• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.262-266
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• 1993

The development of COPS(Computer aided Operation Planning System) needs data mapping paradigm which provides intelligent determonation of cutting conditions from the requirements of process planning side. We proposed the idea of multi-level mapping by the combination of heuristics of domain experts and mathematical abstraction of cutting condition and requirements. Mathematical mathods for the generalization of heuristics were constructed by multi-layer perceptron. DBMS for determination of cutting conditions was constructed by classification and combination of best fitted models. Triangular fuzzy number was used to process the uncertainties in heuristics of experts.

• Journal of Korean Library and Information Science Society
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• v.19
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• pp.35-57
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• 1992

This study is an attempt to provide some helpful data for the design and the implementation of the expert system for the book-classification based on the analysis of various cases of the classification-expert system models. Following the introduction, the concepts and some features of an expert system were overviewed in the second chapter, on the basis of which the following concrete cases were introduced and analyzed in the third chapter : (1) ACN System for NC, (2) Expert System for NDC, (3) Expert System for UDC, (4) Herba Medica System, (5) Expert System for IPC, (6) Stratcyclode Project, (7) Expert System for Classification of INIS Database, (8) AutoBC System, and etc. In the conclusion, for the development of the classification-expert system, it was turned out that constructing a new system by using an AI language such as Prolog or LISP is more desirable than employing any one of expert system shells. Together it is necessary for the following requirements to be met : (1) The subject concept of a document elicited should be accurate. (2) Not only a domain knowledge but also the knowledge covering all the subjects should be represented in the knowledge-bases. (3) The knowledge-bases should be organized in such a way that the characteristics of the knowledge about classification should be well defined. (4) rule-base consisting of accurate rules about classification should be made. (5) It should be possible for classification code wanted to be generated immediately.

• Journal of National Security and Military Science
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• s.1
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• pp.191-229
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• 2003

Multi-sensor data fusion techniques combine evidences from multiple sensors in order to get more accurate and efficient meaningful information through several process levels that may not be possible from a single sensor alone. One of the most important parts in the data fusion system is the identification fusion, and it can be categorized into physical models, parametric classification and cognitive-based models, and parametric classification technique is usually used in multi-sensor data fusion system by its characteristic. In this paper, we propose a novel heuristic identification fusion method in which we adopt desirable properties from not only parametric classification technique but also cognitive-based models in order to meet the realtime processing requirements.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.179-182
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• 2004

The process of categorizing packets into 'class' in an Internet router is called packet classification. All packets with same class obey predefined rule specified in routing tables. Performing classification in real time on an arbitrary number of fields is a very challenge task. In this paper, we present a new algorithm named EnBiT-PC (EnBiT Packet Classification). and evaluate its performance against real classifiers in use today. We compare with previous algorithms, and found out that EnBiT-PC classify packets very efficiently and has relatively small storage requirements.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.288-295
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• 1998

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity theshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that (1) some tolerant objects are required to be included in the same class as many as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grounded into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approxi ation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification. problem and compare its classification performance and learning time with those of the feed forward neural network's back propagation algorithm.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2005.11a
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• pp.57-61
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• 2005

The objectives of this study are to illustrate an alternative housing type responding to the social requirements and customers' needs, to suggest the improvement plan for the related laws and regulations through survey of the actual condition for housing buildings such multi have been raised in classification due to that the existing law systems including the current use classification of residential buildings have been defined unclearly. Especially, various social problems have yielded in line with emergence of the housing type which is not legally classified as residential however used for the living purpose practically. Current zoning planning and related law system have rigidity. So, It is required to introduce a flexible classification system which protects the residential environment based on the housing purpose, function and habitability and provides correspondence between residence and ownership/management method. The legal classification system should be revised in a way that the housing use classification corresponds with the zoning system through breakdown of the use classification system

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.160-170
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• 2018

This paper is an exploratory study on the success factors of Defence Quality Management System (DQMS) which is the certification system granted by the military for improving the quality of munitions. DQMS is established by adding military requirements to the ISO standard, thus, we especially focus on the additional requirements to figure out success key factors of DQMS certification. The 51 additional requirements of Korean Defense Specification (KDS) are empirically investigated from 67 companies that acquired DQMS certification. Firstly, we conduct an independent t-tests on 51 additional requirements of KDS 0050-900-3 to determine if there is a difference between an easily certified company and a hard-to-certify company, and obtain 8 requirements such as 'Internal propagation of performance', 'Preparation of documented work instructions', 'Work instructions in the workplace', 'Documentation of equipment management', 'Inventory management', 'Packaging and identification', 'Guarantee of access to internal audit result for customers', 'Notification to the customer for improper product.' Secondly, we carry out an factor analysis to the 51 additional requirements for classification, and figure out that 4 requirements among the 8 requirements above mentioned are grouped together in the same factor. The 4 requirements are 'Preparation of documented work instructions', 'Work instructions in the workplace', 'Packaging and identification', and 'Guarantee of access to internal audit result for customers.' The result of this paper will provide useful information to the company preparing for DQMS.

• Journal of the Society of Disaster Information
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• v.14 no.1
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• pp.28-35
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• 2018

The following research has focused and implemented on designing a system that classifies the severity of mass casualty situations across both normal and disaster levels. The system's algorithm has implemented requirements such as accuracy as well as user convenience. The developed e-Triage System has applied various severity classification algorithms implemented from IoT concepts. In order to overcome flaws of currently used severity classification systems, the e-Triage System used electronic elements including the NFC module. By using the mobile application's severity classification algorithm the system demonstrated quick and accurate assessment of patient. Four different LED lamps visualized the severity classification results and RTS scores were portrayed through FND(Flexible Numeric Display) after a two wave classification.