• 대한기계학회논문집
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• 제17권8호
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• pp.2061-2068
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• 1993

Condition monitoring technology is of great importance for the maintenance of complex machinery in view of its early monitoring of the abnormal condition and the protection against failure. Several methods have been used for the detection of failure of journal bearings, one of the main elements of mechanical system. The methods most frequently used are vibration and temperature monitoring, but these are unable to monitor the wear conditions exactly. In this study, an ultrasonic measument method, one of the non-destructive testing methods, was introduced as the monitoring technology. Furtermore a pattem recognition method was applied to analyze the ultrasonic signal. The monitoring system using the pattern recognition method is composed of digital signal processing units and uses Hamming net algorithm for the recognition of ultrasonic waves. From the journal bearing wear test, the occurrence of adhesive wear of the white metal in rubbing contact with the shaft was exactly detected by this system, and the wear status of the journal bearing was monitored by measuring the wear thickness.

• 한국산학기술학회논문지
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• 제19권10호
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• pp.603-609
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• 2018

수면 중 돌연사는 급성 심근경색 등의 이유로 노인 뿐 만 아니라 영아나 20~40대와 같은 비교적 젊은 층에서도 종종 발생하고 있다. 수면 중 돌연사는 미리 예측하기 어려우므로 이를 방지하기 위해서는 수면 모니터링이 필요하다. 본 논문에서는 별도의 센서 부착 없이도 수면 중 돌연사 감지를 할 수 있는 새로운 비디오 분석 방법을 제안한다. 제안하는 비디오 분석 방법에서는 호흡에 의한 미세 움직임을 감지하기 위해 모션 증폭 기법을 적용한다. 모션 증폭을 적용했는데도 프레임 간 차이가 거의 없는 경우, 모션이 존재하지 않아 돌연사 가능성이 있는 것으로 판단한다. 수면 중인 아기를 촬영한 비디오 두 편에 대해 모션 증폭을 적용한 결과, 호흡에 의한 미세 모션을 정확하게 감지하였고, 이는 수면 상태와 돌연사를 구분하는데 유용할 것으로 판단되었다. 제안하는 비디오 분석 방법은 신체에 센서 부착을 필요로 하지 않으므로 아기를 키우는 가정이나 독신 가정에서 편리하게 활용될 수 있을 것이다.

• Nuclear Engineering and Technology
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• 제30권1호
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• pp.8-16
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• 1998

In this work a method to detect the vibrational peak and to decide the vibrational mode of detected peak for core internal vibration monitoring system which is particularly concerned on the core support barrel (CSB) and fuel assemblies is developed. Flow induced vibration and aging process in the reactor internals cause unsoundness of the internal structure. In order to monitor the vibrational status of core internal, signals from the ex-core neutron detectors are transformed into frequency domain. By analyzing transformed frequency domain signal, an analyst can acquire the information on the vibrational characteristics of the structures, i.e., vibration frequencies of each component, vibrational level, modes of vibration, and the causes of the abnormal vibration, if any. This study is focused on the development of the automated monitoring system. Several methods are surveyed to define the peaks in power spectrum and fuzzy theory is used to automatic detection of the vibrational peaks. Fuzzy algorithm is adopted to define the modes of vibration using the peak values from fuzzy peak recognition, phase spectrum, and coherence spectrum.

• 산업공학
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• 제19권1호
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• pp.43-52
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• 2006

In order to make best use of NC machine tools with minimal labor costs, they need to be in operation 24 hours a day without being attended by human operators except for setup and tool changes. Thus, unattended machining is becoming a dream of every modern machine shop. However, without a proper mechanism for real-time monitoring of the machining processes, unattended machine could lead to a disaster. Investigated in this paper are ways to using PC camera as a real-time monitoring system for unattended NC milling operations. This study defined five machining states READY, NORMAL MACHINING, ABNORMAL MACHINING, COLLISION and END-OF-MACHINING and modeled them with DEVS (discrete event system) formalism. An image change detection algorithm has been developed to detect the table movements and a flame and smoke detection algorithm to detect unstable cutting process. Spindle on/off and cutting status could be successfully detected from the sound signals. Initial experimentation shows that the PC camera could be used as a reliable monitoring system for unattended NC machining.

