Scheduling is one of the fields that are crucial in the manufacturing industry. A good schedule improves productivity and may help in making related production decision. This paper proposes a schedule generator framew...
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ISBN:
(纸本)9781467308946
Scheduling is one of the fields that are crucial in the manufacturing industry. A good schedule improves productivity and may help in making related production decision. This paper proposes a schedule generator framework for mining sequentialpatterns in the production schedules. The aim of this study is to produce knowledge pattern through a set of sequence rules. This framework consists of a few phases of sequentialpattern mining to extract certain patterns or knowledge embedded in schedules. The pattern or knowledge produced by this framework is illustrated in two ways that are comprised of a set of sequence rules and a set of frequent schedules.
Generation of data with an inherent sequential nature is the order of today's digital society. This kind of data is composed of discrete events that have either a temporal or spatial ordering and is generally obta...
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ISBN:
(纸本)9781467308762
Generation of data with an inherent sequential nature is the order of today's digital society. This kind of data is composed of discrete events that have either a temporal or spatial ordering and is generally obtained by sectors like telecommunication networks, E-Commerce, Internet servers and gene databases, medical domain to name a few. The ability to explore and exploit the sequential nature of the data for prediction leverages strategic decision making and problem solving. Symbolic sequence data consists of long sequence of ordered events with possible relationships among them. Symbolic sequence mining techniques aim at extracting frequent sequentialpatterns from huge collections of event sequences based on the user defined minimum support threshold. For a given set of symbols / events due to the possible repetition of events an infinite large number of sequences is possible and hence the task of extracting frequent sequences is complex. Whereas in closed sequentialpatterns, the set of sequentialpatterns automatically eliminates a lot of redundancy from the set of all frequent sequences and provides a concise set of patterns maintaining completeness. A novel approach is proposed for extracting closed sequentialpatterns which can be applied to fields that prefer complete and concise number patterns for analysis to aid the process of effective decision making.
In this paper, we investigate the problem of learning the decision functions for sequential data describing complex objects that are composed of subobjects. The decision function maps sequence of attribute values into...
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ISBN:
(纸本)3540476938
In this paper, we investigate the problem of learning the decision functions for sequential data describing complex objects that are composed of subobjects. The decision function maps sequence of attribute values into a relational structure, representing properties of the object described by the sequence. This relational structure is constructed in a way that allows us to answer questions from a given language. The decision function is constructed by means of rule system. The rules are learned incrementally in a dialog with an expert. We also present an algorithm that implements the rule system and we apply it to real life problems.
Acoustic sensors can provide real time information about moving targets. The acoustic information is typically processed sequentially, allowing the sequential probability ratio test (SPRT) to be used as the basis to s...
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Acoustic sensors can provide real time information about moving targets. The acoustic information is typically processed sequentially, allowing the sequential probability ratio test (SPRT) to be used as the basis to solve the target identification problem. The SPRT keeps gathering observations only as long as the statistical test has a value between the upper stopping boundary and the lower stopping boundary. When the test goes above the upper boundary or below the lower boundary, the system can make a decision. The desired false alarm error rate and the desired missed detection error rate determine the upper and lower stopping boundaries. We present extensions to the sequential probability ratio test to handle problems of dependence, contamination, and the unknown class. We also present results for using the SPRT for target identification using acoustic information. [ABSTRACT FROM AUTHOR]
A new approach to autonomous land vehicle (ALV) navigation by the person following is proposed. This approach is based on sequential pattern recognition and computer vision techniques, and maintenance of smoothness fo...
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A new approach to autonomous land vehicle (ALV) navigation by the person following is proposed. This approach is based on sequential pattern recognition and computer vision techniques, and maintenance of smoothness for indoor navigation is the main goal. The ALV is guided automatically to follow a person who walks in front of the vehicle. The vehicle can be used as an autonomous handcart, go-cart, buffet car, golf cart, weeder, etc. in various applications. sequential pattern recognition is used to design a classifier for making decisions about whether the person in front of the vehicle is walking straight or is too right or too left of the vehicle. Multiple images in a sequence are used as input to the system. Computer vision techniques are used to detect and locate the person in front of the vehicle. By sequential pattern recognition, the relation between the location of the person and that of the vehicle is categorized into three classes. Corresponding adjustments of the direction of the vehicle are computed to achieve smooth navigation. The approach is implemented on a real ALV, and successful and smooth navigation sessions confirm the feasibility of the approach. (C) 1999 John Wiley & Sons, Inc.
The general approach to matching two scenes by a digital computer is usually costly in computations. A match is determined by selecting the position of maximum cross correlation between the window and each possible sh...
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The general approach to matching two scenes by a digital computer is usually costly in computations. A match is determined by selecting the position of maximum cross correlation between the window and each possible shift position of the search region. A new approach which is logarithmically efficient is presented in this paper. Its logarithmic efficiency and computational savings will be demonstrated both theoretically and in practical examples. Experimental results are presented for matching an image region corrupted by noise and for matching images from optical and radar sensors. The significance of this approach is that scene matching can be accomplished by the use of a computer even in cases which are difficult for humans or standard correlation techniques, and can be accomplished with greatly reduced computations.
A sequential pattern recognition (SPR) procedure does not test all the features of a pattern at once. Instead, it selects a feature to be tested. After receiving the result of that test, the procedure either classifie...
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A sequential pattern recognition (SPR) procedure does not test all the features of a pattern at once. Instead, it selects a feature to be tested. After receiving the result of that test, the procedure either classifies the unknown pattern or selects another feature to be tasted, etc. Medical diagnosis is an example of SPR. In this paper the authors suggest that SPR be viewed as a one-person game played against nature (chance). Virtually all the powerful techniques developed for searching two-person, strictly competitive game trees can easily be incorporated either directly or by analogy into SPR procedures. In particular, one can incorporate the 'miniaverage backing-up procedure' and the 'gamma procedure,' which are the analogues of the minimax backing-up procedure and the alpha-beta procedure,' respectively. Some computer simulated experiments in character recognition ore presented. The results indicate that the approach is promising. [ABSTRACT FROM AUTHOR]
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