A Hybrid Fuzzy Knowledge-Based Expert System and Genetic Algorithm for efficient selection and assignment of Material Handling Equipment

Material Handling (MH) is one of the key issues for every production site and has a great impact on manufacturing costs. The core concern in the design of a MH system is selecting the most suitable equipment for every MH operation and optimising them totally in order to attain an optimum solution. This paper presents a hybrid method for the selection and assignment of the most appropriate Material Handling Equipment (MHE). In the first phase, the system selects the most appropriate MHE types for every MH operation in a given application using a Fuzzy Knowledge-Based Expert System consisting of two sets of rules: Crisp Rules and Fuzzy Rules. In the second phase, a Genetic Algorithm (GA) searches throughout the feasible solution space, constituting of all possible combinations of the feasible equipment specified in the previous phase, in order to discover optimum solutions. The validity of the methodology developed in this paper is proved through the use of a real problem. Finally a comparison of the method with the other available publicised methods reveals the effectiveness of this hybrid approach.     

基于UML的适应调度知识系统开发

对复杂制造系统进行有效的适应调度，构建性能优良的知识系统是基础和核心。采用统一建模语言对适应调度知识系统的分析、设计和实现进行描述，建立了反映知识系统静态结构和动态行为的各种视图，使用对象约束语言对在系统设计中出现的类对象进行精确语义约束。使用这种建模技术减小了对领域知识专家的严重依赖，提高了知识系统开发的质量和效率。最后给出了应用实例验证该方法的可行性。     

A knowledge-based system for conveyor equipment selection

Conveyor equipment selection is a complex, and sometimes, tedious task since there are literally hundreds of equipment types and manufacturers to choose from. The expert system approach to conveyor selection provides advantages of unbiased decision making, greater availability, faster response, and reduced cost as compared to human experts. This paper discusses the development of a prototype expert system for industrial conveyor selection. The system, which was developed on Level V Object, provides the user with a list of conveyor solutions for their material handling needs along with a list of suppliers for the suggested conveyor devices. Conveyor types are selected on the basis of a suitability score, which is a measure of the fulfillment of the material handling requirements by the characteristics of the conveyor. The computation of the score is performed through the Weighted Evaluation Method, and the Expected Value Criterion for decision making under risk. The prototype system was successfully validated through two industrial case studies.     

ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system

This paper describes ROGET, a knowledge-based system that assists a domain expert with an important design task encountered during the early phases of expert-system construction. ROGET conducts a dialogue with the expert to acquire the expert system's conceptual structure, a representation of the kinds of domain-specific inferences that the consultant will perform and the facts that will support these inferences. ROGET guides this dialogue on the basis of a set of advice and evidence categories. These abstract categories are domain independent and can be employed to guide initial knowledge acquisition dialogues with experts for new applications. This paper discusses the nature of an expert system's conceptual structure and describes the organization and operation of the ROGET system that supports the acquisition of conceptual structures.     

自动泊车超声传感器系统设计

文中设计并实现了一种超声传感器系统,主要应用于在自动泊车系统中汽车对泊车环境的感知。根据超声回波的特点来设计硬件电路以及编制相应的软件程序,增强了超声传感器检测环境信息的灵敏度和数据的稳定性。该系统可提高汽车自主驾驶系统对泊车环境的感知能力,是汽车主动安全功能的关键部分,具有很好的应用价值。     

A novel fault-tolerant sensor system for sensor drift compensation

The conventional fault-tolerant sensor systems would fail when outputs from incorporated sensors are either noisy or drifting. This paper presents a novel real-time fault compensation method, which uses state estimation and compensation techniques, that the sensor system can perform robust measurements even when outputs from every incorporated sensor are noisy and drifting. In a simulation example, the proposed design can detect and correct the sensor errors (dc bias and drift) in real time. For the dc bias, the minimum detectable offset value is 0.1, which is the same as the standard deviation of the sensor noise. The compensated sensor output is biased at values smaller than 0.02. For the sensor drifts, the proposed method can compensate drifts for the change rate of drifts up to four times faster than that of the signal to be measured. The highest change rate of drifts, that can be compensated by this method, is determined by the standard deviation of the sensor noise.     

A knowledge-based expert system for technology acquisition in small and medium scale manufacturing organizations

Consistent and reliable decision making for technology acquisition in small and medium scale manufacturing organizations is vitally important since these firms are the backbone of national economies, both in developed and developing countries. Because of their flexibility, small and medium scale firms are successful in adopting new technologies. However, a careful analysis should be conducted in technology acquisition decisions. Since these decisions require special type of knowledge and expertise, expert system, as an important tool of computerized decision making, can overcome these multidimensional difficulties.

This paper proposes a knowledge-based approach making use of issues such as sales, processes, costs and general policies, in decision processes for technology acquisition by small and medium scale manufacturing organizations in the developing environments.     

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.     

A knowledge-based system for the selection of appropriate work measurement techniques

As a result of technological advancements and telecommunication innovations the nature of work performed by all classes of workers has undergone drastic changes. This is evidenced by the increased automation of long-run, short-cycled manual work and the increased application of word processors and microcomputers in the office. These changes demand new and more structured approaches to measuring and controlling work. One such approach is the application of a knowledge-based system to select the most appropriate work measurement technique for determining the expected or “standard” task completion time, depending on the nature of the task.     

A knowledge-based expert system for rolling-element bearing selection in mechanical engineering design

The selection of mechanical components to fulfil the technical requirement of mechanical systems relies on the domain knowledge and experience of the designer. The designer uses heuristic knowledge supported by manufacturers' catalogues to arrive at a final solution. This article describes the development of an expert system in bearing selection that attempts to mimic the design and selection activity of the designer. Whilst the expert system is able to advise on the bearing selection, one of the impediments is the inability of encoding of the imprecise language used in rules and facts.     

