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人工智能英文参考文献(最新120个)
人工智能是一门新兴的具有挑战力的学科。
自人工智能诞生以来,发展迅速,产生了许多分支。
诸如强化学习、模拟环境、智能硬件、机器学习等。
但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。
下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。
人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? 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Current opinion in ophthalmology,2020,31(3).[51]Ho Dean,Quake Stephen R,McCabe Edward R B,Chng Wee Joo,Chow Edward K,Ding Xianting,Gelb Bruce D,Ginsburg Geoffrey S,Hassenstab Jason,Ho Chih-Ming,Mobley William C,Nolan Garry P,Rosen Steven T,Tan Patrick,Yen Yun,Zarrinpar Ali. Enabling Technologies for Personalized and Precision Medicine.[J]. Trends in biotechnology,2020,38(5).[52]Fischer Andreas M,Varga-Szemes Akos,van Assen Marly,Griffith L Parkwood,Sahbaee Pooyan,Sperl Jonathan I,Nance John W,Schoepf U Joseph. Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.[J]. AJR. American journal ofroentgenology,2020,214(5).[53]Moore William,Ko Jane,Gozansky Elliott. Artificial Intelligence Pertaining to Cardiothoracic Imaging and Patient Care: Beyond Image Interpretation.[J]. Journal of thoracic imaging,2020,35(3).[54]Hwang Eui Jin,Park Chang Min. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. Cardiovascular research,2020,116(6).[59]Fischer Andreas M,Eid Marwen,De Cecco Carlo N,Gulsun Mehmet A,van Assen Marly,Nance John W,Sahbaee Pooyan,De Santis Domenico,Bauer Maximilian J,Jacobs Brian E,Varga-Szemes Akos,Kabakus Ismail M,Sharma Puneet,Jackson Logan J,Schoepf U Joseph. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[60]Ghosh Adarsh,Kandasamy Devasenathipathy. Interpretable Artificial Intelligence: Why and When.[J]. AJR. American journal of roentgenology,2020,214(5).[61]M.Rosario González-Rodríguez,M.Carmen Díaz-Fernández,Carmen Pacheco Gómez. Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions[J]. Telematics and Informatics,2020,51.[62]Ru-Xi Ding,Iván Palomares,Xueqing Wang,Guo-Rui Yang,Bingsheng Liu,Yucheng Dong,Enrique Herrera-Viedma,Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective[J]. Information Fusion,2020,59.[63]Abdulrhman H. Al-Jebrni,Brendan Chwyl,Xiao Yu Wang,Alexander Wong,Bechara J. Saab. AI-enabled remote and objective quantification of stress at scale[J]. Biomedical Signal Processing and Control,2020,59.[64]Gillian Thomas,Elizabeth Eisenhauer,Robert G. Bristow,Cai Grau,Coen Hurkmans,Piet Ost,Matthias Guckenberger,Eric Deutsch,Denis Lacombe,Damien C. Weber. The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report[J]. European Journal of Cancer,2020,131.[65]Muhammad Asif. Are QM models aligned with Industry 4.0? A perspective on current practices[J]. Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. Shlobin,Kartik Kesavabhotla,Zachary A Smith,Nader S Dahdaleh. Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions[J]. Clinical Neurology and Neurosurgery,2020,192.[71]Mei Yang,Runze Zhou,Xiangjun Qiu,Xiangfei Feng,Jian Sun,Qunshan Wang,Qiufen Lu,Pengpai Zhang,Bo Liu,Wei Li,Mu Chen,Yan Zhao,Binfeng Mo,Xin Zhou,Xi Zhang,Yingxue Hua,Jin Guo,Fangfang Bi,Yajun Cao,Feng Ling,Shengming Shi,Yi-Gang Li. Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals[J]. Environment International,2020,139.[72]Fatemehalsadat Madaeni,Rachid Lhissou,Karem Chokmani,Sebastien Raymond,Yves Gauthier. Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review[J]. Cold Regions Science and Technology,2020,174.[73]Steve Chukwuebuka Arum,David Grace,Paul Daniel Mitchell. A review of wireless communication using high-altitude platforms for extended coverage and capacity[J]. Computer Communications,2020,157.