Artificial Intelligence in Engineering最新文献_第2页

Incorporating machine intelligence in a parameter-based control system: a neural-fuzzy approach 在基于参数的控制系统中结合机器智能:一种神经模糊方法

Artificial Intelligence in Engineering Pub Date : 2001-07-01 DOI: 10.1016/S0954-1810(01)00020-6

H.C.W Lau , T.T Wong , A Ning

{"title":"Incorporating machine intelligence in a parameter-based control system: a neural-fuzzy approach","authors":"H.C.W Lau , T.T Wong , A Ning","doi":"10.1016/S0954-1810(01)00020-6","DOIUrl":"10.1016/S0954-1810(01)00020-6","url":null,"abstract":"<div><p>The capabilities of the two computational intelligence technologies including neural network and fuzzy logic can be synergized through the formation of an integrated and unified model which capitalizes on the benefits and concurrently offsets the flaws of the involved technologies. In this paper, a neural-fuzzy model, which is characterized by its ability to suggest the appropriate change of process parameters in a relatively complex parameter-based control situation involving multiple parameters, is presented. This model is particularly useful in multiple input and multiple output situations where complex mathematical calculations are required if conventional control approach is adopted. In particular, it serves to acquire knowledge from the information base for extracting rules, which are then fuzzified based on fuzzy principle. To validate the feasibility of this approach, a test has been conducted based on the neural-fuzzy model with the objective to achieve heat transfer enhancement in rectangular ducts using transverse ribs. This paper describes the roadmap for the deployment of this hybrid model to enhance machine intelligence of a complex system with the description of a case study to exemplify its underlying principles.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"15 3","pages":"Pages 253-264"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(01)00020-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84743793","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: 9

Adaptive reconstruction of bone geometry from serial cross-sections 自适应骨几何重建从连续横截面

Artificial Intelligence in Engineering Pub Date : 2001-07-01 DOI: 10.1016/S0954-1810(01)00006-1

George K. Knopf, Rasha Al-Naji

{"title":"Adaptive reconstruction of bone geometry from serial cross-sections","authors":"George K. Knopf, Rasha Al-Naji","doi":"10.1016/S0954-1810(01)00006-1","DOIUrl":"10.1016/S0954-1810(01)00006-1","url":null,"abstract":"<div><p>Many biomedical applications, such as the design of customized orthopaedic implants, require accurate mathematical models of bone geometry. The surface geometry is often generated by fitting closed parametric curves, or contours, to the edge points extracted from a sequence of evenly spaced planar images acquired using computed tomography (CT), magnetic resonance imaging (MRI), or ultrasound imaging. The Bernstein basis function (BBF) network described in this paper is a novel neural network approach to performing functional approximation tasks such as curve and surface fitting. In essence, the BBF architecture is a two-layer basis function network that performs a weighted summation of nonlinear Bernstein polynomials. The weight values generated during network training are equivalent to the control points needed to create a smooth closed Bézier curve in a variety of commercially available computer-aided design software. Modifying the number of basis neurons in the architecture is equivalent to changing the degree of the Bernstein polynomials. An increase in the number of neurons will improve the curve fit, however, too many neurons will diminish the network's ability to generate a smooth approximation of the cross-sectional boundary data. Additional constraints are imposed on the learning algorithm in order to ensure positional and tangential continuity for the closed curve. A simulation study and real world experiment are presented to show the effectiveness of this functional approximation method for reverse engineering bone structures from serial medical imagery.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"15 3","pages":"Pages 227-239"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(01)00006-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89646180","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: 24

Analysis of artificial neural network models for freeway ramp metering control 高速公路匝道计量控制中的人工神经网络模型分析

Artificial Intelligence in Engineering Pub Date : 2001-07-01 DOI: 10.1016/S0954-1810(01)00019-X

Chien-Hung Wei

引用次数: 33

Heuristic methods for vehicle routing problem with time windows 带时间窗车辆路径问题的启发式方法

Artificial Intelligence in Engineering Pub Date : 2001-07-01 DOI: 10.1016/S0954-1810(01)00005-X

