Yih Tng Chong

A novel neuro-cognitive approach to modeling traffic control and flow based on fuzzy neural techniques

In many developed and developing countries, efficient monitoring and controlling of the citys traffic have become major challenges. Conventional traffic light control methods, Preset Cycle Timing and Preset Cycle Timing with proximity sensors used today are neither sufficiently efficient nor effective to manage different traffic conditions. One solution is to employ a human operator. Unfortunately this method is expensive and error-prone due to lapse in concentration and other factors. Another alternative is to introduce an intelligent controller using fuzzy neural learning memory techniques, which have the capability to mimic human intelligence in controlling the frequency of traffic light changes at a junction. The performances of four suitable soft-computing architectures are investigated in this study as a possible platform to model and develop an intelligent traffic light control regime. These neural fuzzy learning structures construct memories that possess the intelligence and capabilities of a human operator in monitoring and managing the traffic at road intersections under different traffic scenarios. An open source traffic light simulator, Green Light District, is used to create and simulate different traffic conditions at (i) a simple traffic light intersection and (ii) a complex traffic light intersection. Traffic data generated by the simulator under the control of a human operator is then used as inputs for the training and testing of four fuzzy neural network architectures. The four architectures are Generic Self-organizing Fuzzy Neural Network (GenSoFNN), Pseudo Outer Product based Fuzzy Neural Network (POPFNN), Fuzzy Adaptive Learning Control Network (Falcon) and Multilayer Perceptrons (MLP). The performance of each of the neural network architectures was found to be promising from the simulation results derived for both simple and complex traffic light intersections. Performance was based on the mean classified rate, mean training time, mean number of rules, and standard deviation of the classified rate across the traffic conditions simulated. A technique from each of the architectures with the best results is subsequently selected for more in-depth study on its performance in a complex traffic light intersection. Although all the selected techniques from the four architectures suffered a decline in performance in the complex traffic light intersection; architectures such as GenSoFNN and Falcon continue to produce good results. The POPFNN architecture generated a large number of rules and the MLP architecture produced poor classified rates. This work has demonstrated that it is highly feasible to develop neuro-cognitive traffic control regime that can mimic the behaviors of a human operator.

Management and forecast of dynamic customer needs: An artificial immune and neural system approach

The twenty-first century is marked by fast evolution of customer tastes and needs. Research has shown that customer requirements could vary in the temporal space between product conceptualization and market introduction. In such cases, the products generated might not fit the consumer needs as companies originally expected. This paper advocates the proactive management and forecast of the dynamic customer requirements in bid to lower the inherent risk in developing products for fast shifting markets. The research identified the principles of artificial immune and neural systems as a solution to the problem. A customer requirements analysis and forecast (CRAF) system is defined in this paper to address the issue. The system aims to support product development functions with quantitative and qualitative customer requirements information, in the pursuit of generating products for near future markets. A case study is presented in this article to illustrate the functions of the system.

Human-centric product conceptualization using a design space framework

Designing products to fit human needs, preferences and capabilities is an essential key to competitiveness. In contested product markets, the management of user-related knowledge is therefore critical. Studies have shown that the identification and implementation of user requirements are significant issues for determining successful product development, especially during the conceptual design phase. User requirements represented in a single or limited level of abstraction is inadequate for effective incorporation into the conceptual design process. Such representation of user needs is argued here to be associated with issues such as the errors of problem framing, which is a cause of inadmissible, uncreative or sub-optimal designs. In this paper, a human-centric knowledge organization structure, Design Space Framework, is established to facilitate the consistent incorporation of user information into the length of the product conceptualization process. The role of this structure in human-centric design is illustrated in a case study.