[74]Yong-Hong Kuo,Nicholas B. Chan,Janny M.Y. Leung,Helen Meng,Anthony Man-Cho So,Kelvin K.F. Tsoi,Colin A. Graham. An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department[J]. International Journal of Medical Informatics,2020,139.[75]Matteo Terzi,Gian Antonio Susto,Pratik Chaudhari. Directional adversarial training for cost sensitive deep learning classification applications[J]. Engineering Applications of Artificial Intelligence,2020,91.[76]Arman Kilic. Artificial Intelligence and Machine Learning in Cardiovascular Health Care[J]. The Annals of Thoracic Surgery,2020,109(5).[77]Hossein Azarmdel,Ahmad Jahanbakhshi,Seyed Saeid Mohtasebi,Alfredo Rosado Mu?oz. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)[J]. Postharvest Biology and Technology,2020,166.[78]Wafaa Wardah,Abdollah Dehzangi,Ghazaleh Taherzadeh,Mahmood A. Rashid,M.G.M. Khan,Tatsuhiko Tsunoda,Alok Sharma. Predicting protein-peptide binding sites with a deep convolutional neural network[J]. Journal of Theoretical Biology,2020,496.[79]Francisco F.X. Vasconcelos,Róger M. Sarmento,Pedro P. Rebou?as Filho,Victor Hugo C. de Albuquerque. Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis[J]. Engineering Applications of Artificial Intelligence,2020,91.[80]Masaaki Konishi. Bioethanol production estimated from volatile compositions in hydrolysates of lignocellulosic biomass by deep learning[J]. Journal of Bioscience and Bioengineering,2020,129(6).人工智能英文参考文献三：[81]J. Kwon,K. Kim. Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography[J]. Journal of Heart and Lung Transplantation,2020,39(4).[82]C. Maathuis,W. Pieters,J. van den Berg. Decision support model for effects estimation and proportionality assessment for targeting in cyber operations[J]. Defence Technology,2020.[83]Samer Ellahham. Artificial Intelligence in Diabetes Care[J]. The American Journal of Medicine,2020.[84]Yi-Ting Hsieh,Lee-Ming Chuang,Yi-Der Jiang,Tien-Jyun Chang,Chung-May Yang,Chang-Hao Yang,Li-Wei Chan,Tzu-Yun Kao,Ta-Ching Chen,Hsuan-Chieh Lin,Chin-Han Tsai,Mingke Chen. Application of deep learning image assessment software VeriSee? for diabetic retinopathy screening[J]. Journal of the Formosan Medical Association,2020.[85]Emre ARTUN,Burak KULGA. Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference[J]. 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科技术语自动提取技术
科技术语自动提取技术作者：***来源：《中国科技术语》2022年第01期摘要：文章简要介绍了自动术语提取任务的定义、主要方法和评价指标。
针对传统的自动术语提取方法，以互信息、t值、tf-idf、C/NC-value为例介绍了单元度和术语度的概念;针对自动术语标注方法，主要介绍了基于序列标注的建模思想。
从提取效果来看，现有自动术语提取技术距离期望仍有差距，文章也尝试给出了一些值得探索的方向。
关键词：自动术语提取;自动术语标注;单元度;术语度;机器学习中图分类号：TP391;H083 文献标识码：A DOI：10.12339/j.issn.1673-8578.2022.01.001Techniques of Automatic Term Extraction：Current Sate and Reflections//CHANG BaobaoAbstract： This paper overviews the definition， major approaches and the evaluation metrics of the ATE task. For the traditional approaches， we mainly elaborate the measurement of the Unithood and Termhood， using pointwise mutual information， t-value， ti-idf weighting and C/NC-value as examples. For Automatic Term Labelling， we mainly present the sequence labelling modelling. We think the performance of Automatic Term Extraction/Labelling is still not satisfactory from a point of view of real application， and try to offer a few directions of further improvements.Keywords： automatic term extraction; automatic term labelling; unithood; termhood; machine learning引言术语（term）是“各门学科的专门用语，在专业范围内表示单一的专门概念”[1]。
计算理论基础课件II
2.1 Deterministic Finite Automata(DFA)
Why Deterministic
Automata reads one symbol from the input tape and then enters a new state that depends only on the current state and the symbol just read.
Formally, the transition function can be extended to *(q, w), where w is any string of input symbols.
