The long lead time for jig and fixture design is a serious bottle-neck in modern manufacturing. Efforts to automate the fixture design process using intelligent CAD systems have not been taken beyond the prototype stage, mainly because of impediments which include the requirement of complex data structures to represent the workpiece and its attributes. Two deficiencies in existing systems for fixture design are addressed; design for a sequence of operations and design with tolerance considerations. Tolerance and process specifications are obtained from the user during an interactive session. A knowledge-based design system, AutoFix II, designs a fixture for the workpiece. The fixture is optimised for a particular datum specification and sequence of operations. It is analysed and presented via the CAD system. AutoFix II is automated to minimise user-induced errors.
{"title":"Automated design of machining fixtures: tolerance and sequential operations","authors":"A. D. S. Lazaro, D. A. King","doi":"10.1049/ISE.1992.0015","DOIUrl":"https://doi.org/10.1049/ISE.1992.0015","url":null,"abstract":"The long lead time for jig and fixture design is a serious bottle-neck in modern manufacturing. Efforts to automate the fixture design process using intelligent CAD systems have not been taken beyond the prototype stage, mainly because of impediments which include the requirement of complex data structures to represent the workpiece and its attributes. Two deficiencies in existing systems for fixture design are addressed; design for a sequence of operations and design with tolerance considerations. Tolerance and process specifications are obtained from the user during an interactive session. A knowledge-based design system, AutoFix II, designs a fixture for the workpiece. The fixture is optimised for a particular datum specification and sequence of operations. It is analysed and presented via the CAD system. AutoFix II is automated to minimise user-induced errors.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"142 1","pages":"172-184"},"PeriodicalIF":0.0,"publicationDate":"1992-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80218093","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}
The analytic approach to signal processing performs well if there is adequate understanding of the characteristics of the signal source. In more complicated cases, syntactic signal processing tools used to be a working alternative; however, these share the common algorithmic background with the numerical methods. On the other hand, the filed area of order statistics (OS) introduced into signal processing a number of tools that handle phenomena that the usual analytic theory could not even model. To grasp the essence of the filtering operation requires a kind of symbolical description, ambiguous and full of dependencies, creating a gap between the filed and other customary areas of signal processing. Thus, proper choice of an OS filter for a given application must be based on a mixed numerical versus symbolical evaluation of the signal features and goals, which is clearly outside the scope of normal signal-processing expertise. A possible solution to this problem is to interface the OS tool library to the user via an advisory layer capable of the integrated maintenance of the quantitative and symbolic information, supporting the user in the modelling, decision and evaluation phases of problem-solving. The study presented in this paper addresses the concrete case of OS signal smoothing, evaluating the components of the problem and presenting the structure of the intelligent front-end system. >
{"title":"Knowledge-based approach to signal smoothing","authors":"A. Abdulrahim, T. Dobrowiecki","doi":"10.1049/ISE.1992.0006","DOIUrl":"https://doi.org/10.1049/ISE.1992.0006","url":null,"abstract":"The analytic approach to signal processing performs well if there is adequate understanding of the characteristics of the signal source. In more complicated cases, syntactic signal processing tools used to be a working alternative; however, these share the common algorithmic background with the numerical methods. On the other hand, the filed area of order statistics (OS) introduced into signal processing a number of tools that handle phenomena that the usual analytic theory could not even model. To grasp the essence of the filtering operation requires a kind of symbolical description, ambiguous and full of dependencies, creating a gap between the filed and other customary areas of signal processing. Thus, proper choice of an OS filter for a given application must be based on a mixed numerical versus symbolical evaluation of the signal features and goals, which is clearly outside the scope of normal signal-processing expertise. A possible solution to this problem is to interface the OS tool library to the user via an advisory layer capable of the integrated maintenance of the quantitative and symbolic information, supporting the user in the modelling, decision and evaluation phases of problem-solving. The study presented in this paper addresses the concrete case of OS signal smoothing, evaluating the components of the problem and presenting the structure of the intelligent front-end system. >","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"16 11 1","pages":"63-75"},"PeriodicalIF":0.0,"publicationDate":"1992-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90023503","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}
