Browsing by Author "Hsia, TC"

Now showing 1 - 4 of 4

A cost effective approach to establish benchmark for automatic workpiece classification systems
(2002-06-01)
Hsu, SH
;
Hsia, TC
;
Wu, MC
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The utility of an automatic workpiece classification system depends primarily on the extent to which its classification results are consistent with users' judgments. Thus to evaluate the effectiveness of an automatic classification system it is necessary to establish classification benchmarks based on users' judgments. Such benchmarks are typically established by having subjects perform pair comparisons of all workpieces in a set of sample workpieces. The result of such comparisons is called a full-data classification. However, when the number of sample workpieces is very large, such exhaustive comparisons become impractical. This paper proposes a more efficient method, called lean-data classification, in which data on some pair comparison are used to infer the complete pair comparison results. The proposed method has been verified by using a set of 36 sample workpieces. The results revealed that the method could produce a classification that was 78% consistent with the full-data classification while using only 40% of the total data.
A flexible classification method for evaluating the utility of automated workpiece classification system
(1997)
Hsu, SH
;
Hsia, TC
;
Wu, MC
;
交大名義發表
;
工業工程與管理學系
In group technology, workpieces are categorised into families according to their similarity in design or manufacturing, attributes. This categorisation can eliminate design duplication and facilitate the production of workpieces. Much effort has been focused on the development of automated workpiece classification systems. However it is difficult to evaluate the utility of such systems. The objective of this study was to develop a benchmark classification system based on global shape information for. use in evaluating the utility of workpiece classification systems. A classification system has a high level of utility if its classification scheme is consistent with users perceptual judgement of the similarity between workpiece shapes. Hence, in the proposed method, the consistency between a classification system and users' perceptual judgements is used as an index of the utility of the system. The proposed benchmark classification has two salient characteristics: 1. It is user-oriented, because it is based or? users' judgements concerning the similarity of the global shape of workpieces. 2. It is flexible, allowing users to adjust the criteria of similarity applied in the automated workpiece classification. The development of this classification consisted of three steps. 1. Gathering row data on global shape similarity from a group of representative users and modelling the data by fuzzy numbers. 2. Developing benchmark classification for various similarity criteria by rising fuzzy, clustering analysis. 3. Developing indices for evaluating the appropriate number of workpiece categories and homogeneity within each group. The applicability of the benchmark classification system in evaluating the utility of automated workpiece classification systems was examined.
A similarity inference method for reducing the cost of pair comparison
(2006-01-01)
Wu, MC
;
Wu, SC
;
Hsia, TC
;
Hsu, SH
;
工業工程與管理學系
Group technology must group similar parts into families. In classifying parts based on their global shapes, the similarity of parts has to be manually measured by performing pair comparison. The cost of exhaustively performing pair comparison is quite high when the number of parts to be grouped is large. This paper proposes interval intersection, a novel similarity inference method that effectively infers the pair-comparison data from a set of known data. Justified by empirical experiments, the proposed method outperforms the previous methods when 31% or more of data is known.
An efficient method for creating benchmark classifications for automatic workpiece classification systems
(1998)
Hsu, SH
;
Hsia, TC
;
Wu, MC
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The usefulness of an automatic workpiece classification system depends primarily on the extent to tt which its classification results are consistent with users judgements. Thus, to evaluate the effectiveness of an automatic, classification system it is necessary to establish classification benchmarks based on users' judgements. Such benchmarks al are typically established by having subjects perform pair comparisons of all workpieces iir a set of sample workpieces. The result of such comparisons is called a full-data classification. However; when the number of sample workpieces is very large, such exhaustive comparisons become impratical. This paper proposes a more efficient method, called lean classification, in which data on comparisons between the samples and a small number of typical workpieces are used to infer the complete classification results. The proposed,method has been verified by using a small set of 36 sample workpieces and by computer simulation with medium to large sets of 100 to 800 sample workpieces. The results reveal that the method could produce a classification that was 71% consistent with the full-data classification while using only 10% of the total data.