• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.113-118
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• 2019

A solar power plant is a facility that produces electricity. As the risk of fire and electric shock accidents is diversified, the risk of workers, surrounding people, and facilities is increased, preventing safety accidents and promptly responding to safety accidents Is emerging. In light of the necessity of such development, it is necessary to develop a solar power generation management system that can diagnose and maintain the problems of the power generation system in real time by developing technologies for collecting and analyzing the data produced by the solar power generation system As a result, the utilization rate and the maintenance cost can be reduced. In order to do this, it is necessary to accurately predict the solar power generation amount in the present state, to diagnose the abnormality of the current power generation state and to grasp the abnormal position, and to use the model considering economical efficiency when the abnormal position is grasped, And the time and other information should be provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1311-1319
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• 2019

Currently, the web environment is a commonly used area for sharing information and conducting business. It is becoming an attack point for external hacking targeting on personal information leakage or system failure. Conventional signature-based detection is used in cyber threat but signature-based detection has a limitation that it is difficult to detect the pattern when it is changed like polymorphism. In particular, injection attack is known to the most critical security risks based on web vulnerabilities and various variants are possible at any time. In this paper, we propose a novelty detection technique to detect abnormal state that deviates from the normal state on web-server log dataset(WSLD). The proposed method is a machine learning-based technique to detect a minor anomalous data that tends to be different from a large number of normal data after replacing strings in web-server log dataset with vectors using machine learning-based embedding algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.141-153
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• 2012

In the recent years, various researches on the use cases of the cloud computing service have been achieved for its standardization. Notwithstanding, we need more additory effort to refine the operating mechanisms on the cloud computing environment. In this paper, we suggest an operating mechanism on IaaS cloud computing environment that is related to the integrated security management system. By using CloudStack 2.2.4 toolkit, we have built a test-bed for IaaS cloud computing service i.e., SWU-IaaS cloud computing environment. Through operating this hierarchical SWU-IaaS cloud computing environment, we have derived the attributes and the methods of its components. Its scenarios can be described in case of both normal state and abnormal state. At the end, a special scenario has been described when it receives a security event from the integrated security management system.

• Smart Structures and Systems
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• v.30 no.6
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• The Journal of Korean Physical Therapy
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• v.13 no.2
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• pp.477-482
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• 2001

Until now, it has not been well known for Far-infrared(FIR) how to affect to the human body. We introduced and presumed the mechanism of FIR based on molecular biology in this study, as below. The human body is composed of proteins which get easily changed by a thermal factor (about 42 $^{\circ}$C over). FIR with low temperature can deeply penetrate on the human body composed things without troublesome, since FIR has effectively operated on the human body at low temperature (35-40 $^{\circ}$C). When FIR penetrated on the human body, it would inhibit the abnormal genes and cells expression, and then information of DNA and RNA would be reexpressed for arranging DNA and RNA abnormal state. As FIR's receptors in the body, it colud be presumed that N-glycosyl linkage of purine and deoxyribose, RNA splicing process, and heat shock protein.

• Tribology and Lubricants
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• v.27 no.3
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• pp.125-133
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• 2011

The purpose of this study is to develop an engine oil quality monitoring system to warn the abnormal condition of engine oil. To do this, first of all, it is needed a personal controller development to measure the capacitance of a pre-developed engine oil deterioration detection sensor integrated with an oil filter. To measure the capacitance of engine oil in the sensor, it is used the way measuring the electric charging time in a capacitor by impressing DC volt. This method has merits on cost and signal stability. The measured capacitance is compensated by comparing with the one measured by an impedance analyzer. Also, using the dielectric constant gained by an impedance analyzer, the calculating equation of the dielectric constant of engine oil related with the currently developed sensor is decided. Then, the deterioration degree of engine oil is estimated according to the change rate of dielectric constant between green oil and used oil. Finally, using this dielectric constant information together with engine oil temperature and pressure, the currently developed engine oil quality monitoring system is to tell the abnormal state of engine oil.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.185-190
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• 1995

Conventional alarm system has many difficulties in the operator's identifying the plant status during special situations such as design basis accidents. To solve the shortcomings, an on-line alarm annunciator system, called dynamic alarm console (DAC), was developed. In the DAC, a signal is generated as alarm by the use of an adaptive setpoint check strategy based on operating mode, and time delay technique is used not to generate nuisance alarms. After alarm generation, if activated alarm is a level precursor alarm or a consequencial alarm, it would be suppressed, and the residual alarms go through dynamic prioritization which provide the alarms with pertinent priorities to the current operating mode. Dynamic prioritization is achieved by going through the system- and mode-oriented prioritization. The DAC has the alarm hierarchical structure based on the physical and functional importance of alarms. Therefore the operator can perceive alarm impacts on the safety or performance of the plant with the alarm propagation from equipment level to plant functional level. In order to provide the operator with the most possible cause of the event and quick cognition of the plant status even without recognizing the individual alarms, reactor trip status tree (RTST) was developed. The DAC and the RTST have been simulated with on-line data obtained from the full-scope simulator for several abnormal cases. The results indicated that the system can provide the operator with useful and compact information fur the earlier termination and mitigation of an abnormal state.

• Journal of the Korean Institute of Gas
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• v.9 no.3 s.28
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• pp.38-43
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• 2005

There is monitored amount operation variables and controlled by operating conditions and loads at many facilities using gas also chemical plants. The process fault which can be indicated by operators, is occurred when the abnormal state was accumulated continuously owing to physical failure, external disturbance or human error. This is studied a Early Warning System which is to estimate process status by real-time monitoring operation variables and to early warning before it will be occurred process fault.

• Journal of Biosystems Engineering
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• v.28 no.3
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• pp.253-260
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• 2003

In this study, the feasibility of magnetic resonance techniques for nondestructive internal quality evaluation of Korean red ginseng was examined. Relaxation time constants were measured using various grades of red ginsengs. Solid state magnetic resonance imaging technique was applied to image dried red ginsengs which have low moisture contents (about 13%). A 7 tesla magnetic resonance imaging system operating at a proton resonant frequency of 300 ㎒ was used for acquiring MR images of dried Korean red ginseng. The comparison test of cross cut digital images and magnetic resonance images of heaven grade, good grade with cavity inside, and good grade with white part inside red ginseng suggested the feasibility of the internal quality evaluation of Korean red ginsengs using MRI techniques. A good grade red ginseng included abnormal tissues such as cavities or white parts inside was observed by the signal intensity of MR image based on magnetic resonance properties of proton nucleus. Analysis on an one dimensional profile of acquired MR image of Korean red ginseng showed easy discrimination of normal and abnormal tissues. MR techniques suggested ways to detect internal defects of red ginsengs effectively.