• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.23-26
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• 2005

This paper describes the algorithm for deciding the status of the operating machines in the power plants. It is very important to decide whether the status of the operating machines is good or not in the industry to protect the accidents of machines and improve the operation efficiency of the plants. There are two steps to analyze the status of the running machines. First, we extract the features from the input original data. Second, we classify those features into normal/abnormal condition of the machines using the wavelet transform and the input RMS vector through the K-means algorithm. In this paper we developed the algorithm to detect the fault operation using the K-means method from the sound of the operating machines.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.575-580
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• 2024

Detecting the status of websites, normal or phishing, is necessary to defend against intelligent phishing attacks. We propose a machine learning-based classification to predict the status of websites. First, we collect information about 'URL', convert it into numerical data, and remove outliers. Second, we apply VIF(Variance Inflation Factors) to understand the correlation and independence between variables. Finally, we develop a phishing website detection model with machine learning-based classifications, which predicts website status. In the test datasets, Random Forest showed the best performance, with precision of 93.74%, recall of 92.26%, and accuracy of 93.14%. In the future, we expect to apply our model to detect various phishing crimes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.4
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• pp.137-147
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• 1998

The neural net application was tried to develop the technique for monitoring the health status of a steel truss bridge which was scaled down to 1/15 of the real bridge for the laboratory experiments. The damage scenarios were chosen as 7 cases. The dynamic behavior, which was changed due to the breakage of the members, of the bridge was investigated by finite element analysis. The bridge consists of single spam, and eight (8) main structural subsystems. The loading vehicle, which weighs as 100 kgf, was operated by the servo-motor controller. The accelerometers were bonded on the surface of 7 cross-beams to measure the dynamic behavior induced by the abnormal structural condition. Artificial neural network technique was used to determine the severity of the damage. At first, the neural net was learnt by the results of finite element analysis, and also, the maximum detection error was 3.65 percents. Another neural net was also learnt, and verified by the experimental results, and in this case, the maximum detection error was 1.05 percents. In future study, neural net is necessary to be learnt and verified by various data from the real bridge.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.677-679
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• 1999

This paper introduces a implementation of fault detection for national highway line 3. Fault detection system was installed and operated on national highway line 3, environmental elements caused by abnormal status or faults has often happened. Therefore, the function of fault detection system is to speedy notify fault site, cause as well as scale of fault to manager. Though the fault detection and diagnosis system has been imported in the field of process of water and electric power, it is just beginning step in the field of ITS(Intelligent Transportation Systems). In general, Maintenance system is performed the online/offline process of detection, diagnosis and measure. This paper is studied online detection process, which is realtime remote detection.

• Journal of Plant Biotechnology
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• v.45 no.4
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• pp.322-327
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• 2018

Post-translational modification of proteins regulates signaling cascades in eukaryotic system, including plants. Among these modifications, phosphorylation plays an important role in modulating the functional properties of proteins. Plants perceive environmental cues that directly affect the phosphorylation status of many target proteins. To determine the effect of environmentally induced phosphorylation in plants, in vivo methods must be developed. Various in vitro methods are available but, unlike in animals, there is no optimized methodology for detecting protein phosphorylation in planta. Therefore, in this study, a robust, and easy to handle Phos-Tag Mobility Shift Assay (PTMSA) is developed for the in vivo detection of protein phosphorylation in plants by empirical optimization of methods previously developed for animals. Initially, the detection of the phosphorylation status of target proteins using protocols directly adapted from animals failed. Therefore, we optimized the steps in the protocol, from protein migration to the transfer of proteins to PVDF membrane. Supplementing the electrophoresis running buffer with 5mM $NaHSO_3$ solved most of the problems in protein migration and transfer. The optimization of a fast and robust protocol that efficiently detects the phosphorylation status of plant proteins was successful. This protocol will be a valuable tool for plant scientists interested in the study of protein phosphorylation.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.357-366
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• 2013

Spectrum sensing, as a fundamental functionality of Cognitive Radio (CR), enables Secondary Users (SUs) to monitor the spectrum and detect spectrum holes that could be used. Recently, the security issues of Cognitive Radio Networks (CRNs) have attracted increasing research attention. As one of the attacks against CRNs, a Primary User Emulation (PUE) attack compromises the spectrum sensing of CR, where an attacker monopolizes the spectrum holes by impersonating the Primary User (PU) to prevent SUs from accessing the idle frequency bands. Energy detection is often used to sense the spectrum in CRNs, but the presence of PUE attack has not been considered. This study examined the effect of PUE attack on the performance of energy detection-based spectrum sensing technique. In the proposed protocol, the stationary helper nodes (HNs) are deployed in multiple stages and distributed over the coverage area of the PUs to deliver spectrum status information to the next stage of HNs and to SUs. On the other hand, the first stage of HNs is also responsible for inferring the existence of the PU based on the energy detection technique. In addition, this system provides the detection threshold under the constraints imposed on the probabilities of a miss detection and false alarm.

• Fire Science and Engineering
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• v.16 no.4
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• pp.28-32
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• 2002

The performance based design in fire detection system, the effect of high airflow and dilution of smoke produced in any fire situation serve to increase the response time of point-type smoke detectors. This study investigated the smoke density of ceiling, under the air stream and in normal status when fire type is smoldering fires. The result of study, smoke generated in the fire was swept away from nearby spot type smoke detector which failed to actuate because dispersed in diluted form around the room. The concept of performance based design in fire detection system of protected area influenced by high airflow provided the need of active fire detection system such as air sampling smoke detection system.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.6
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• pp.417-424
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• 2010

This paper proposes a new islanding detection method for a grid-connected photovoltaic system. It is based on the fact that the equivalent harmonic components vary according to the grid connection status. The advantage of the proposed method is the reduced Non-Detection Zone and fast detection time. Also it can have the robust detection capability against grid disturbances. The theoretic analysis using grid-harmonic modeling is performed and verified by test result using 32-bit high performance DSP processor.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.739-741
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• 1999

This paper describes the fault monitoring system for lift type tower parking facilities. This system consists of tower parking facility control panel and monitoring computer, and offers real-time monitoring of parking status and fault detection, and status data acquisition of tower parking system using graphic user interface.