K.C Tan , L.H Lee , Q.L Zhu , K Ou

{"title":"Heuristic methods for vehicle routing problem with time windows","authors":"K.C Tan , L.H Lee , Q.L Zhu , K Ou","doi":"10.1016/S0954-1810(01)00005-X","DOIUrl":"10.1016/S0954-1810(01)00005-X","url":null,"abstract":"<div><p>This paper documents our investigation into various heuristic methods to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions. The objective of the VRPTW is to serve a number of customers within predefined time windows at minimum cost (in terms of distance travelled), without violating the capacity and total trip time constraints for each vehicle. Combinatorial optimisation problems of this kind are non-polynomial-hard (NP-hard) and are best solved by heuristics. The heuristics we are exploring here are mainly third-generation artificial intelligent (AI) algorithms, namely simulated annealing (SA), Tabu search (TS) and genetic algorithm (GA). Based on the original SA theory proposed by Kirkpatrick and the work by Thangiah, we update the cooling scheme and develop a fast and efficient SA heuristic. One of the variants of Glover's TS, strict Tabu, is evaluated and first used for VRPTW, with the help of both recency and frequency measures. Our GA implementation, unlike Thangiah's genetic sectoring heuristic, uses intuitive integer string representation and incorporates several new crossover operations and other advanced techniques such as hybrid hill-climbing and adaptive mutation scheme. We applied each of the heuristics developed to Solomon's 56 VRPTW 100-customer instances, and yielded 18 solutions better than or equivalent to the best solution ever published for these problems. This paper is also among the first to document the implementation of all the three advanced AI methods for VRPTW, together with their comprehensive results.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"15 3","pages":"Pages 281-295"},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(01)00005-X","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76959931","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: 328

Generating code for engineering design systems using software patterns 使用软件模式为工程设计系统生成代码

Artificial Intelligence in Engineering Pub Date : 2001-04-01 DOI: 10.1016/S0954-1810(01)00018-8

Joan Peckham , Bonnie MacKellar

引用次数: 8

Conceptual modeling in design 设计中的概念建模

Artificial Intelligence in Engineering Pub Date : 2001-04-01 DOI: 10.1016/S0954-1810(01)00007-3

Mike Rosenman , Simeon J Simoff

引用次数: 1

Phenomenologial foundations of conceptual product modelling in architecture, engineering and construction 建筑、工程和建筑中概念产品建模的现象学基础

Artificial Intelligence in Engineering Pub Date : 2001-04-01 DOI: 10.1016/S0954-1810(01)00008-5

Žiga Turk

{"title":"Phenomenologial foundations of conceptual product modelling in architecture, engineering and construction","authors":"Žiga Turk","doi":"10.1016/S0954-1810(01)00008-5","DOIUrl":"10.1016/S0954-1810(01)00008-5","url":null,"abstract":"<div><p>The ultimate goal of conceptual modelling in architecture, engineering and construction (AEC) has been to define the data structures that could be used to describe the entire built environment through all its life cycle stages — from inception and design to demolition. In spite of the magnitude and complexity of this task, the theoretical foundations of modelling received little attention. In this paper, the theoretical foundations of the traditional modelling approaches are questioned using phenomenology and hermeneutics as philosophical base. The author exposes the difference between the remodelling of some existing models, the modelling of physical objects and the modelling of psychical, intentional objects. The author concludes that AEC or building product and process models do not model objective reality but the modeller's partial understanding of that reality. Therefore, several correct but different models may and should exist. Future software architectures in AEC should not be built on a unified, centralized model but, on a combination of models, which may not be standardised but whose schemas are encoded in a standard manner.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"15 2","pages":"Pages 83-92"},"PeriodicalIF":0.0,"publicationDate":"2001-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(01)00008-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87306840","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: 44

