Concept formation in design

This paper presents a computationally tractable view on where simple design concepts come from by proposing a paradigm for the formation of design concepts based on the emergence of patterns in the representation of designs. It is suggested that these design patterns form the basis of concepts. These design patterns once learned are then added to the repertoire of known patterns so that they do not need to be learned again. This approach uses the notion called the `loosely-wired brain'. This paper elaborates on this idea primarily through implemented examples drawn from the genetic engineering of evolutionary systems, and the qualitative representation of shapes and their multiple representations.     

Structure optimization of neural networks for evolutionary design optimization

We study the use of neural networks as approximate models for the fitness evaluation in evolutionary design optimization. To improve the quality of the neural network models, structure optimization of these networks is performed with respect to two different criteria: One is the commonly used approximation error with respect to all available data, and the other is the ability of the networks to learn different problems of a common class of problems fast and with high accuracy. Simulation results from turbine blade optimizations using the structurally optimized neural network models are presented to show that the performance of the models can be improved significantly through structure optimization.We would like to thank the BMBF, grant LOKI, number 01 IB 001 C, for their financial support of our research.     

Experiments with Incremental Concept Formation: UNIMEM

Learning by observation involves automatic creation of categories that summarize experience. In this paper we present UNIMEM, an artificial intelligence system that learns by observation. UNIMEM is a robust program that can be run on many domains with real-world problem characteristics such as uncertainty, incompleteness, and large numbers of examples. We give an overview of the program that illustrates several key elements, including the automatic creation of non-disjoint concept hierarchies that are evaluated over time. We then describe several experiments that we have carried out with UNIMEM, including tests on different domains (universities, Congressional voting records, and terrorist events) and an examination of the effect of varying UNIMEM's parameters on the resulting concept hierarchies. Finally we discuss future directions for our work with the program.     

基本形及其在概念交通工具创意草图设计中的运用

概念交通工具造型创新是形态创新的过程,创意草图是形态推演的常用手段。在了解基本形概念、形体组合方式的基础上,结合造型形态的设计原则,对基本形的绘制原则和方法进行分析。通过案例的设计应用,阐述了基本形的抽象性、发散性与概念交通工具造型设计具有适应性和可应用性,为概念交通工具创意草图设计提供了较为可行的方法。     

A framework to integrate design knowledge reuse and requirements management in engineering design

This paper presents a framework to integrate requirements management and design knowledge reuse. The research approach begins with a literature review in design reuse and requirements management to identify appropriate methods within each domain. A framework is proposed based on the identified requirements. The framework is then demonstrated using a case study example: vacuum pump design. Requirements are presented as a component of the integrated design knowledge framework. The proposed framework enables the application of requirements management as a dynamic process, including capture, analysis and recording of requirements. It takes account of the evolving requirements and the dynamic nature of the interaction between requirements and product structure through the various stages of product development.     

Acquisition of state transitions and concept formation in neural networks

Searching of state transitions is an important subject of problem solving in artificial intelligence, computer science, engineering and operations research. In artificial intelligence, a breadth-first search is optimal, with uniform cost, but it takes considerable time to obtain a solution. Neural networks process state transitions in parallel with learning ability. The authors have developed a search procedure for state transitions, that resembles a breadth-first search, using neural networks. First, the input pattern states are self-organized in the neural network, which consists of a Kohonen layer followed by a state-planning layer. The state-planning layer makes lateral connections between the cells of transitions. Then, the initial and the target states are given as a problem. The network shows an optimal transition pathway of states in the neuron firings. Next, the state-transition procedure is developed for the formation of a concept for action planning. Here, as the action planning, an integration between the symbols and the action pattern is carried out in the extended neural network.     

A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models

Genetic programming (GP) and artificial neural networks (ANNs) can be used in the development of surrogate models of complex systems. The purpose of this paper is to provide a comparative analysis of GP and ANNs for metamodeling of discrete-event simulation (DES) models. Three stochastic industrial systems are empirically studied: an automated material handling system (AMHS) in semiconductor manufacturing, an (s,S) inventory model and a serial production line. The results of the study show that GP provides greater accuracy in validation tests, demonstrating a better generalization capability than ANN. However, GP when compared to ANN requires more computation in metamodel development. Even given this increased computational requirement, the results presented indicate that GP is very competitive in metamodeling of DES models.     

