A knowledge-based supplier intelligence retrieval system for outsource manufacturing

Knowledge management is to promote business success through a formal, structured initiative to improve the use of knowledge in an organization, in which an effective organizational memory information system plays an increasingly important role. Unlike the past, the performance of an enterprise now depends much on the performance and relationship of its customer–suppliers in the value chain. Good customer–supplier relationships are important for an organization to respond to dynamic and unpredictable changes. This paper describes a knowledge-based supplier selection and evaluation system, which is a case-based reasoning decision support system for outsourcing operations at Honeywell Consumer Products (Hong Kong) Limited in China. As a result, collaborative suppliers are identified quickly during the new product development process. By using the system, the cumulative performance of suppliers is constantly updated automatically according to past practice. This means that the knowledge of suppliers can be retained, categorized, retrieved and managed effectively.     

A spreadsheet-based fuzzy retrieval system

Under the downsizing boom and cost decrease in computer-related industries, both business and individual computer users have been greatly influenced. For instance, the users will come up against the chance to handle imprecise data in real life with their computers more than ever. Therefore, solutions for easily developing fuzzy systems are strongly needed. In response to this need, many studies have been done in the fuzzy database area because fuzzy retrieval is one of the suitable ways to deal with those kind of data. In this article, the development of a fuzzy retrieval system which can be used on a personal computer is described, especially from an industrial point of view. Additionally, the effects of fuzzy retrieval are proposed and its thinkable applications are introduced. © 1996 John Wiley & Sons, Inc.     

An intelligent knowledge-based system for robotic cell design

As research fields in AI accelerate and a greater number of experts are demanded by industry, Expert y tem play an important role in meeting the technological sophistication required in today's competitive world. Industries are demanding the assistance of human experts for solving complicated problems. However, there is a shortage of experts due to this demand. Expert Systems are rapidly becoming one of the major approaches to solve engineering and manufacturing problems. They have been implemented for several practical applications in many decision making problems. Expert Systems are helping major companies to diagnose processes in real time, schedule operations, maintain machinery and to design service and production facilities.

Robots are an integral part of today's manufacturing environment. New tasks are being defined for robots in order to meet the challenges of Flexible Manufacturing Systems. Along with this growth there is an increasing variety of robots to choose from. One of the major problems facing the potential robot user will be his/her choice of an optimum robot for a particular task. Various parameters should be considered and the user should choose an industrial robot whose characteristics satisfy the requirements of the intended task. This paper will present a solution to the problem of selecting an optimum robot by building a Knowledge-Based Expert System. It uses the knowledge base and the rules to determine an optimum robot.     

A knowledge-based intelligent decision support system for national defense budget planning

In this paper, we present a new framework for knowledge-based intelligent decision support systems for developing a national defense budget planning. The planning procedure for and architecture of the national defense budget in Taiwan are discussed in detail. In particular, the theories and techniques of intelligent decision support are used in the yearly practical budget planning process. Based on data in the financial database and knowledge in the knowledge base, we easily adjust the beforehand budget proposal. Furthermore, a knowledge-based intelligent decision support system has been implemented and it collects a series of rules extracted from national defense experts for successful reasoning. By using forward reasoning and knowledge rules, the system can automatically change and regenerate the national defense budget plan immediately. Finally, the empirical functions of the KIDSS system are also addressed.     

A fuzzy set theoretic framework for knowledge-based simulation

Complex systems such as an industrial enterprise operating in a physical, biological, socioeconomic environments generally have many ill defined or imprecisely known parameters which are typically approximated by exact numbers or represented with random variables. Fuzzy set theory is a promising alternative for explicitly treating these aspects of a system. This paper describes a fuzzy set theoretic formalism for knowledge-based simulations. A prototype system is being implemented in Mathematica.     

A repeating pattern based Query-by-Humming fuzzy system for polyphonic melody retrieval

Query-by-Humming involves retrieving music with a melody that matches the hummed query. An improved Query-by-Humming system for extracting pitch contour information based on a fuzzy inference model is introduced. In addition, an improved content-based music repeating pattern extraction model is introduced. Our bar-indexing method can extract the melody, identify repeating patterns and handle polyphonic MIDI files. To verify the effectiveness of the system, 15 volunteers recorded queries that were fed as input to the system and the longest common subsequence (LCS) was used to identify the most related top N matches. The system achieves 70% accuracy among the top 5 items retrieved.     