Compares fuzzy and Kalman-filter control systems for real-time target tracking. Both systems performed well in the presence of additive measurement noise. In the presence of mild process (unmodelled-effects) noise, the fuzzy system exhibited finer control. The authors tested the robustness of the fuzzy controller by removing random subsets of fuzzy associations or 'rules', and by adding destructive or 'sabotage' fuzzy rules to the fuzzy system. They tested the robustness of the Kalman tracking system by increasing the variance of the unmodelled-effects noise process. The fuzzy controller performed well until over 50% of the fuzzy rules were removed. The Kalman controller's performance quickly depreciated as the unmodelled-effects variance increased. The authors used unsupervised neural-network learning to adaptively generate the fuzzy controller's fuzzy-associative-memory structure. The fuzzy systems did not require a mathematical model of how system outputs depended on inputs. >
{"title":"Adaptive fuzzy systems for target tracking","authors":"P. J. Pacini, B. Kosko","doi":"10.1049/ISE.1992.0002","DOIUrl":"https://doi.org/10.1049/ISE.1992.0002","url":null,"abstract":"Compares fuzzy and Kalman-filter control systems for real-time target tracking. Both systems performed well in the presence of additive measurement noise. In the presence of mild process (unmodelled-effects) noise, the fuzzy system exhibited finer control. The authors tested the robustness of the fuzzy controller by removing random subsets of fuzzy associations or 'rules', and by adding destructive or 'sabotage' fuzzy rules to the fuzzy system. They tested the robustness of the Kalman tracking system by increasing the variance of the unmodelled-effects noise process. The fuzzy controller performed well until over 50% of the fuzzy rules were removed. The Kalman controller's performance quickly depreciated as the unmodelled-effects variance increased. The authors used unsupervised neural-network learning to adaptively generate the fuzzy controller's fuzzy-associative-memory structure. The fuzzy systems did not require a mathematical model of how system outputs depended on inputs. >","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"34 1","pages":"3-21"},"PeriodicalIF":0.0,"publicationDate":"1992-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90219143","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}
Considers the problem of qualitative modelling of discrete-time continuous-variable dynamical systems, for which only a quantised measurement (x(k)) of the state x(k) is available. The qualitative model has to describe the qualitative trajectory x(1), x(2),. . . for given qualitative initial state x(0) and qualitative input sequence. First, it is shown that the qualitative trajectory of the system is ambiguous. Hence, the qualitative model has to be nondeterministic. Secondly, it is shown that nondeterministic automata provide reasonable qualitative models of the continuous-variable system. The relation between the automaton and the given system shows what knowledge about the system has to be available if the qualitative model is to be set up. Thirdly, the authors propose to use stochastic automata, which provide a means for weighting each state concerning its appearance on the qualitative trajectory of the continuous-variable system. On this basis, the set of spurious solutions, which exist for any qualitative model, can be reduced. The suitability of the model becomes obvious by designing a qualitative controller. The results are illustrated by the problem of stabilising an 'inverted pendulum'. >
{"title":"Qualitative modelling of continuous-variable systems by means of non deterministic automata","authors":"J. Lunze","doi":"10.1049/ISE.1992.0003","DOIUrl":"https://doi.org/10.1049/ISE.1992.0003","url":null,"abstract":"Considers the problem of qualitative modelling of discrete-time continuous-variable dynamical systems, for which only a quantised measurement (x(k)) of the state x(k) is available. The qualitative model has to describe the qualitative trajectory x(1), x(2),. . . for given qualitative initial state x(0) and qualitative input sequence. First, it is shown that the qualitative trajectory of the system is ambiguous. Hence, the qualitative model has to be nondeterministic. Secondly, it is shown that nondeterministic automata provide reasonable qualitative models of the continuous-variable system. The relation between the automaton and the given system shows what knowledge about the system has to be available if the qualitative model is to be set up. Thirdly, the authors propose to use stochastic automata, which provide a means for weighting each state concerning its appearance on the qualitative trajectory of the continuous-variable system. On this basis, the set of spurious solutions, which exist for any qualitative model, can be reduced. The suitability of the model becomes obvious by designing a qualitative controller. The results are illustrated by the problem of stabilising an 'inverted pendulum'. >","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"113 1","pages":"22-30"},"PeriodicalIF":0.0,"publicationDate":"1992-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86222592","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}
The problem of allocating resources to activities may be decomposed into sub-problems, and these sub-problems may in turn be distributed across a society of problem-solving agents. Distribution may be due to the geographical nature of the problem, or may reflect the structure of the organisation within which the activities take place. However, one might anticipate some degree of coupling between sub-problems. Resources may be shared between agents, and relations may exist between activities. This may lead to agents making decisions that are locally consistent, but globally inconsistent. Therefore, one requires a mechanism that will attempt to maintain consistency between agents and detect (and possibly avoid) conflicts that occur between agents. A system that addresses this problem is described, i.e., the distributed constraint maintenance system.
