• STIMA Bulletin
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• s.4
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• pp.32-42
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• 2006

• Journal of the Korea Institute of Building Construction
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• v.9 no.4
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• pp.147-155
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• 2009

Recently, the scale and technical complexity of agricultural production has been growing. Therefore, agricultural facilities are being gradually diversified, expanded, and made more complex. To furnish Korea's agricultural industry with international competitiveness, it is thus necessary to introduce new management techniques. The PCM (procurement-construction-maintenance) information management system for agricultural facilities is established by setting up its WBS (work breakdown structure). In this study, the WBS of a facility such as facility, space, element, works, and resources is analyzed. Following this analysts, a WBS of an agricultural facility that is appropriate for the PCM information system of an agricultural facility, is proposed by deriving it from actual WBS.

• Journal of the Korea Convergence Society
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• v.6 no.1
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• pp.49-55
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• 2015

In the FTA environment, it is necessary to comply with the rules of origin in order to receive duty-free benefits. To do this, they have to precede the Harmonized System(HS) tariff classification of the goods and understand thoroughly the basic principles that constitute the tariff schedule of HS classification. For the correct classification, they should understand exactly the product name of "Heading" about the items, "Legal Note" in the relevant "Section" or "Chapter" as well as provisions of the commentary. Therefore, this paper proposes to develop a HS classification services based on the performance system of knowledge convergence of field terms commonly used in various industries. In result, our services can provide users the conveniences which users first selects one of seven decision elements of the classification and perform the classification easily and accurately.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.792-798
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• 2007

In this paper, we propose a hierarchical fuzzy-rough classification method based on statistical information for maximizing the performance of pattern classification and reducing the number of rules without learning approaches such as neural network, genetic algorithm. In the proposed method, statistical information is used for extracting the partition intervals of antecedent fuzzy sets at each layer on hierarchical fuzzy-rough classification systems and rough sets are used for minimizing the number of fuzzy if-then rules which are associated with the partition intervals extracted by statistical information. To show the effectiveness of the proposed method, we compared the classification results(e.g. the classification accuracy and the number of rules) of the proposed with those of the conventional methods on the Fisher's IRIS data. From the experimental results, we can confirm the fact that the proposed method considers only statistical information of the given data is similar to the classification performance of the conventional methods.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.473-482
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• 2007

Network-based IDS(Intrusion Detection System) gathers network packet data and analyzes them into attack or normal. They raise alarm when possible intrusion happens. But they often output a large amount of low-level of incomplete alert information. Consequently, a large amount of incomplete alert information that can be unmanageable and also be mixed with false alerts can prevent intrusion response systems and security administrator from adequately understanding and analyzing the state of network security, and initiating appropriate response in a timely fashion. So it is important for the security administrator to reduce the redundancy of alerts, integrate and correlate security alerts, construct attack scenarios and present high-level aggregated information. False alarm rate is the ratio between the number of normal connections that are incorrectly misclassified as attacks and the total number of normal connections. In this paper we propose a false alarm classification model to reduce the false alarm rate using classification analysis of data mining techniques. The proposed model can classify the alarms from the intrusion detection systems into false alert or true attack. Our approach is useful to reduce false alerts and to improve the detection rate of network-based intrusion detection systems.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.432-438
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• 2023

Purpose: The purpose of this study is to enable users to conveniently report risks by automatically classifying risk categories in real time using AI for images reported in the life safety prevention service app. Method: Through a system consisting of a life safety prevention service platform, life safety prevention service app, AI model serving server and sftp server interconnected through the Internet, the reported life safety images are automatically classified in real time, and the AI model used at this time An AI learning algorithm for generation was also developed. Result: Images can be automatically classified by AI processing in real time, making it easier for reporters to report matters related to life safety.Conclusion: The AI image automatic classification system presented in this paper automatically classifies reported images in real time with a classification accuracy of over 90%, enabling reporters to easily report images related to life safety. It is necessary to develop faster and more accurate AI models and improve system processing capacity.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.336-340
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• 2008

Sentiment classification is a recent subdiscipline of text classification, which is concerned not with the topic but with opinion. In this paper, we present a Korean sentence and document classification system using effective sentiment features. Korean sentiment classification starts from constructing effective sentiment feature sets for positive and negative. The synonym information of a English word thesaurus is used to extract effective sentiment features and then the extracted English sentiment features are translated in Korean features by English-Korean dictionary. A sentence or a document is represented by using the extracted sentiment features and is classified and evaluated by SVM(Support Vector Machine).

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.655-660
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• 2003

In general, Support Vector Machine has a good performance in binary classification, but it has the limitation on multi-pattern classification. So, we proposed an Import Vector Voting model for two or more labels classification. This model applied kernel bagging strategy to Import Vector Machine by Zhu. The proposed model used a voting strategy which averaged optimal kernel function from many kernel functions. In experiments, not only binary but multi-pattern classification problems, our proposed Import Vector Voting model showed good performance for given machine learning data.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.484-489
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• 2003

본 논문에서는 역전파 알고리즘의 성능 개선을 위해 퍼지 논리 시스템을 이용한 학습률 자동 조정 방법을 제안한다. 제안된 방법은 목표값과 출력값의 차이에 대한 절대값이 $\varepsilon$ 보다 적거나 같으면 정확성으로 분류하고 크면 부정확성으로 분류한다. 정확성의 총 개수를 퍼지 논리 시스템에 적용하여 학습률과 모멘텀을 동적으로 조정한다. 제안된 방법을 XOR 문제와 숫자패턴 문제에 적용하여 실험한 결과, 기존의 역전파 알고리즘, 모멘텀 방식, Jacob의 delta-bar-delta 방식보다 성능이 개선됨을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.830-837
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• 2004

Neural networks-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the multilayer neural network-based nominal model output cross a Predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by nultilayer/ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.