• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.89-101
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• 2023

In order to prevent damages caused by cyber-attacks on nations, businesses, and other entities, anomaly detection techniques for early detection of attackers have been consistently researched. Real-time reduction and false positive reduction are essential to promptly prevent external or internal intrusion attacks. In this study, we hypothesized that the type and frequency of attack events would influence the improvement of anomaly detection true positive rates and reduction of false positive rates. To validate this hypothesis, we utilized the 2015 login log dataset from the Los Alamos National Laboratory. Applying the preprocessed data to representative anomaly detection algorithms, we confirmed that using characteristics that simultaneously consider the type and frequency of attack events is highly effective in reducing false positives and execution time for anomaly detection.

• Journal of Korea Foundry Society
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• v.42 no.6
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• pp.369-376
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• 2022

The die-casting process is an important process for various industries, but there are limitations in the profitability and productivity of related companies due to the high defect rate. In order to overcome this, this study has developed die-casting fault detection modules based on industrial AI technologies. The developed module is constructed from three-stage models depending on the characteristics of the dataset. The first-stage model conducts fault detection based on supervised learning from the dataset without labels. The second-stage model realizes one-class classification based on semi-supervised learning, where the dataset only has production success labels. The third-stage model corresponds to fault detection based on supervised learning, where the dataset includes a small amount of production failure cases. The developed fault detection module exhibited outstanding performance with roughly 96% accuracy for actual process data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.

• The Journal of Bigdata
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• v.7 no.1
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• pp.113-124
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• 2022

Various methods using machine learning and big data have been applied to prevent failures in Data Centers. However, there are many limitations to referencing individual equipment-based performance indicators or to being practically utilized as an approach that does not consider the infrastructure operating environment. In this study, the performance indicators of individual infrastructure equipment are integrated monitoring and the performance indicators of various equipment are segmented and graded to make a single numerical value. Data pre-processing based on experience in infrastructure operation. And an ensemble of RRCF (Robust Random Cut Forest) analysis and Prophet analysis model led to reliable analysis results in detecting anomalies. A failure analysis system was implemented to facilitate the use of Data Center operators. It can provide a preemptive response to Data Center failures and an appropriate tuning time.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.4
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• pp.493-500
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• 2002

The operational data of diesel generator engine is two kinds of data. One is interactive the other is non interactive. We can find the fault information from interactive data measured for every sampling time when the changing rate, direction and status of data are investigated in comparition with those of normal status to diagnose the fault of combustion system. The various data values of combustion system for diesel engine are not proportional to load condition. The criterion to decide the level of data value is not absolute but relative to relational data. This study proposes to compose malfunction diagnosis engine using neural networks to decide that level of data value is out of normal status with the data collected from generator engine of the ship using the commercial data mining tool. This paper investigates the real ship's operational data of diesel generator engine and confirms usefulness of fault detecting through simulations for fault detecting.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.245-250
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• 2002

The operational data of diesel generator engine is two kind of discrete signal and analog signal. We can find the fault information from analog data measured for every sampling time if it is invested the changing rate or direction of data. This paper propose the Malfunction Diagnosis Engine(MDE) using the commercial data mining tool and show the data Process and fault finding method with the data collected from generator engine of the ship.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.298-303
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• 2011

최근 한반도의 잇따른 북한의 Jamming(교란신호)으로 인해 무선통신 기반 산업에 피해사례가 늘고 있다. 이에 국토해양부(위성항법중앙사무소)에서 운영중인 위성항법보정시스템(이하 "DGPS") 데이터 분석하였다. 그 결과 2010년도 발생한 재밍과 달리 2011년도에는 DGPS 기준국/감시국에서는 감지가 되지 않은 것으로 분석 되었으나, 피해 현황을 조사하여 이를 토대로 범국가적 대책방안(항행 백업시스템 개발, 유관기관과의 정보공유를 통한 감지 통합시스템 구축) 및 DGPS 운영 효율성 확보방안(감시국 신설, 실시간 감시프로그램 강화 등)에 대하여 제안하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.78-79
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• 2018

이중화 시스템은 가용성 및 신뢰성 향상을 위한 방안의 하나로 시스템의 고장으로 인한 임무중지나 성능 감소를 방지하기 위한 시스템이다. 이중화 구조 중 하나인 마스터-슬레이브 구조를 갖는 시스템에서는 슬레이브 모듈이 마스터 모듈의 상태를 모니터링하고 있다가 이상 감지 시 슬레이브 모듈에서 마스터 모듈로 전환되는 기능을 가지고 있다. 본 논문에서는 네트워크 시스템 구성 시 항시 시스템 fail 를 감지하고 무중단 데이터 전송을 수행하기 위한 네트워크 이중화 구성 방안에 대해 소개 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.342-345
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• 2019

본 연구는 사전에 수집한 공공 CCTV 및 비상벨 설치 현황 좌표를 지도/로컬 API에 추가하여 구현함으로써 경유지 설정을 통한 안전한 길안내 서비스를 사용자에게 제공하도록 하였다. 또한 사용자의 심박과 주변 소리센서 값을 실시간으로 수집하여 분석한다. 센서 측정을 통한 데이터는 실시간으로 애플리케이션에 전송되며, 이상 값이 발생할 경우 위기감지 모드에 진입한다. 위기감지 모드에 진입하면 애플리케이션을 통하여 강력한 경보음을 발동시키며 미리 지정한 지인들에게 긴급 SMS를 자동으로 전송함으로써 사용자의 안전을 보장한다.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.