Innovative seismic design optimization with reliability constraints

Performance-Based Design (PBD) methodologies is the contemporary trend in designing better and more economic earthquake-resistant structures where the main objective is to achieve more predictable and reliable levels of safety and operability against natural hazards. On the other hand, reliability-based optimization (RBO) methods directly account for the variability of the design parameters into the formulation of the optimization problem. The objective of this work is to incorporate PBD methodologies under seismic loading into the framework of RBO in conjunction with innovative tools for treating computational intensive problems of real-world structural systems. Two types of random variables are considered: Those which influence the level of seismic demand and those that affect the structural capacity. Reliability analysis is required for the assessment of the probabilistic constraints within the RBO formulation. The Monte Carlo Simulation (MCS) method is considered as the most reliable method for estimating the probabilities of exceedance or other statistical quantities albeit with excessive, in many cases, computational cost. First or Second Order Reliability Methods (FORM, SORM) constitute alternative approaches which require an explicit limit-state function. This type of limit-state function is not available for complex problems. In this study, in order to find the most efficient methodology for performing reliability analysis in conjunction with performance-based optimum design under seismic loading, a Neural Network approximation of the limit-state function is proposed and is combined with either MCS or with FORM approaches for handling the uncertainties. These two methodologies are applied in RBO problems with sizing and topology design variables resulting in two orders of magnitude reduction of the computational effort.     

Neural networks and their applications in component design data retrieval

Neural networks have gained increased importance in the past few years. One of the basic characteristics of neural networks is the property of associative memory. In this paper we study the possibility of using the ideas of neural networks and associative memory in the manufacturing domain, with specific reference to design data retrieval in group technology. A two-layer feed-forward perceptron with backpropagation is simulated on a Vax-8550 to train example parts. The complete scheme along with the simulation results are explained and future directions indicated.     

Machine grouping using sequence-based similarity coefficients and neural networks

Most neural network approaches to the cell formation problem do not use information on the sequence of operations on part types. They only use as input the binary part-machine incidence matrix. In this paper we investigate two sequence-based neural network approaches for cell formation. The objective function considered is the minimization of transportation costs (including both intracellular and intercellular movements). Constraints on the minimum and maximum number of machines per cell can be imposed. The problem is formulated mathematically and shown to be equivalent to a quadratic programming integer program that uses symmetric, sequence-based similarity coefficients between each pair of machines. Of the two energy-based neural network approaches investigated, namely Hopfield model and Potts Mean Field Annealing, the latter seems to give better and faster solutions, although not as good as a Tabu Search algorithm used for benchmarking.     

Extraction of quantitative and image information from flame images of steam boiler burners

Several types of detector, such as ultraviolet, infrared, visible light, differential pressure, flame rod, and others, are employed to detect fire flame in power generation plants. However, these flame detectors have some performance problems. This article describes the image-processing method of fire detection as well as neural network modeling. Nowadays, the image-processing technique is broadly applied in industrial fields. The neural network model has strong adaptability and learning capability, and is suitable for pattern classification. The Ulsan Steam Power Generation Plant in Korea was employed as the test field. If this technique can be implemented, boilers will be able to operate more economically and effectively. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

Patterns: from system design to software testing

Design patterns are used extensively in the design of software systems. Patterns codify effective solutions for recurring design problems and allow software engineers to reuse these solutions, tailoring them appropriately to their particular applications, rather than reinventing them from scratch. In this paper, we consider the following question: How can system designers and implementers test whether their systems, as implemented, are faithful to the requirements of the patterns used in their design? A key consideration underlying our work is that the testing approach should enable us, in testing whether a particular pattern P has been correctly implemented in different systems designed using P, to reuse the common parts of this effort rather than having to do it from scratch for each system. Thus in the approach we present, corresponding to each pattern P, there is a set of pattern test case templates (PTCTs). A PTCT codifies a reusable test case structure designed to identify defects associated with applications of P in all systems designed using P. Next we present a process using which, given a system designed using P, the system tester can generate a test suite from the PTCTs for P that can be used to test the particular system for bugs in the implementation of P in that system. This allows the tester to tailor the PTCTs for P to the needs of the particular system by specifying a set of specialization rules that are designed to reflect the scenarios in which the defects codified in this set of PTCTs are likely to manifest themselves in the particular system. We illustrate the approach using the Observer pattern.     

From critiquing to representational talkback: computer support for revealing features in design

Computational critiquing mechanisms support designers in refining a partial design solution in an alternating cycle of reflection and action. This paper argues that critiquing mechanisms can support designers not only in refining a partial solution, but also in gaining a better understanding of the problem. We further argue that different types of critiquing are possible, ranging from conventional rule-based messages to an implicit type of critiquing based on the notion of “representational talkback” – representations that can reveal to the user otherwise implicit features of a design. We support these claims with user studies of three types of design support systems: Kid, IAM-eMMa, and Art.     

