• Proceedings of the KIEE Conference
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• 1994.07b
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• pp.1298-1300
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• 1994

An ability of $NO_2$ gas detection has been investigated using dilithium phthalocyanine($Li_2Pc$) Langmuir-Blodgett (LB) films. It is a well-known gas sensitive material and has been manufactured under a surface pressure of 30mN/m. A status of deposited films was confirmed by UV-visible absorption spectrum, ellipsometry measurements and current-voltage characteristics. Gas-detection characteristics of the films were studied through an electrical conductivity, response time, recovery time, and reproducibility under 200 ppm of $NO_2$ gases.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.30-42
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• 2020

Unlike in previous generations of mobile technology, machine learning (ML)-based self-healing research trend are currently attracting attention to provide high-quality, effective, and low-cost 5G services that need to operate in the HetNets scenario where various wireless transmission technologies are added. Self-healing plays a vital role in detecting and mitigating the faults, and confirming that there is still room for improvement. We analyzed the research trend in self-healing framework and ML-based fault detection, fault diagnosis, and fault compensation. We propose that to ensure that self-healing is a proactive instead of being reactive, we have to design an ML-based self-healing framework and select a suitable ML algorithm for fault detection, diagnosis, and outage compensation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.95-95
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• 2010

Because of the environmental matters the importance of the city railroad is as time goes by increasing. The case of obstacle of the power equipment which supplies electric power to city railroad will occur social and economical enormous loss. Thus, I studied on the preventing method in advance which makes it possible for us to maintain facilities efficiently. The main check points of the power facilities are voltage, current, humidity, partial discharge, move current. These points are gathered by sensor and transmitting to data acquisition device. These data are used to check equipment status in real time. In this paper I described in brief test process and results of the detection system.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.314-316
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• 1993

In this paper, a method to detect and diagnose faults in Automated Manufacturing Systems(AMS) is proposed. In AMS, it is necessary to monitor the process-status. The detection and diagnosis of faults are often difficult in monitoring level with given passive data. We propose the model-based monitoring system for faults detection and diagnosis using Petri Nets to model AMS efficiently and easily. Simulation results show the validity of proposed method with example of Reverse Mill Process in Automated Mill Lines.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.8
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• pp.26-33
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• 1992

This paper describes a new phase-difference detection method and the associate process algorithm for calculating the mean value of phase difference detected and OVCXO control value and for monitoring and controlling the DP-PLL operation status to be used in the design of a high-frequency DP-PLL. Through the experiments of DP-PLL implemented with 16-bit processor, memories, pheriperals and OVCXO to eraluate the suggested method and algorithm, it is shown that a remarkable improvement in PLL function such as phase detection, and reference clock tracing capability, jitter absorbability and frequency stability compared with other existing DP-PLL synchronization device is achieved.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.821-826
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. Accordingly, this paper deals with Basic system and Online system. Basic system comprised of spindle rotational speed, feed rates, thrust, torque and flank wear measured tool microscope. Online system comprised of spindle rotational speed, feed rates, AE signal, flank wear area measured computer vision. On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Structural Monitoring and Maintenance
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• v.1 no.2
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• pp.213-230
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• 2014

A novel real-time actuator failure detection algorithm is developed in this paper. Actuator fails when the input to the structure is different from the commanded one. Previous research has shown that one error function can be formulated for each actuator through interaction matrix method. For output without noise, non-zero values in the actuator functions indicate the instant failure of the actuator regardless the working status of other actuators. In this paper, it is further demonstrated that the actuator's error function coefficients will be directly calculated from the healthy input of the examined actuator and all outputs. Hence, the need for structural information is no longer needed. This approach is termed as direct method. Experimental results from a NASA eight bay truss show the successful application of the direct method for isolating and identifying the real-time actuator failure. Further, it is shown that the developed method can be used for real-time sensor failure detection.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.142-147
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• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1204-1208
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• 2011

In this study, we proposed the system that calculates the algorithm with an accelerometer signal and detects the fall shock and it's direction. In order to gather the activity patterns of fall status and attach on the subject's body without consciousness, the device needs to be small. With this aim, it is attached on the right side of subject's waist. With roll and pitch angle which represent the activity of upper body, the fall situation is determined and classified into the posture pattern. The impact is calculated by the vector magnitude of accelerometer signal. And in the case of the elderly keep the same posture after fall, it can distinguish the situation whether they can stand by themselves or not. Our experimental results showed that 95% successful detection rate of fall activity with 10 subjects. For further improvement of our system, it is necessary to include tasks-oriented classifying algorithm to diverse fall conditions.