• ETRI Journal
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• 제42권6호
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• pp.976-986
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• 2020

Internet of Things (IoT) is considered the future network to support wireless communications. To realize an IoT network, sufficient spectrum should be allocated for the rapidly increasing IoT devices. Through cognitive radio, unlicensed IoT devices exploit cooperative spectrum sensing (CSS) to opportunistically access a licensed spectrum without causing harmful interference to licensed primary users (PUs), thereby effectively improving the spectrum utilization. However, an open access cognitive IoT allows abnormal IoT devices to undermine the CSS process. Herein, we first establish a hard-combining attack model according to the malicious behavior of falsifying sensing data. Subsequently, we propose a weighted sequential hypothesis test (WSHT) to increase the PU detection accuracy and decrease the sampling number, which comprises the data transmission status-trust evaluation mechanism, sensing data availability, and sequential hypothesis test. Finally, simulation results show that when various attacks are encountered, the requirements of the WSHT are less than those of the conventional WSHT for a better detection performance.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 C
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• pp.1676-1678
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• 2001

In general, power demand is on an increasing trend as industries have made rapid strides. Power transformer is the most important equipment in substation for this reason. Transformer trobles go with blackout, expensive repair costs and huge economic losses. Therefore it is important to find the quick detection of incipient fault for the least losses. There have been gas, partial discharge, temperature, OLTC, fan and pump diagnosis for preventive techniques by present. Specially gas analysis has been adapted for a long time and proved as confident method. In this paper, we analysed the fault causes of used power transformer. The insulation faults was occupied 40% of inquired 152 faults from 1991 to 2000. This study presents the developed algorithm and expert system for finding abnormal status within transformer. We used the Element Expert tool developed Neuron DATA Inc.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.16-21
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• 2020

How accurately reproducible energy is delivered to the wafer in the process of making thin films using PE-CVD (Plasma enhanced chemical vapor deposition) during the semiconductor process. This is the most important technique, and most of the reaction on the wafer surface is made by thermal energy. In this study, we studied the method of monitoring the change of thermal energy transferred to the wafer surface by monitoring the temperature change according to the change of the thin film formed on the showerhead facing the wafer. Through this research, we could confirm the monitoring of wafer thin-film which is changed due to abnormal operation and accumulation of equipment, and we can expect improvement of semiconductor quality and yield through process reproducibility and equipment status by real-time monitoring of problem of deposition process equipment performance.

• 대기
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• 제29권4호
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• pp.501-509
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• 2019

Asian Dust is a meteorological phenomenon that sand particles are raised from the arid and semi-arid regions-Taklamakan Desert, Gobi Desert and Inner Mongolia in China-and transported by westerlies and deposited on the surface. Asian dust results in a negative effect on human health as well as environmental, social and economic aspects. For monitoring of Asian Dust, Korea Meteorological Administration operates 29 stations using a continuous ambient particulate monitor. Kim et al. (2016) developed an automatic quality check (AQC) algorithm for objective and systematic quality check of observed PM₁₀ concentration and evaluated AQC with results of a manual quality check (MQC). The results showed the AQC algorithm could detect abnormal observations efficiently but it also presented a large number of false alarms which result from valid error check. To complement the deficiency of AQC and to develop an AQC system which can be applied in real-time, AQC has been modulated. Based on the analysis of instrument status codes, valid error check process was revised and 6 status codes were further considered as normal. Also, time continuity check and spike check were modified so that posterior data was not referred at inspection time. Two-year observed PM₁₀ concentration data and corresponding MQC results were used to evaluate the modulated AQC compared to the original AQC algorithm. The results showed a false alarm ratio decreased from 0.44 to 0.09 and the accuracy and the probability of detection were conserved well in spite of the exclusion of posterior data at inspection time.

• 한국전자거래학회지
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• 제23권4호
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• pp.153-169
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• 2018

기존의 전자금융 이상거래 분석 및 탐지기술은 전자금융 업무시스템으로부터 발생된 대량의 전자금융 거래로그를 빅데이터 기반의 저장 공간으로 수집하고, 기존 고객의 거래패턴 프로 파일링 및 다양한 사고거래를 분석한 탐지룰을 이용하여 비정상적인 이상거래를 실시간 또는 준 실시간으로 탐지하고 있다. 하지만, 정작 피해금액 규모 및 사회적 파급효과가 큰 금융회사 내부자의 전자금융 부정접속 시도 및 내부 통제환경의 우회를 통한 전자금융 이용자의 중요정보 탈취와 같은 적극적인 분석은 제대로 이루어지지 못하고 있다. 이에 본 논문에서는 금융회사의 전자금융 보안프로그램에 대한 관리 실태를 분석하고, 관리상 취약점을 악용한 내부자의 보안통제 우회사고 가능성 도출한다. 또한, 이를 효율적으로 대응하기 위하여 기존 전자금융 이상거래탐지시스템에 더불어 내부자 위협모니터링과 연계한 포괄적인 전자금융 보안관리 환경을 제시하고자 한다.