于或等于25%。

流动比率和速动比率是公司满足其短期债务的能力，传统的财务指标。

然而，以下几个明显的会计造假行为，这些比率是不合适的指标。

例如，它可能夸大期末存货价值。

另一个例子是一个虚假的策略可能存在于当前的资产价格风险管理活动。

因此，我们转而使用现金比率，即现金及现金等价物除以流动负债。

（3）净资产比率小于或等于25%。

资产负债率是传统的财务指标来衡量公司满足其所有负债能力。

但因为公司可以把资产总额，该指标不再能可靠地作为一个警告信号。