Creative conceptual design: Extending the scope by infused design

Many methods that support human creativity by manual or computational means have been proposed in the past. They rely on the assumption that following a certain process of reasoning might lead to generating ideas considered creative. We start by defining creativity as a capability that enables the creation of systems that are patentable. We review the state-of-the-art of creative conceptual design and organize it with a framework. Subsequently, we present a method called infused design that guarantees the generation of design solutions by transforming systems and methods from remote disciplines. In many cases, these solutions would be deemed creative. Finding these systems and their transformations is done through a process based on the underlying discrete mathematical representation. This process is partially supported by a computer tool.We describe the method of infused design and illustrate its operation through part of the results achieved until now, including designing a new active torque amplifier system. We further demonstrate its benefits through other examples. We discuss the relation of infused design with other creativity or design methods and conclude with future developments of the method.“A well-directed imagination is the source of great deeds.”Chinese proverb     

Relating product specifications and performance data with a neural network model for design improvement

This paper presents research resulting in a neural network model relating product design specifications and performance testing results using data from a sanitary ware manufacturer. The main constraint of the work was the limited availability of actual data for neural network training and testing, a situation often found in real situations where a priori product knowledge is limited during the product design phase. The authors used two training techniques, the standard hold-back and the leave-k-out, for the neural network model to leverage the sparseness of the data. Neural network results are compared and contrasted to statistical models relating product design and performance. This work is an exploration of the value of neural network models to assist with interactive product design.     

浅谈广电运营支撑系统的系统管理的设计与实现

从2009年广电大力推广NGBOSS的概念起,到国务院常务会议提出加快三网融合融合,再到三网融合融合写进政府工作报告,广电行业这两年可谓是风起云涌。NGB和三网融合的推出是从根本上改变广电网络运营市场环境体制和业务结构,广电网络运营商将从单一的节目传输运营商转变为综合信息服务提供商,这是实质上的一个转变。我们将新形势下的广电称为新广电,在三网融合时间表已经推出,三网融合试点城市即将推出之际,建设能够为新广电提供运营支撑的下一代综合业务运营支撑系统及新BOSS迫在眉睫。本文首先介绍了运营支撑系统的基本概念,并对运营支撑系统的系统管理功能模块进行设计与实现。     

Neural network applications in business: A review and analysis of the literature (1988–1995)

A survey of journal articles on neural network business applications published between 1988 and 1995 indicates that an increasing amount of neural network research is being conducted for a diverse range of business activities. The classification of literature by (1) year of publication, (2) application area, (3) problem domain, (4) decision process phase, (5) level of management, (6) level of task interdependence, (7) means of development, (8) corporate/academic interaction in development, (9) technology integration, (10) comparative study, (11) major contribution, and (12) journal provides some insights into the trends in neural networks research. The implications for neural networks developers/researchers and suggestions on future research areas are discussed.     

Negotiation support systems and team negotiations: The coalition formation perspective

The use of software to support negotiations has captured the attention of academics and practitioners for some three decades and the research stream of negotiation support systems (NSS) has emerged. Over the years, many NSS have been developed and used in training and research but they have been rarely deployed in organizations. Our speculation is that much existing research is confined to dyadic (i.e., one-to-one) settings which may not adequately reflect the real-world situations in which teams, rather than individuals, often engage in negotiations. To address the gap, our current research aspires to conceptualize the NSS in supporting team negotiations and to theoretically examine their impact. Coalition formation has been a prevalent organizational phenomenon that constitutes important dynamics in any negotiating team; it will be conceptualized as the mechanism through which NSS impacts upon team negotiation outcomes in our paper. Globalization has rendered cross-cultural negotiations a commonplace; at the same time, culture serves as a most salient attribute in activating coalitions. In light of the above, cultural diversity is studied as the antecedent to coalition formation and moderates the influence of NSS on the extent of coalition formation. A set of research propositions, derived from a theoretical framework, are raised and their implications discussed.     

Self organising maps for the investigation of clinical data: A case study

The clinical process often involves comparisons of how one set of measurements is related to previous, similar, data and the use of this information to take decisions concerning possible courses of action, often with insufficient data to make meaningful calculations of probabilities. Self-organising maps are useful devices for data visualisation. To illustrate how visualisation with self-organising maps might be used in the clinical process, this paper describes the investigation of an osteoporosis data set using this technique. The data set had previously been used to show that backpropagation neural networks were capable of distinguishing between patients who had suffered a fracture, and those who had not using measured bone mineral density values; illustrating the power of these networks to model relationships in data. However, we had realised that this was somewhat of an academic exercise given that in reality a non-fracture case might be a fracture case waiting to happen. We felt it would be more productive to examine the data itself rather than model an imposed classification. As part of this investigation, the data set was examined using self-organising maps. From the results of the investigation, we conclude that it is possible to create a map, a compressed data representation, using BMD values which may then be partitioned into low and high fracture risk areas. Using such a map may be a useful screening mechanism for detecting people at risk of osteoporotic fracture.     

Neural net robot controller: Structure and stability proofs

A multilayer neural net (NN) controller for a general serial-link robot arm is developed. The structure of the NN controller is derived using a filtered error approach. It is argued that standard backpropagation tuning, when used for real-time closed-loop control, can yield unbounded NN weights if: (1) the net can not exactly reconstruct a certain required control function, (2) there are bounded unknown disturbances in the robot dynamics, or (3) the robot arm has more than one link (i.e. nonlinear case). On-line weight tuning algorithms including correction terms to backpropagation, plus an added robustifying signal, guarantee tracking as well as bounded weights. The correction terms involve a second-orderforward-propagated wave in the backprop network.