A stakeholder-oriented innovative product conceptualization strategy based on fuzzy integrals

Knowledge, experiences and skills of corporate members promote new ideas into products, in the context of innovative design. This dynamism is especially important in the critical product conceptualization stage. In customer-oriented design paradigm, most methodologies focus on exploiting end-user requirements, instead of considering project stakeholder requirements in a holistic manner. Previous research in the area of innovative product conceptualization focused on such aspects as functional, cognitive, marketing and commercial perspectives. It involves the participations of customers, designers, engineers, marketers and business managers. Accordingly, a stakeholder-oriented innovative product concept development system (IPCDS) is established to integrate the bespoke four perspectives so as to evaluate product concept innovation comprehensively. To resolve the incomplete, ambiguous, uncertain and subjective nature of innovative design knowledge, the product platform represented in a design knowledge hierarchy (DKH) is generated using general sorting. Subsequently, an innovative design criteria matrix (DCM) constructed using repertory grids technique is rated with regard to specific design option. The ratings are then used as input features to the fuzzy integrals technique, for the purpose of innovative design. A case study on cellular phone design is used for system illustration and the results are discussed on the basis of product concept innovation.

A fuzzy c-means based hybrid evolutionary approach to the clustering of supply chain

This paper describes the work that adapts group technology and integrates it with fuzzy c-means, genetic algorithms and the tabu search to realize a fuzzy c-means based hybrid evolutionary approach to the clustering of supply chains. The proposed hybrid approach is able to organise supply chain units, transportation modes and work orders into different unit-transportation-work order families. It can determine the optimal clustering parameter, namely the number of clusters, c, and weighting exponent, m, dynamically, and is able to eliminate the necessity of pre-defining suitable values for these clustering parameters. A new fuzzy c-means validity index that takes into account inter-cluster transportation and group efficiency is formulated. It is employed to determine the promise level that estimates how good a set of clustering parameters is. The capability of the proposed hybrid approach is illustrated using three experiments and the comparative studies. The results show that the proposed hybrid approach is able to suggest suitable clustering parameters and near optimal supply chain clusters can be obtained readily.

Recent Patents of Product Design Technologies

Design phase, the front-end of a development process, is known to have the highest impact on the lifecycle cost of products. Presumably, it is considered necessary for product developers to legally protect the product design technologies that they have invented. This paper categorically reviews a group of selected patents, which were filed in the last twenty years, in this area of concerns. From the structural review, several patterns and trends were identified. The product development pipelines in corporations have visibly undergone technological revolution during the last two decades. Notably, design methodologies and product data management techniques were the prominent classes of patents observed. Furthermore, this work has revealed the recent focuses of the industry in the areas of collaborative design and environmental-friendly design.

A quality-time-cost-oriented strategy for product conceptualization

In general, product development companies aim to deliver products of optimal quality while incurring minimal cost in the shortest time possible. In this work, a quality-time-cost-oriented strategy (QTCOS) is proposed to facilitate product concept generation and selection. Firstly, general sorting is employed to elicit an initial product platform. The platform, constructed with a design space framework (DSF), serves as a base for generating a preliminary range of design options. Using the repertory grids elicitation method, designers contribute importance ratings with respect to a set of time and cost criteria for the range of design options. To account for trade-offs between cost and time related concerns, these ratings are employed to reduce the number of the derived design options, and thereby used as input features to a restricted coulomb energy (RCE) neural network. The RCE network function is applied to classify the set of design options into different patterns, i.e. cost-time-pairs. The classification results can subsequently serve as bases for the selection of preferred design options. A case study on wood golf club design is conducted to illustrate the proposed QTCOS.

AN INVESTIGATION INTO DYNAMIC CUSTOMER REQUIREMENT USING COMPUTATIONAL INTELLIGENCE

The twenty-first century is marked by fast evolution of customer tastes and needs. Research has shown that customer requirements could vary in the temporal space between product conceptualisation and market introduction. In markets characterized by fast changing consumer needs, products generated might often not fit the consumer needs as the companies have originally expected. This paper advocates the proactive management and analysis of the dynamic customer requirements in bid to lower the risk inherent in developing products for fast shifting markets. A customer requirements analysis and forecast (CRAF) system that can address the issue is introduced in this paper. Computational intelligence methodologies, viz. artificial immune system and artificial neural network, are found to be potential techniques in handling and analysing dynamic customer requirements. The investigation aims to support product development functions in the pursuit of generating products for near future markets.