Dong-Hyun Baek

A Technology Valuation Model to Support Technology Transfer Negotiations

A web-based technology valuation system is developed with which interested users can make efficient and real-time evaluations of technologies. The development and commercialization of advanced technologies will depend increasingly on efficient technology transfer and technology trading systems. This requires the development of technology markets or exchanges and hence a reliable technology valuation methodology. This paper develops a methodology for an objective and impartial valuation of fully developed technologies. A web-based technology valuation system is developed with which interested users can make efficient and real-time evaluations of technologies.

Development and evaluation of a computer-aided system for analyzing human error in railway operations

As human error has been recognized as one of the major contributors to accidents in safety-critical systems, there has been a strong need for techniques that can analyze human error effectively. Although many techniques have been developed so far, much room for improvement remains. As human error analysis is a cognitively demanding and time-consuming task, it is particularly necessary to develop a computerized system supporting this task. This paper presents a computer-aided system for analyzing human error in railway operations, called Computer-Aided System for Human Error Analysis and Reduction (CAS-HEAR). It supports analysts to find multiple levels of error causes and their causal relations by using predefined links between contextual factors and causal factors as well as links between causal factors. In addition, it is based on a complete accident model; hence, it helps analysts to conduct a thorough analysis without missing any important part of human error analysis. A prototype of CAS-HEAR was evaluated by nine field investigators from six railway organizations in Korea. Its overall usefulness in human error analysis was confirmed, although development of its simplified version and some modification of the contextual factors and causal factors are required in order to ensure its practical use.

Co-evolutionary genetic algorithm for multi-machine scheduling: coping with high performance variability

Optimizing dispatching policy in a networked, multi-machine system is a formidable task for both field experts and operations researchers due to the problems stochastic and combinatorial nature. This paper proposes an innovative variation of co-evolutionary genetic algorithm (CGA) for acquiring the adaptive scheduling strategies in a complex multi-machine system. The task is to assign each machine an appropriate dispatching rule that is harmonious with the rules used in neighbouring machines. An ordinary co-evolutionary algorithm would not be successful due to the high variability (i.e. noisy causality) of system performance and the ripple effects among neighbouring populations. The computing time for large enough populations to avoid premature convergence would be prohibitive. We introduced the notion of derivative contribution feedback (DCF), in which an individual rule for a machine takes responsibility for the first-order change of the overall system performance according to its participation in decisions. The DCFCGA effectively suppressed premature convergence and produced dispatching rules for spatial adaptation that outperformed other heuristics. The required time for knowledge acquisition was also favourably compared with an efficient statistical method. The DCF-CGA method can be utilized in a wide variety of genetic algorithm application problems that have similar characteristics and difficulties.

A spatial rule adaptation procedure for reliable production control in a wafer fabrication system

In conventional approaches to scheduling problems, a single dispatching rule was applied to the all machines in a manufacturing system. However, since the condition of a machine generally differs from those of other machines in the context of overall system operation, it is reasonable to identify a suitable dispatching rule for each machine. This study proposes an adaptive procedure which produces a reliable dispatching rule for each machine. The dispatching rule consists of several criteria of which weights are adaptively determined by learning through repeated runs of simulation. A Taguchi experimental design for the simulation is used to find effective criteria weights with efficiency and robustness. For evaluation, the proposed method was applied to a scheduling problem in a semiconductor wafer fabrication system. The method resulted in reliable performances compared with those of traditional dispatching rules.

A classification method using a hybrid genetic algorithm combined with an adaptive procedure for the pool of ellipsoids

This paper presents a hybrid classification method that utilizes genetic algorithms (GAs) and adaptive operations of ellipsoidal regions for multidimensional pattern classification problems with continuous features. The classification method fits a finite number of the ellipsoidal regions to data pattern by using hybrid GAs, the combination of local improvement procedures and GAs. The local improvement method adaptively expands, rotates, shrinks, and/or moves the ellipsoids while each ellipsoid is separately handled with a fitness value assigned during the GA operations. A set of significant features for the ellipsoids are automatically determined in the hybrid GA procedure by introducing “don’t care” bits to encode the chromosomes. The performance of the method is evaluated on well-known data sets and a real field classification problem originated from a deflection yoke production line. The evaluation results show that the proposed method can exert superior performance to other classification methods such as k nearest neighbor, decision trees, or neural networks.