Basis: *(q, ) = q Induction: *(q, wa) = (*(q, w), a)
Yield * :
the reflexive transitive closure of . (q,w) * (q’,w’ )
A string w* is said to be accepted by M if and only if qF such that (s,w) * (q,e). The language accepted by M, L(M), is the set of all strings accepted by M.
Example 2.1.2
Design a DFA M that accepts the language L(M)={w{a,b}*: w does not contain the bbb substrings} Let M=(K, , , s, F) K = {q0, q1 , q2 , q3} ={a,b} q (q,) s= q0 q0 a q0 q0 b q1 F={q0 , q1 , q2} q1 a q0 : q b q
matlab 英文模糊匹配算法
matlab 英文模糊匹配算法摘要：一、引言二、Matlab 简介三、英文模糊匹配算法1.概念解释2.算法原理3.实现方法四、Matlab 实现英文模糊匹配算法的优势五、总结正文：一、引言随着互联网技术的飞速发展，搜索引擎成为人们获取信息的主要途径。
在这个过程中，如何有效地进行文本匹配和检索成为一个关键问题。
英文模糊匹配算法就是为了解决这个问题而提出的。
Matlab 作为一款功能强大的数学软件，可以方便地实现英文模糊匹配算法。
二、Matlab 简介Matlab 是一种数学软件，广泛应用于科学计算、数据分析、可视化等领域。
它的语法简单，易于上手，支持多种编程方式，如脚本编程、命令行编程和GUI 编程等。
同时，Matlab 还有丰富的工具箱，可以方便地实现各种功能。
三、英文模糊匹配算法1.概念解释英文模糊匹配算法是一种在文本处理中常用的算法，它可以在一定程度上忽略文本中字母的大小写、单词的顺序和拼写错误等，从而实现对文本的高效匹配。
2.算法原理英文模糊匹配算法通常基于编辑距离（Levenshtein 距离）进行计算。
编辑距离是指将一个字符串转换成另一个字符串所需的最少编辑操作次数。
常见的编辑操作包括插入、删除和替换。
通过计算编辑距离，可以衡量两个字符串之间的相似度。
3.实现方法在Matlab 中，可以利用现有的函数和工具箱实现英文模糊匹配算法。
例如，可以使用Matlab 的string 函数处理字符串，计算编辑距离。
此外，还可以利用Matlab 的优化工具箱进行算法优化。
四、Matlab 实现英文模糊匹配算法的优势Matlab 在实现英文模糊匹配算法时具有以下优势：1.Matlab 的语法简单，易于上手，可以降低实现算法的难度；2.Matlab 具有丰富的函数和工具箱，可以方便地实现各种功能；3.Matlab 的图形界面功能强大，可以直观地展示算法结果；4.Matlab 支持多种编程方式，可以满足不同用户的需求。
US 8,839,695 B2号专利中英对照
(2013.01)
USPC81/9.51
USPC81/9.51
(58) Field of Classification Search
（58）分类检索领域
USPC81/9.51; 30/90.1, 90.4, 90.8, 90.9;
USPC81/9.51; 30/90.1, 90.4, 90.8, 90.9;
The device employs cutting blades engaged to a first roller to cut a slice along the length of an inserted insulated wire or cable.
该设备采用的切割刀片与第一压辊接合，沿着插入的绝缘电线或电缆的长度切割线缆。
A frictional engagement between the distal edge of the blade and an opposing recess surface provides for translation of the length of wire through the device during rotation of one or both of the blade and recess surface.
发明领域
This application claims priority to Chinese Application Serial Number 201030705094.5 filed on Dec. 31, 2011, and incorporated herein by reference in its entirety.