{"title":"A constraint maintenance system for the distributed resource allocation problem","authors":"P. Prosser, C. Conway, Claude Muller","doi":"10.1049/ISE.1992.0007","DOIUrl":"https://doi.org/10.1049/ISE.1992.0007","url":null,"abstract":"The problem of allocating resources to activities may be decomposed into sub-problems, and these sub-problems may in turn be distributed across a society of problem-solving agents. Distribution may be due to the geographical nature of the problem, or may reflect the structure of the organisation within which the activities take place. However, one might anticipate some degree of coupling between sub-problems. Resources may be shared between agents, and relations may exist between activities. This may lead to agents making decisions that are locally consistent, but globally inconsistent. Therefore, one requires a mechanism that will attempt to maintain consistency between agents and detect (and possibly avoid) conflicts that occur between agents. A system that addresses this problem is described, i.e., the distributed constraint maintenance system.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"37 1","pages":"76-83"},"PeriodicalIF":0.0,"publicationDate":"1992-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75654038","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}
Our system, Easel, is a smart CAD package which attempts to analyse and tidy up sketches as a designer draws them. In this paper, we present an overview of the system, details of its user interface and the curve-fitting approach. The main part of the paper is concerned with automatic methods to infer and enforce geometric relations intended by the user. We also discuss our conclusions as regards performance.
{"title":"Applying constraints to enforce users' intentions in free-hand 2-D sketches","authors":"D. Jenkins, R. Martin","doi":"10.1049/ISE.1992.0004","DOIUrl":"https://doi.org/10.1049/ISE.1992.0004","url":null,"abstract":"Our system, Easel, is a smart CAD package which attempts to analyse and tidy up sketches as a designer draws them. In this paper, we present an overview of the system, details of its user interface and the curve-fitting approach. The main part of the paper is concerned with automatic methods to infer and enforce geometric relations intended by the user. We also discuss our conclusions as regards performance.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"12 1","pages":"31-49"},"PeriodicalIF":0.0,"publicationDate":"1992-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82035068","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}
Effective control strategies for robotic manipulators usually require the on-line computation of the robot dynamic model in real time. However, the complexity of the robot dynamic model makes this difficult to achieve in practice, and multiprocessor controller architectures appear attractive for real-time implementation inside the control servo loop. Furthermore, inevitable modelling errors, changing parameter values and disturbances can compromise controller stability and performance. In this paper, the performance of a neuro-model-based controller architecture is investigated. The neural network is used to adapt to unmodelled dynamics and parameter modelling errors. Simulation of the neuro-model-based control of a one-link robot demonstrates an improved performance over standard model-based control algorithm, in the presence of modelling errors and in the presence of disturbance and noise. >
{"title":"Performance of a neuro-model-based robot controller: adaptability and noise rejection","authors":"A. Poo, M. Ang, C. Teo, Q. Li","doi":"10.1049/ISE.1992.0005","DOIUrl":"https://doi.org/10.1049/ISE.1992.0005","url":null,"abstract":"Effective control strategies for robotic manipulators usually require the on-line computation of the robot dynamic model in real time. However, the complexity of the robot dynamic model makes this difficult to achieve in practice, and multiprocessor controller architectures appear attractive for real-time implementation inside the control servo loop. Furthermore, inevitable modelling errors, changing parameter values and disturbances can compromise controller stability and performance. In this paper, the performance of a neuro-model-based controller architecture is investigated. The neural network is used to adapt to unmodelled dynamics and parameter modelling errors. Simulation of the neuro-model-based control of a one-link robot demonstrates an improved performance over standard model-based control algorithm, in the presence of modelling errors and in the presence of disturbance and noise. >","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"65 1","pages":"50-62"},"PeriodicalIF":0.0,"publicationDate":"1992-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84388015","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}
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