• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.40-44
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• 2005

Applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. These classifiers are performed by batch way and it is not proper method for realtime intrusion detection system. We propose an incremental feature extraction and classification technique for realtime intrusion detection system. Applying proposed system to KDD CUP 99 data, experimental result shows that it has similar capability compared to batch way intrusion detection system.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.59-65
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• 2002

Recently, lots of researchers work focused on the intrusion detection system. Pattern matching technique is commonly used to detect the intrusion in the system, However, the method requires a lot of time to match between systems rule and inputted packet data. This paper proposes a new intrusion detection system based on the pattern matching technique. Proposed system reduces the required time for pattern matching by using classified system rule. The classified rule is implemented with a general tree for efficient pattern matching. Thereby, proposed system could perform network intrusion detection efficiently.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.59-65
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• 2002

Recently, lots of researchers work focused on the intrusion detection system. Pattern matching technique is commonly used to detect the intrusion in the system, However, the method requires a lot of time to match between systems rule and inputted packet data. This paper proposes a new intrusion detection system based on the pattern matching technique. Proposed system reduces the required time for pattern matching by using classified system rule. The classified rule is implemented with a general tree for efficient pattern matching. Thereby, proposed system could perform network intrusion detection efficiently.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.515-518
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• 2002

KM(Knowledge Management) is factor more paradigm of period than survival factor. Stewart, Sveiby etc., a scholar was Present to definition, knowledge classification and measurement method. KMS(Knowledge Management System) was made by scholar theory. But, it is hardly adapt to construction industry. Because it is have property that construction industry have one product, recieve-industry etc. Therefore, we must knowledge classification and measurement method that property of construction industry. So, we can effectively manage to knowledge of construction industry. And, knowledge of construction industry will evaluate according to property. Measure method of construction company will find through benchmarking that measure method of construction company is analyze to case.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.66-71
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• 2005

Using for the internet information is easy or difficult. The effort to obtain the useful information is developed the various technique such as search as well as the information repository, classification, processing and the utilization. Specially, such developments are remarkable to the Agent of various uses and the classification, conversion in processing techniques. The study introduces the classification service system of internet documents which is processing from the repository of internet information to the automatic classification and search service.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.431-435
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• 2013

Aroma has been used for healing for a long time. The healing effects depend on aroma used. We made gas sensor array system to classify aromas systematically. We used outputs of sensors as the input to IAFC neural network. Results show that the neural network successfully classified jasmine, orange, roman chamomile, and lavender into 4 classes, and classified without any error.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.45-49
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• 2013

기존 연구에서는 질의 응답 시스템에서 정답 유형을 분류하기 위해 패턴 매칭 방식이나 교사 학습(Supervised Learning)을 이용했다. 패턴 매칭 방식은 질의 분석을 통해 수동으로 패턴을 구축해야 한다. 교사 학습에서는 훈련 데이터 전체에 정답 유형이 태깅(Tagging)되어야 하며, 이를 위해서는 사용자의 질의에 정답 유형을 수동으로 태깅하는 작업이 많이 필요하다. 웹을 통해 정답 유형이 태깅되지 않은 대용량의 사용자 질의 말뭉치를 구할 수 있지만, 이 데이터에는 정답 유형이 태깅되어 있지 않다. 따라서, 대용량의 사용자 질의에 비례하여, 정답 유형을 수동으로 태깅하는 작업량이 증가한다. 앞서 언급한 두 가지 방법론에서, 정답 유형 분류를 위해 수작업이 많이 필요하다는 문제점을 해결하고자 본 논문에서는 일부 태깅된 훈련 데이터를 필요로 하는 반교사 학습(Semi-supervised Learning)에 기반한 정답 유형 분류를 제안한다. 이는 정답 유형 분류 작업에 필요한 노동력을 최소화함으로 대용량의 데이터를 통한 효율적 질의 응답 시스템 구축을 가능하게 한다.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.361-372
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• 2019

The Overseas Construction Engineering Information System (OVICE), which is operated by the Korea Institute of Construction Technology, uses the Overseas Construction Information Service classification system to service information required for overseas construction of domestic construction engineering companies. In this study, through the application of the recommendation system using collaborative filtering, identifying the relationship between the subjects for real users, verifying the adequacy of the overseas construction information classification system currently in service. Through this, I would like to propose an information service classification system that reflects actual user demand.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.1-8
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• 2019

Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.88-90
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• 2021

This paper introduces a method for extracting subtitles from lecture videos through a Korean morpheme analyzer and classifying video categories according to the extracted morpheme information. In some cases incorrect information is entered due to human error and reflected in the characteristics of the items, affecting the accuracy of the recommendation system. To prevent this, we generate a keyword table for each category using morpheme information extracted from pre-classified videos, and compare the similarity of morpheme in each category keyword table to classify categories of Lecture videos using the most similar keyword table. These human intervention reduction systems directly classify videos and aim to increase the accuracy of the system.