A knowledge-based intelligent framework for anterior cruciate ligament rehabilitation monitoring

This study presents an integration of knowledge-based system and intelligent methods to develop a recovery monitoring framework for post anterior cruciate ligament (ACL) injured/reconstructed subjects. The case based reasoning methodology has been combined with fuzzy clustering and intelligent classification techniques in order to develop a knowledge base and a learning model for identifying the recovery stage of ACL-reconstructed subjects and objectively monitoring the progress during the convalescence regimen. The system records kinematics and neuromuscular signals from lower limbs of healthy and ACL-reconstructed subjects using self adjusted non-invasive body-mounted wireless sensors. These bio-signals are synchronized and integrated, and a combined feature set is generated by performing data transformation using wavelet decomposition and feature reduction techniques. The knowledge base stores the subjects’ profiles, their recovery sessions’ data and problem/solution pairs for different activities monitored during the course of rehabilitation. Fuzzy clustering technique has been employed to form the initial groups of subjects at similar stage of recovery. In order to classify the recovery stage of subjects (i.e. retrieval of similar cases), adaptive neuro-fuzzy inference system (ANFIS), fuzzy unordered rule induction algorithm (FURIA) and support vector machine (SVM) have been applied and compared. The system has been successfully tested on a group of healthy and post-operated athletes for analyzing their performance in two activities (ambulation at various speeds and one leg balance testing) selected from the rehabilitation protocol. The case adaptation and retention is a semi-automatic process requiring input from the physiotherapists and physiatrists. This intelligent framework can be utilized by physiatrists, physiotherapists, sports trainers and clinicians for multiple purposes including maintaining athletes’ profile, monitoring progress of recovery, classifying recovery status, adapting recovery protocols and predicting/comparing athletes’ sports performance. Further, the knowledge base can easily be extended and enhanced for monitoring different types of sports activities.     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

A knowledge-based system for disk-drive diagnostics

A frame-based, backward-chaining knowledge-based system was developed to help the diagnostic process in the manufacture of computer high-end disk drives. The nature of the diagnostic and testing process is presented and the methodology used to develop the knowledge-based system is explained. The development and consultation environments are introduced and the final system is described. The improvements and results obtained with the utilization of this knowledge-based system in a factory environment are also included.     

A system for knowledge-based process planning

We describe the application of problem solving, knowledge based methods in creating process plans in manufacturing. The planner presented - called TOLTEC - is designed for experiential domains and bases its operation on the use of cases in a dynamic memory environment. We will describe the way TOLTEC creates process plans by utilizing previous experiences, dynamic clustering of its memories and dynamic constraint generation and by shifting its focus of attention to different features of the workpiece by using importance values. Also, we will present how TOLTEC learns by modifying its memories according to new experiences and how it helps bridge some of the gap between design and manufacturing by detecting design errors.The emphasis in this paper is more on the application aspects of our system and the examples presented will demonstrate the abilities of TOLTEC to design process plans, detect design errors, predict manufacturing errors, recover from planning errors, handle multiple branching solutions and improve its performance by utilizing learning techniques.     

A knowledge-based system for visualization design

Vista is a knowledge-based system that helps scientists design visualization techniques. It generates a technique for a given data set and lets users modify the design interactively using a compositional design methodology. To ensure the effectiveness of its designs, Vista uses many rules, mostly heuristic in nature, that were acquired through literature surveys and discussions with visualization experts. In general, Vista's design was based on research in graphical perception. It extends the design methodology of Automatic Presentation Tool (APT) (J.D. Mckinlay, 1986), a presentation tool for 2D graphics, to three dimensions. While Vista's primary function is to automatically generate an effective visualization technique design for a given data set, it also allows users to interactively modify this design and renders the resulting image using a variety of rendering algorithms     

A knowledge-based cooperative differential evolution for neural fuzzy inference systems

This study presents a knowledge-based cooperative differential evolution (KCoDE) for neural fuzzy inference systems to solve nonlinear control system problems. KCoDE decomposes the fuzzy system into subpopulations, and each individual within each subpopulation evolves separately. The KCoDE method uses five mutation strategies of differential evolution as the knowledge sources to generate a new population space to influence the population space. The exemplary individuals are selected from the population space to the belief space. The belief space in KCoDE is the information repository in which individuals can store their experiences to guide others. Finally, the experimental results show that the proposed KCoDE method better approximates the global optimal solution and has a faster convergence rate than the other methods.     

FILIP: A fuzzy intelligent information system with learning capabilities

FILIP (fuzzy intelligent learning information processing) system is designed with the goal to model human information processing. The issues addressed are uncertain knowledge representation and approximate reasoning based on fuzzy set theory, and knowledge acquisition by “being told” or by “learning from examples”. Concepts that can be “learned” by the system can be imprecise (fuzzy), or the knowledge can be incomplete. In the latter case, FILIP uses the concept of similarity to extrapolate the knowledge to cases that were not covered by examples provided by the user. Concepts are stored in the Knowledge Base and employed in intelligent query processing, based on flexible inference that supports approximate matches between the data in the database and the query.

The architecture of FILIP is discussed, the learning algorithm is described, and examples of the system's performance in the knowledge acquisition and querying modes, together with its explanatory capabilities are shown.     

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.     

A knowledge-based scheduling system for Emergency Departments

A knowledge-based reactive scheduling system is proposed to answer the requirements of Emergency Departments (EDs). The algorithm includes detailed patient priority, arrival time, flow time and doctor load. The main aim is to determine the patients who have higher priorities initially, and then minimize their waiting times. To achieve this aim, physicians and the other related workers can use an interactive system. In this study, we evaluated the existing system by comparing the proposed system. Also, reactive scheduling cases were evaluated for some items such as decreasing the number of doctors, changing durations and entering of an urgent patient to the system. All experiments were performed with proposed algorithm and right shift rescheduling approach.