• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.299-310
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• 1994

Re-engineering tools can substantially increase software maintenance productivity and the quality of maintenance work. Re-engineering usually involves changing the form(e.g.changining objects names and definitions, restructuring process logic) of a program. In this paper, we describe the design and implementation of InMaC＋＋ that is a software tool oriented towards maintenance of C＋＋ object oriented programs. With InMaC＋＋, programms can be displayed and edited in two forms : as the code and as the diagram InMaC＋＋ also contains transformations in both directions, i,e. from code to diagram and from diagram to skeletons of code. Hence, it is suitable for re-engineering and maintenance of existing code. Specially designed browsers implement the graphical interface. InMaC＋＋ contains a database that is based on a simple but effective data model of InMaC＋＋ programs. The model contains only four object classes and three relations, which makes the tool small, and easy to implement and use. A simple query language allows browsing through the database.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.6
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• pp.94-101
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• 2007

In the Hard Disk Drive(HDD) production, the detect pattern or defective HDD set is important information to diagnosis of defective HDD set. This paper proposes a pattern recognition neural network for the defect distribution of HDD. In this paper, 5 characteristics are determined for the classification to six standard defect pattern classes. A multi-layer perceptron is trained for the pattern classification the inputs of which are 5 characteristic values and the 6 outputs are the nodes of standard patterns. The experiment with proposed neural network shows satisfactory results.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.83-90
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• 2020

Prototype selection offers the advantage of ensuring low learning time and storage space by selecting the minimum data representative of in-class partitions from the training data. This paper designs a new training data generation method using hyper-rectangles that can be applied to general classification algorithms. Hyper-rectangular regions do not contain different class data and divide the same class space. The median value of the data within a hyper-rectangle is selected as a prototype to form new training data, and the size of the hyper-rectangle is adjusted to reflect the data distribution in the class area. A set cover optimization algorithm is proposed to select the minimum prototype set that represents the whole training data. The proposed method reduces the time complexity that requires the polynomial time of the set cover optimization algorithm by using the greedy algorithm and the distance equation without multiplication. In experimented comparison with hyper-sphere prototype selections, the proposed method is superior in terms of prototype rate and generalization performance.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.679-686
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• 2005

Because EPON access network shares a medium and aggregates the traffic from EPON subscribers, scheduling media access control on EPON bandwidth allocation is very important. Furthermore DBA mechanism of EPON based on TDMA is out of specification and up to implementation. This paper deals with a DBA method to guarantee the QoS of the delay sensitive traffic on the base of best-effort service and delay priority queue management. The proposed method performs virtual scheduling algorithm for the integrated traffic. It uses the same MAC messages and tries to guarantee the QoS of higher priority traffic first with a simple DBA architecture. We evaluate the algorithm for traffic delay according to polling interval and traffic load of upstream and downstream. The results show that the proposed method can guarantee the QoS of the delay sensitive traffic with priority of the service classes.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.793-799
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• 2004

Many researches and validations for the complexity metrics of the object-oriented systems have been studied. Most of them are aimed for the measurement of the partial aspects of the systems, for example, the coupling between objects, the complexity of inheritance structures, the cohesion of methods, and so on. But the software practitioners want to measure the complexity of overall system, not partial. We studied the complexity of the overall structures of object-oriented systems by analyzing the class diagram of UML. The class diagram is composed of classes and their relations. There are three kinds of relations, association, generalization, and aggregation, which are making the structure of object-oriented systems to be difficult to understand. We proposed a heuristic metric to measure the complexity of object-oriented systems by putting together the three kinds of the relations. This metric will be helpful to the software developers for their designing tasks by evaluating the complexity of the structures of object-oriented system and redesigning tasks of the system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.1-8
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• 2016

In this paper, several problems functioning as the obstacles against software reuse were summarized. Among them, the issues dealt with in this paper include the effective method for constructing the library, the proper structure of the library, and the efficient retrieval technique. The knowledge-based approach and the information retrieval approach were integrated to construct and manage the library. The former is on the object- oriented model. Basically the object-oriented library is based on the classes and organized by inheritance. Because inheritance hierarchy is based on syntactical information, it dose not present the relationship of functionality. Using the information retrieval approach, the index file which characterizes the component and similarity among the components can be analyzed. Especially, we focused on the reusable library for the object-oriented programming environments.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2011-2025
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• 2021

In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.98-107
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• 2007

For high-dimensional pattern recognition, such as face classification, the small number of training samples leads to the Small Sample Size problem when the number of pattern samples is smaller than the number of dimensionality. Recently, various LDA-extensions have been developed, including LDA, PCA+LDA, and Direct-LDA, to address the problem. This paper proposes a method of improving the classification efficiency by increasing the number of (sub)-classes through pre-clustering a training set prior to the execution of Direct-LDA. In LDA (or Direct-LDA), since the number of classes of the training set puts a limit to the dimensionality to be reduced, it is increased to the number of sub-classes that is obtained through clustering so that the classification performance of LDA-extensions can be improved. In other words, the eigen space of the training set consists of the range space and the null space, and the dimensionality of the range space increases as the number of classes increases. Therefore, when constructing the transformation matrix, through minimizing the null space, the loss of discriminatve information resulted from this space can be minimized. Experimental results for the artificial data of X-OR samples as well as the bench mark face databases of AT&T and Yale demonstrate that the classification efficiency of the proposed method could be improved.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.37-50
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• 2003

In the paper, we describe the query extension techniques for spatiotemporal query functionalities on object-relational DBMS. The spatial objects in real world change the shapes over time. Spatiotemporal databases support to manage a temporal dimension as well as a spatial dimension for history of the objects. The proposed techniques can make conventional object-relational databases to support spatiotemporal databases system by the implementation and inheritance of abstract data types. We define and implement spatial and temporal classes as superclass. And, spatiotemporal classes inherits and extends the classes. The proposed extensions make it easy that conventional database systems not only are transformed into the spatiotemporal database systems, but also do not need to be changed to support spatiotemporal applications.

• Korean Journal of Cognitive Science
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• v.6 no.2
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• pp.27-45
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• 1995

In this paper, we suggest the "Fuzzy Behavior Knowledge Space(FBKS)" and explain how to utilize the FBKS when aggregating decisions of individual classifiers. The concept of "Behavior Knowledge Space(BKS)" is known to be the best method in the context that each classifier offers only one class label as its decision. However. the BKS does not considers measurement value of class label. Furthermore, it does not allow the heuristic knowledge of human experts to be embedded when combining multiple decisions. The FBKS eliminates such drawbacks of the BKS by adapting the fwzy concepts. Our method applies to the classification results that contain both class labels and associated measurement values. Experimental results confirm that the FBKS could be a very promising tool in pattern recognition areas.