为了保护环境，避免过度使用原材料，现在世界上的许多国家都鼓励回收利用先前精加工的材料。
大学实用翻译教程(英汉双向 )第三章 计算机辅助翻译
1.2计算机辅助翻译的发展前景展望
• 根据前面对机器翻译的介绍可以看出，自然语言的复杂性决定了机器翻译 技术发展的困难性。计算机语言学家提出了各种各样的机器翻译理论，但 目前为止还没有哪种理论能够有效的解决所有问题。但是，机器翻译系统 的应用领域正在发生变化。人们正在尝试将机译技术结合到信息访问、信 息提取和自动文摘中。这类跨语言应用在全球范围内越来越引起人们的兴 趣。未来对于口语的翻译也是市场迫切需求的，但尽管基于某些方面的需 求，机器翻译的发展方向更加多元化，但无论从实际应用角度还是从理论 研究角度来看，全自动的话语翻译还是很难实现的。当然，虽然机器翻译 的发展有诸多阻碍，但仍然有更多的发展希望。目前，单语语料库加工技 术以及应用在计算语言学领域内取得的成功，使建立双语或多语语料库并 进行多层次的加工作为大规模的跨语言资源成为研究的焦点之一。另外， 大规模的语料库适合于统计方法的应用，通过统计来自动进行知识获取， 有助于克服自然语言处理中知识获取的瓶颈。把机器学习方法应用到机器 翻译中，可以帮众系统实现在线学习功能，最终建立主动的智能翻译服务 也是机器翻译的发展方向之一。
1.1计算机辅助翻译的发展历程
• “机器翻译”的概念可以追溯到20世纪30年代，40-50年代初经历 了早期的尝试阶段，当时，大多数从事手工翻译人士对于“利用计 算机进行翻译”不以为然，他们根本就不相信翻译会机械化，少数 人则或多或少有一点担心，害怕有一天机器会把他们取而代之。 • 50—60年代中期学界对于“机器翻译”持高度乐观的态度。 Systran翻译软件将“机器翻译”定义为利用计算机软件把文本内 容从一种自然语言转换成另一种自然语言，这个定义就是说“机器 翻译”是利用语言结构、规律和把原文（the source language） 的语言结构转换成译文(the target language)的语言结构。“机器 翻译”这一想法产生的时间正是结构主义语言学的观点盛行时期。 由于人类对语言结构规律的研究越发深入，语言学家为翻译找到了 更多的理论依据作为支撑，从而给翻译加上了科学主义的色彩。奈 达、巴尔胡达罗夫以及彼得· 纽马克等人的翻译观都是从结构主义 理论开始的。（张治中，俞可怀，2002：54-58）结构主义者认 为在各种复杂的表面现象的下都有着一种普遍性的规律，这些规律 就是结构，人们通过分割归并作品的各种结构就可弄清语言信息变 成文艺作品的奥秘。（吕俊， 2001:96-111）
计算机毕设英文参考文献
计算机毕设英文参考文献当涉及到毕业设计或者毕业论文的参考文献时，你可以考虑以下一些经典的计算机科学领域的文献：1. D. E. Knuth, "The Art of Computer Programming," Addison-Wesley, 1968.2. A. Turing, "On Computable Numbers, with an Application to the Entscheidungsproblem," Proceedings of the London Mathematical Society, 1936.3. V. Bush, "As We May Think," The Atlantic Monthly, 1945.4. C. Shannon, "A Mathematical Theory of Communication," Bell System Technical Journal, 1948.5. E. W. Dijkstra, "Go To Statement Considered Harmful," Communications of the ACM, 1968.6. L. Lamport, "Time, Clocks, and the Ordering of Events in a Distributed System," Communications of the ACM, 1978.7. T. Berners-Lee, R. Cailliau, "WorldWideWeb: Proposal for a HyperText Project," 1990.8. S. Brin, L. Page, "The Anatomy of a Large-Scale Hypertextual Web Search Engine," Computer Networks and ISDN Systems, 1998.这些文献涵盖了计算机科学领域的一些经典工作，包括算法、计算理论、分布式系统、人机交互等方面的内容。
自动机理论 语言和计算导论课后习题答案
Basis: If y = ε, then the statement is δ-hat(q,x) = δ-hat(δ-hat(q,x),ε). This statement follows from the basis in the definition of δ-hat. Note that in applying this definition, we must treat δ-hat(q,x) as if it were just a state, say p. Then, the statement to be proved is p = δ-hat(p,ε), which is easy to recognize as the basis in the definition of δ-hat.
01 ->s d q1 *q0 q0 q1 q1 q2 q3 q2 q4 q0 q3 q1 q2 q4 q3 q4
dd d
Exercise 2.2.9
Part (a) is an easy induction on the length of w, starting at length 1.
Basis: |w| = 1. Then δ-hat(q0,w) = δ-hat(qf,w), because w is a single symbol, and δ-hat agrees with δ on single symbols.
Definition of δ-hat
δˆ 的定义
δˆ (q,xy)
y=za
Exercise 2.2.4(a)