• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.729-736
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• 2017

In this study, the physics-based model and machine learning technique were used to conduct model-assisted probability of detection (MAPOD) experiments. The possibility of using in-service cracked parts was also investigated. Bolt hole shaped specimens with fatigue crack on the hole surface were inspected using eddy current inspection. Owing to MAPOD, the number of experimental factors decreased significantly. The uncertainty in the crack length measurement for in-service cracked parts was considered by the application of Monte Carlo simulation.

• The Journal of Sustainable Design and Educational Environment Research
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• v.18 no.1
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• pp.38-48
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• 2019

The purpose of this study is to present a more effective daily checklist than the formal routine check before the experiment to prevent accidents in the university laboratory. To do this, we reconstructed the current daily checklist and previous research data and conducted a second Delphi survey. As a result, there were four general safeties such as arranging the laboratory, three mechanical safeties such as abnormal condition of machine and tool tightening parts, three electric safeties such as prohibition of loading around the electric distribution panel, six chemical safeties such as handling and managing harmful factors, three items of fire safety such as fire extinguisher inspection, five cases of gas safety gas container inspection, one item of biological safety such as the state of hand sterilizer management and one other item, which were provided in the daily checklist as twenty six categories in total. According to the opinions of related experts, it is necessary to have an easy and simplified daily checklist for actual daily checkups.

• Convergence Security Journal
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• v.23 no.2
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• pp.55-66
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• 2023

Recently, there is an increasing movement to increase the value of AI learning data and to secure high-quality data based on previous research on AI learning data in all areas of society. Therefore, quality management is very important in construction projects to secure high-quality data. In this paper, quality management to secure high-quality data when building AI learning data and improvement plans for each construction process are presented. In particular, more than 80% of the data quality of unstructured data built for AI learning is determined during the construction process. In this paper, we performed quality inspection of image/video data. In addition, we identified inspection procedures and problem elements that occurred in the construction phases of acquisition, data cleaning, labeling, and models, and suggested ways to secure high-quality data by solving them. Through this, it is expected that it will be an alternative to overcome the quality deviation of data for research groups and operators participating in the construction of AI learning data.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• Computers and Concrete
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• v.14 no.6
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• pp.727-743
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• 2014

The management of existing concrete bridges has become a major social concern in many developed countries due to the large number of bridges exhibiting signs of significant deterioration. This problem has increased the demand for effective maintenance and renewal planning. In order to implement an appropriate management procedure for a structure, a wide array of corrective strategies must be evaluated with respect to not only the condition state of each defect but also safety, economy and sustainability. This paper describes a new performance evaluation system for existing concrete bridges. The system evaluates performance based on load carrying capability and durability from the results of a visual inspection and specification data, and describes the necessity of maintenance. It categorizes all girders and slabs as either unsafe, severe deterioration, moderate deterioration, mild deterioration, or safe. The technique employs an expert system with an appropriate knowledge base in the evaluation. A characteristic feature of the system is the use of neural networks to evaluate the performance and facilitate refinement of the knowledge base. The neural network proposed in the present study has the capability to prevent an inference process and knowledge base from becoming a black box. It is very important that the system is capable of detailing how the performance is calculated since the road network represents a huge investment. The effectiveness of the neural network and machine learning method is verified by comparing diagnostic results by bridge experts.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.6 no.1
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• pp.78-82
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• 2010

The feeder pipe of Main Primary Heat Transfer System in Wolsong Nuclear Power Plant was inspected by the Ultrasonic Phase Array technique in 2010. It is the first time to apply this method to the construction at Nuclear Power Plant in Korea. The time required for UT technique is less than RT method. The UT method doesn't need to evacuate personnel who works nearby inspecting area and doesn't need to wait developing of film. For these reasons, the UT method is the fastest method among the volumetric inspections. As a result of the examination, it became clear that main defect of the feeder pipe is the Lack of fusion in the welded area. Moreover, the rate of defect was reduced gradually as improvement of welder's skill. If welding machine has problem, the defect has tended to same pattern(occurred same position in the welding area) but these defects were founded without specific rules. For these reasons, the creation of defect is dependent on the skill of worker not on the automatic welding machine. This evaluation of defect signal and collecting data would be useful to further examination in ISI.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.5
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• pp.240-247
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• 2006

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.1-7
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• 2020

With the increase in cyber attacks, automated IDS using machine learning is being studied. According to recent research, the IDS using the recursive learning model shows high detection performance. However, the simple application of the recursive model may be difficult to reflect the associated session characteristics, as the overlapping session environment may degrade the performance. In this paper, we designed the session management module and applied it to LSTM (Long Short-Term Memory) recursive model. For the experiment, the CSE-CIC-IDS 2018 dataset is used and increased the normal session ratio to reduce the association of mal-session. The results show that the proposed model is able to maintain high detection performance even in the environment where session relevance is difficult to find.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.353-359
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• 1988

In this paper, the design and implementation of a multi-processor based die bonder machine for the semiconductor will be described. This is a final research results carried out for two years from June, 1986 to July, 1988. The mechanical system consists of three subsystems such as bonding head module, wafer feeding module, and lead frame feeding module. The overall control system consists of the following three subsystems each of which employs a 16 bit microprocessor MC 68000 : (i) supervisory control system, (ii) visual recognition / inspection system and (iii) the display system. Specifically, the supervisory control system supervises the whole sequence of die bonder machine, performs a self-diagnostics while it controls the bonding head module according to the prespecified bonding cycle. The vision system recognizes the die to inspect the die quality and deviation / orientation of a die with respect to a reference position, while it controls the wafer feeding module. Finally, the display system performs a character display, image display ans various error messages to communicate with operator. Lead frame feeding module is controlled by this subsystem. It is reported that the proposed control system were applied to an engineering sample and tested in real-time, and the results are sucessful as an engineering sample phase.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.3
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• pp.479-486
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• 2012

This paper presents a high-speed automatic micro-alignment system that is a part of an inspection machine for small-sized molded lenses of mobile phones, palm-top computers, and so on. This work was motivated by the shortcomings of existing highest-grade commercial machine. A simple tip/tilt/Z parallel mechanism is designed based on kinematic couplings, which is a 3-degree-of-freedom (3-DOF) moderate-cost alignment stage. It is used to automatically adjust the posture of each lens on the tray, which is impossible by the conventional instrument. Amplified piezoelectric actuators are used to ensure the accuracy and dynamic response. Forward kinematic analysis and simulation show that the parasitic motion is small enough compared to the actuator stroke. From the workspace analysis of the moving platform, it is clear that the output motion range satisfies the design requirements.