Knowledge level model of an individual designer as an agent in collaborative distributed design 协同分布式设计中个体设计者作为代理的知识层次模型

Artificial Intelligence in Engineering Pub Date : 2001-04-01 DOI: 10.1016/S0954-1810(01)00012-7

Frances M.T Brazier , Lilia V Moshkina , Niek J.E Wijngaards

引用次数: 26

Development of a maintenance management model based on IAI standards 开发基于IAI标准的维护管理模型

Artificial Intelligence in Engineering Pub Date : 2001-04-01 DOI: 10.1016/S0954-1810(01)00015-2

M.A Hassanain , T.M Froese , D.J Vanier

{"title":"Development of a maintenance management model based on IAI standards","authors":"M.A Hassanain , T.M Froese , D.J Vanier","doi":"10.1016/S0954-1810(01)00015-2","DOIUrl":"10.1016/S0954-1810(01)00015-2","url":null,"abstract":"<div><p>This paper presents an object model for maintenance management of roofing systems as a case study to demonstrate the applicability of a proposed generic framework for integrating the maintenance management of built-assets. The framework consists of five sequential management processes: (1) Identify Asset, (2) Identify Performance Requirements, (3) Assess Performance, (4) Plan Maintenance, (5) Manage Maintenance Operations. The model builds upon the Industry Foundation Classes (IFCs) (Releases 2.0 and 2X) to define object requirements and relationships for the exchange and sharing of maintenance information between applications. Maintenance management is one of the defined projects within the facilities management (FM) domain committee of the International Alliance for Interoperability (IAI). The paper proposes several extensions to the IFC's including the representation of functional requirements, assessed condition of objects, inspection and maintenance tasks, and libraries of non-specific information. Usage scenarios are provided to illustrate the use of the model to carry out selected processes.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"15 2","pages":"Pages 177-193"},"PeriodicalIF":0.0,"publicationDate":"2001-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(01)00015-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83623608","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: 66

Design ontology in context — a situated cognition approach to conceptual modelling 情境中的设计本体——概念建模的情境认知方法

Artificial Intelligence in Engineering Pub Date : 2001-04-01 DOI: 10.1016/S0954-1810(01)00010-3

Debbie Richards , Simeon J. Simoff

{"title":"Design ontology in context — a situated cognition approach to conceptual modelling","authors":"Debbie Richards , Simeon J. Simoff","doi":"10.1016/S0954-1810(01)00010-3","DOIUrl":"10.1016/S0954-1810(01)00010-3","url":null,"abstract":"<div><p>If we take a situated view of cognition, human thought and action are inextricably connected and affected by the context. It is not just the external environment that will affect the context but that thinking itself modifies further action and context occurs at a conceptual level that exists within a social setting. Thus, a situated view of knowledge necessitates knowledge acquisition techniques which handle change. This is particularly true of design knowledge where the design will change as more experience is gained and the changing model will itself change the perception of a design while designing. The approach described in this paper is based on the view that knowledge is always evolving and the premise that it is not easy to capture or evaluate a conceptual model. The alternative offered is based on the combined use of cases, ripple-down rules (RDR), formal concept analysis (FCA) and the Activity/Space (A/S) ontology. Cases are design episodes and used to motivate the capture of rules in a simple user-driven manner. Cases ground the KBS in the real world and provide the context in which the knowledge applies. Rules are the indexes by which the cases are retrieved. Using FCA, we are able to build an abstraction hierarchy of the rules and cases. To facilitate comparison and validation we use A/S design ontology to acquire a consistently organised set of cases. This ontology provides a common structure and shared set of descriptive terms. The ease with which the knowledge is acquired and maintained using RDR, the use of a dynamic design ontology and the automatic generation of conceptual models using FCA allows for the continual evolution of the KBS in keeping with the notion that knowledge is continually evolving and ‘made-up’ to fit the situation.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"15 2","pages":"Pages 121-136"},"PeriodicalIF":0.0,"publicationDate":"2001-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(01)00010-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91141434","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: 32