Generating interpretable fuzzy rules for adaptive job dispatching

Adaptive scheduling is an approach that selects and applies the most suitable strategy considering the current state of the system. The performance of an adaptive scheduling system relies on the effectiveness of the mapping knowledge between system states and the best rules in the states. This study proposes a new fuzzy adaptive scheduling method and an automated knowledge acquisition method to acquire and continuously update the required knowledge. In this method, the criteria for scheduling priority are selected to correspond to the performance measures of interest. The decisions are made by rules that reflect those criteria with appropriate weights that are determined according to the system states. A situated rule base for this mapping is built by an automated knowledge acquisition method based on system simulation. Distributed fuzzy sets are used for evaluating the criteria and recognizing the system states. The combined method is distinctive in its similarity to the way human schedulers accumulate and adjust their expertise: qualitatively establishing meaningful criteria and quantitatively optimizing the use of them. As a result, the developed rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop effective and robust rules.

Application of data mining for improving yield in wafer fabrication system

This paper presents a comprehensive and successful application of data mining methodologies to improve wafer yield in a semiconductor wafer fabrication system. To begin with, this paper applies a clustering method to automatically identify AUF (Area Uniform Failure) phenomenon from data instead of visual inspection that bad chips occurs in a specific area of wafer. Next, sequential pattern analysis and classification methods are applied to find out machines and parameters that are cause of low yield, respectively. Finally, this paper demonstrates an information system, Y2R-PLUS (Yield Rapid Ramp-up, Prediction, analysis & Up Support) that is developed in order to analyze wafer yield in a Korea semiconductor manufacturer.

A Framework for Computerized Human Error Analysis System - Focused on the Railway Industry

Human errors are now considered as the most significant source of accidents or incidents in large-scale systems such as aircraft, vessels, railway, and nuclear power plants. As 61% of the train accidents in Korea railway involving collisions, derailments and fires were caused by human errors, there is a strong need for a systematic research that can help to prevent human errors. Although domestic railway operating companies use a variety of methods for analyzing human errors, there is much room for improvement. Especially, because most of them are based on written papers, there is a definite need for a well-developed computerized system supporting human error analyzing tasks. The purpose of this study is to propose a framework for a computerized human error analysis system focused on the railway industry on the basis of human error analysis mechanism. The proposed framework consists of human error analysis (HEA) module, similar accident tracking (SAT) module, cause factor recommendation (CFR) module, cause factor management (CFM) module, and statistics (ST) module.

A Proposition of Accident Causation Model for the Analysis of Human Error Accidents in Railway Operations

ABSTRACT In accident analysis, it is essential to understand the causal pathways of the accident. Although numerous accident models have been developed to help analysts understand how and why an accident occurs, most of them do not include all elements related to the accident in various fields. Thus analysis of human error accidents in railway operations using these existing models may be possible, but inevitably incomplete. For a more thorough analysis of the accidents in railway operations, a more exhaustive model of accident causation is needed. This paper briefly reviews four recent accident causation models, and proposes a new model that overcomes the limitations of the existing models for the analysis of human error accidents in railway operations. In addition, the usefulness and comprehensiveness of the proposed model is briefly tested by explaining 12 railway accident cases with the model. The proposed accident causation model is expected to improve understanding of how and why an accident/incident occurs, and help prevent analysts from missing any important aspect of human error accidents in railway operations Keyword: Accident causation model, Accident analysis, Human error analysis, Railway accident

Non-normal CV Control Charts

The coefficient of variation (CV) of a population is defined as the ratio of the population standard deviation to the population mean. It is regarded as a measure of stability or uncertainty, and can indicate the relative dispersion of data in the population to the population mean. CV is a dimensionless measure of scatter or dispersion and is readily interpretable, as opposed to other commonly used measures such as standard deviation, mean absolute deviation. CV is often estimated by the ratio of the sample standard deviation to the sample mean, called the sample CV. In this paper, we propose statistical properties of CV for non-normal data and design of non-normal CV control charts based on non-normal distribution. The proposed control charts are effective methods to monitor the process variation