Artificial Intelligence in Engineering最新文献_第8页

On-line control chart pattern detection and discrimination—a neural network approach 在线控制图模式检测与判别——一种神经网络方法

Artificial Intelligence in Engineering Pub Date : 1999-10-01 DOI: 10.1016/S0954-1810(99)00022-9

R.-S. Guh, F. Zorriassatine, J.D.T. Tannock, C. O'Brien

{"title":"On-line control chart pattern detection and discrimination—a neural network approach","authors":"R.-S. Guh, F. Zorriassatine, J.D.T. Tannock, C. O'Brien","doi":"10.1016/S0954-1810(99)00022-9","DOIUrl":"10.1016/S0954-1810(99)00022-9","url":null,"abstract":"<div><p>Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. A common difficulty in existing control chart pattern recognition approaches is that of discrimination between different types of patterns which share similar features. This paper proposes an artificial neural network based model, which employs a pattern discrimination algorithm to recognise unnatural control chart patterns. The pattern discrimination algorithm is based on several special-purpose networks trained for specific recognition tasks. The performance of the proposed model was evaluated by simulation using two criteria: the percentage of correctly recognised patterns and the average run length (ARL). Numerical results show that the false recognition problem has been effectively addressed. In comparison with previous control chart approaches, the proposed model is capable of superior ARL performance while the type of the unnatural pattern can also be accurately identified.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(99)00022-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91081397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 82

A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines 在调度相同并行机器的情况下最小化完工时间的遗传算法

Artificial Intelligence in Engineering Pub Date : 1999-10-01 DOI: 10.1016/S0954-1810(99)00021-7

Liu Min, Wu Cheng

引用次数: 123

Speech understanding and speech translation by maximum a-posteriori semantic decoding 基于最大后验语义解码的语音理解与翻译

Artificial Intelligence in Engineering Pub Date : 1999-10-01 DOI: 10.1016/S0954-1810(99)00010-2

J. Müller , H. Stahl

{"title":"Speech understanding and speech translation by maximum a-posteriori semantic decoding","authors":"J. Müller , H. Stahl","doi":"10.1016/S0954-1810(99)00010-2","DOIUrl":"https://doi.org/10.1016/S0954-1810(99)00010-2","url":null,"abstract":"<div><p>This paper describes a domain-limited system for speech understanding as well as for speech translation. An integrated semantic decoder directly converts the preprocessed speech signal into its semantic representation by a maximum a-posteriori classification. With the combination of probabilistic knowledge on acoustic, phonetic, syntactic, and semantic levels, the semantic decoder extracts the most probable meaning of the utterance. No separate speech recognition stage is needed because of the integration of the Viterbi-algorithm (calculating acoustic probabilities by the use of Hidden-Markov-Models) and a probabilistic chart parser (calculating semantic and syntactic probabilities by special models). The semantic structure is introduced as a representation of an utterance's meaning. It can be used as an intermediate level for a succeeding intention decoder (within a speech understanding system for the control of a running application by spoken inputs) as well as an interlingua-level for a succeeding language production unit (within an automatic speech translation system for the creation of spoken output in another language). Following the above principles and using the respective algorithms, speech understanding and speech translating front-ends for the domains ‘graphic editor’, ‘service robot’, ‘medical image visualisation’ and ‘scheduling dialogues’ could be successfully realised.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(99)00010-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91720191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Neural network modeling of active devices for use in MMIC design 用于MMIC设计的有源器件的神经网络建模

Artificial Intelligence in Engineering Pub Date : 1999-10-01 DOI: 10.1016/S0954-1810(99)00011-4

F. Güneş, H. Torpi, B.A. Çetiner

引用次数: 12

Automatic diagnosis with genetic algorithms and case-based reasoning 基于遗传算法和案例推理的自动诊断

Artificial Intelligence in Engineering Pub Date : 1999-10-01 DOI: 10.1016/S0954-1810(99)00009-6

J.M. Garrell i Guiu, E. Golobardes i Ribé, E. Bernadó i Mansilla, X. Llorà i Fàbrega

引用次数: 54

Graph theory representations of engineering systems and their embedded knowledge 图论表示的工程系统和他们的嵌入式知识

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(99)00002-3

O. Shai , K. Preiss

引用次数: 44

A knowledge-based dynamic job-scheduling in low-volume/high-variety manufacturing 小批量多品种制造中基于知识的动态作业调度

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(98)00014-4

Yaoxue Zhang, Hua Chen

引用次数: 16

Function approximations by coupling neural networks and genetic programming trees with oblique decision trees 用斜决策树耦合神经网络和遗传规划树的函数逼近

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(98)00015-6

Y.-S. Yeun , K.-H. Lee , Y.-S. Yang

{"title":"Function approximations by coupling neural networks and genetic programming trees with oblique decision trees","authors":"Y.-S. Yeun , K.-H. Lee , Y.-S. Yang","doi":"10.1016/S0954-1810(98)00015-6","DOIUrl":"10.1016/S0954-1810(98)00015-6","url":null,"abstract":"<div><p>This paper is concerning the development of the hybrid system of neural networks and genetic programming (GP) trees for problem domains where a complete input space can be decomposed into several different subregions, and these are well represented in the form of oblique decision tree. The overall architecture of this system, called federated agents, consists of a facilitator, local agents, and boundary agents. Neural networks are used as local agents, each of which is expert at different subregions. GP trees serve as boundary agents. A boundary agent refers to the one that specializes at only the borders of subregions where discontinuities or a few different patterns may coexist. The facilitator is responsible for choosing the local agent that is suitable for given input data using the information obtained from oblique decision tree. However, there is a large possibility of selecting the invalid local agent as result of the incorrect prediction of decision tree, provided that input data is close enough to the boundaries. Such a situation can lead the federated agents to produce a higher prediction error than that of a single neural network trained over the whole input space. To deal with this, the approach taken in this paper is that the facilitator selects the boundary agent instead of the local agent when input data is closely located at certain border of subregions. In this way, even if decision tree yields an incorrect prediction, the performance of the system is less affected by it. The validity of our approach is examined by applying federated agents to the approximation of the function with discontinuities and the configuration of the midship section of bulk cargo ships.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(98)00015-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79717555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Identification of plant inverse dynamics using neural networks 利用神经网络识别植物逆动态

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(99)00003-5

D.T. Pham, S.J. Oh

引用次数: 21

Evaluating machine learning models for engineering problems 评估工程问题的机器学习模型

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(98)00021-1

Yoram Reich , S.V. Barai

引用次数: 148