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  • (Kidney cell)  (1)
  • (Spodopterafrugiperda)  (1)
  • Automatic workpiece classification system  (1)
  • Benchmark classification  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    FEBS Letters 259 (1990), S. 249-253 
    ISSN: 0014-5793
    Keywords: (Spodopterafrugiperda) ; Baculovirus ; Colony-stimulating factor ; Gene expression
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Biology , Chemistry and Pharmacology , Physics
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Biochimica et Biophysica Acta (BBA)/Molecular Cell Research 845 (1985), S. 94-100 
    ISSN: 0167-4889
    Keywords: (Kidney cell) ; Amino acid metabolism ; Glutamine uptake
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Biology , Chemistry and Pharmacology , Medicine , Physics
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    The international journal of advanced manufacturing technology 14 (1998), S. 481-494 
    ISSN: 1433-3015
    Keywords: Automatic workpiece classification system ; Classification benchmarks ; Full-data classification ; Lean classification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The usefulness 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 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.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    The international journal of advanced manufacturing technology 13 (1997), S. 637-648 
    ISSN: 1433-3015
    Keywords: Automated workpiece classification ; Benchmark classification ; Flexible classification method ; Group technology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract 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 judgment 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 on users' judgments 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 using 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.
    Type of Medium: Electronic Resource
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