KBLS: A prototype knowledge-based system for the selection of lot-sizing models

Research in experimental simulation of multi-stage inventory systems shows that a poor choice of lot-sizing heuristics has a significant degree of cost penalty and schedule instability. A realistic approach to a multi-stage system is to choose a suitable technique for a certain special circumstance rather than trying for a single best heuristic covering all cases. To avoid serious cost penalties and high schedule instability caused by inferior techniques, knowledge-based system technology could help practitioners to make a sensible choice of heuristics. In this paper, we develop a prototype knowledge-based system whose aim is to provide an acceptable lot-size schedule in a limited time which would hopefully lead to a good master production schedule.     

The integration of an intelligent knowledge-based system into engineering software using the blackboard structure

Over recent years there has been an increase in the use of generic Computational Fluid Dynamics (CFD) software packages spread across various application fields. This has created the need for the integration of expertise into CFD software. Expertise can be integrated into CFD software in the form of an Intelligent Knowledge-Based System (IKBS). The advantages of integrating intelligence into generic engineering software are discussed with a special view to software engineering considerations. The software modelling cycle of a typical engineering problem is identified and the respective expertise and user control needed for each modelling phase is shown. The requirements of an IKBS for CFD software are discussed and compared to current practice. The blackboard software architecture is presented. This is shown to be appropriate for the integration of an IKBS into an engineering software package. This is demonstrated through the presentation of the prototype CFD software package FLOWES.     

集中供热热力站微机监控系统

冬季的采暖．对于北方城镇是必不可少的。集中供热能有效地利用现有能源．减少市区锅炉拥有量．改善环境污染，缓解交通有重要的作用。本文提出的集中供热微机监控系统为热力网的科学管理和可靠运行．充分发挥其最佳经济效益和社会效益提供了一个新途径。     

Knowledge acquisition using a neural network for a weather forecasting knowledge-based system

Neural network technology is experiencing rapid growth and is receiving considerable attention from almost every field of science and engineering. The attraction is due to the successful application of neural network techniques to several real world problems. Neural networks have not yet found widespread application in weather forecasting. The reason for this has been the difficulty in obtaining suitable weather forecasting data sets. In this paper we describe our experience in applying neural network techniques for acquiring the necessary knowledge to predict the weather conditions of Melbourne City and its suburbs in Australia during a 24 hour period beginning at 9 am local time. The accuracy of forecasts produced by a given forecasting procedure typically varies with factors such as geographical location, season, categories of weather, quality of input data, lead time and validity time. Two types of weather data sets assembled from the archives of the Australian Commonwealth Bureau of Meteorology are used for training the neural network. The results of the experiments are competitive and are discussed.     

基于DSP的相关法超声传感器系统设计

文中以车辆主动安全方面的应用为目标设计并实现了一种超声传感器系统。应用基于伪随机二进制序列(PRBS)的平方包络相关算法,以TMS320F2812和AD7656为核心设计了硬件电路和软件程序,增强了超声传感器的抗干扰能力和检测精度。该系统实时性和可靠性比较高,具有一定的推广价值。     

A selection framework of sensor combination feature subset for human motion phase segmentation

Motion phase plays an important role in the spatial–temporal parameters of human motion analysis. Multi-sensor fusion technology based on inertial sensors frees the monitoring of the human body phase from space constraints and improves the flexibility of the system. However, human phase segmentation methods usually rely on the determination of the positioning of the sensor and the number of sensors, it is difficult to artificially select the number and position of the sensors, especially when human motion phases are diverse. This paper proposes a selection framework for the sensor combination feature subset for motion phase segmentation, which combines feature selection algorithms with the subsequent classifiers, and determine the optimum combination of the sensor and the feature subset according to the performance of the trained model. Through the constraint and the sensor combination feature subset (SCFS), the filter method can select any number of sensors and control the size of the feature subset; the embedded method can select any number of sensors, but the size of the feature subset is determined by the classifier model. Experimental results show that the proposed framework can effectively select a specified number of sensors without human intervention, and the number of sensors has an impact on the recognition rate of the classifier within 1.5%. In addition, the filter method has good adaptability to a variety of classifiers, and the classifier prediction time can be controlled by setting the subset size of the feature; the embedded method can achieve a better phase segmentation effect than the filter method. For the application of motion phase segmentation, the proposed framework can reliably and quickly identify redundant sensors that provide effective support for reducing the complexity of the wearable sensor system and improving user comfort.     

Expert system problem selection: A domain characteristics approach

The first generation of commercial expert systems based on AI technology are now available in the market place. But in the available literature, one can find hardly any material on expert system problem selection. In this paper a number of popular and successful expert systems are analyzed. Domain-dependent and domain-independent problem characteristics have been identified, based on the analysis. To test our contention that these characteristics significantly contribute to the success of expert systems, a questionnaire survey involving a number of expert system developers was conducted. Based on this, a domain characteristic approach for expert system problem selection is presented.     

传感器网络中一种能量有效的簇头选择机制

针对LEACH算法存在簇头节点个数和位置分布不稳定的现象,在簇头节点的选择过程中,充分考虑簇头节点的残余能量因素,通过设定簇头的能量阈值防止低能量的节点成为簇头。改进的算法解决了LEACH簇头选择算法存在的簇头节点可能能量不足的问题,从而达到均衡网络能量消耗,延长网络寿命的目的。仿真实验采用改进算法的网络与采用LEACH、DCHS以及LEACH-H算法的网络的比较结果,说明改进算法具有更好的收敛性,实验数据表明该算法能最大限度地均衡利用网络的能量,延长无线传感器网络的寿命。