• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.531-538
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• 2005

This paper proposes an expert system which is able to enhance the accuracy and productivity by determining the test strategy based on heuristic rules for test of the hybrid electronic circuit board producted massively in production line. The test heuristic rules are obtained from test system designer, test experts and experimental results. The guarding method separating the tested device with circumference circuit of the device is adopted to enhance the accuracy of measurements in the test of analog devices. This guarding method can reduce the error occurring due to the voltage drop in both the signal input line and the measuring line by utilizing heuristic rules considering the device impedance and the parallel impedance. Also, PSA(Parallel Signature Analysis) technique Is applied for test of the digital devices and circuits. In the PSA technique, the real-time test of the high integrated device is possible by minimizing the test time forcing n bit output stream from the tested device to LFSR continuously. It is implemented in Visual C++ computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the interface with the electronic circuit database and the hardware direct control. Finally, the effectiveness of the builded expert system is proved by simulating the several faults occurring in the mounting process the electronic devices to the surface of PCB for a typical hybrid electronic board and by identifying the results.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.4
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• pp.517-532
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• 2021

Safe geological disposal of spent nuclear fuel (SNF) requires knowledge of the deep hydrochemical characteristics of the repository site. Here, we conducted a set of deep hydrochemical investigations using a 750-m borehole drilled in a model granite system in Wonju, South Korea. A closed investigation system consisting of a double-packer, Waterra pump, flow cell, and water-quality measurement unit was used for in situ water quality measurements and subsequent groundwater sampling. We managed the drilling water labeled with a fluorescein dye using a recycling system that reuses the water discharged from the borehole. We selected the test depths based on the dye concentrations, outflow water quality parameters, borehole logging, and visual inspection of the rock cores. The groundwater pumped up to the surface flowed into the flow cell, where the in situ water quality parameters were measured, and it was then collected for further laboratory measurements. Atmospheric contact was minimized during the entire process. Before hydrochemical measurements and sample collection, pumping was performed to purge the remnant drilling water. This study on a model borehole can serve as a reference for the future development of deep hydrochemical investigation procedures and techniques for siting processes of SNF repositories.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1165-1172
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• 2006

The rapid progress of the software has enabled individuals to copy and remark digital contents, which was only done by professionals. As a solution for the problems, contents producer needs to have certification and inspection of its contents and hold the proprietary right. A fragile watermarking method is able to detect the distortion and damage of watermarked image, but the watermark is also fragile on standardized image compression. That is the problem of fragile watermarking technique. We propose semi-fragile watermarking technique that is robust in the image processing such as JPEG compression used on computer, but it is fragile on the addition of noise and other attacks. In the proposed method, we can generate the watermarks of an image from the relationship between two block coefficients, also the generated watermark is inserted into insensible part of HVS(Human Visual System) after processing DCT(Discrete Cosine Transform) and using quantization. As a result, on the spatial domain, high detection of distortion has been possible even in slight changes, and could detect the corrupted blocks on image. Therefore, it is simple to judge the pixels at which some location has been changed in the space.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.19-26
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• 2020

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.16 no.1
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• pp.19-22
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• 2005

Background and Objectives : While considerable progress has been made in enhancing the quality of laryngoscopy and image processing, the evaluation of laryngeal erythema is still based on the clinician's judgement. The purpose of this study is to quantitatively measure the degree of erythema and to examine the relationship with clinical grading. Materials and Methods : Color images of larynx from 100 subjects were captured from video-documented examinations of laryngoscopy. The amount of erythema within the digitized larynx image was quantified using software developed and was compared with a grading system (0 to 3 scale) based on visual inspection by 4 experienced clinicians. The results were compared by deriving Kappa, Kendall and Spearman statistic. Results : There was high intra-observer(R=0.402-0.755) and inter-observer correlation (R=0.789). Among parameters, the red composite value had most remarkable agreement with clinical grading(R=0.827). Conclusion : The result suggest that the computer based analysis of laryngeal erythema can provide quantiative data on degree of erythema and the basis for further development of an expert system.

• Journal of Sasang Constitutional Medicine
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• v.17 no.2
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• pp.46-51
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• 2005

1. Objectives The purpose of this study is to objectify the diagnosis of Sasang Constitution. Using the methods of this study, it will improve to classificate Sasang Constitution. 2. Methods 1) Automatic feature extraction of human frontal faces for Sasang Constitution classification. 2) Color feature extraction of human frontal faces (1)Erosion filtering (skin-white, the other-black) (2) Median median 3. Results and Conclusions Observing a person's shape has been the major method for Sasang Constitution classification, which usually has been dependent upon doctor's intuition as of these days. We are developing an automatic system which provides objective basic data for Sasang Constitution classification. For this, in this paper, firstly, the signal processing techniques are applied to automatic feature extraction of human frontal faces for Sasang Constitution classification. The experiment is conducted to verify the effectiveness of the proposed system.

• The Journal of the Korean dental association
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• v.52 no.8
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• pp.456-463
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• 2014

Caries Management by Risk Assessment (CAMBRA), published by California Dental Association in 2003, is a customized caries care system that classifies individuals' caries risk into 4 risk groups based on objective evidences and provides chemical treatments targeted for each caries risk level. However, this system was not only developed but also optimized for situation in the United States, resulting into many limitations to be used in Korea, and thus Korean CAMBRA (K-CAMBRA) that considers the clinical situation in Korea needs to be developed. K-CAMBRA includes various techniques that are newly developed in order to overcome the limitations. First, Q-ray, a new optical technology, is utilized in order to avoid the subjectivity of visual inspection during assessment of disease indicators and risk factors. Moreover, Cariview? that reflects the paradigm shift in cariology as a new form of caries assessment kit is used. In addition, considering the situation in Korea, where it is impossible to use high concentration fluoride product, Oral pack with a customized tray is added to increase the contact time of chemical substance. CAMBRA is believed to be the key clinical tool that overcomes the limitations of the paradigm of the conventional restoration-based surgical model of dentistry. Furthermore, it can be expected that Korean dentists can act as oral physicians who are able to control and care individuals' caries risk rather than operative experts who only care about the outcome of caries.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.271-278
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• 2015

This paper develops an OBD Diagnostic Program (Program) using Visual Studio (C#), which was used to diagnosis malfunction information from OBD-II system vehicles. We accomplished this using the Program, Diagnostic tests, Board (STN1110), FTDI Basic Cable, Mini USB Cable, OBD Data Cable, and both hybrid and regular vehicles. The Program tests real-time data output, DTC output, sensor value output, engine RPM, waveform data, OBD type check, PID inspection, and whole monitoring. We found vehicles used in this research had 19 PIDs, which was within OBD-II regulations. We also gathered data on control and diagnostic code regulated by OBD-II system, such as, sensor output value, engine RPM, DTC output, each PID analytic value, OBD type, fuel mode, and whole monitoring result value. Using the data collected through the Program appropriately can lead to more effective diagnostic practices and contribute to education.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.63.2-63.2
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• 2019

Eight hundred thousand asteroids in the solar system have been identified so far under extensive sky surveys. Kilometer to sub-km sized asteroids, however, are still waiting for discovery, and their size and orbital distribution will provide a better understanding of the collisional and dynamical evolution of the solar system. In order to study the number of asteroids which is detectable with 1.6 m telescope and their orbital distribution, we conducted a small observation campaign as a part of Deep Ecliptic Patrol of the Southern Sky (DEEP-South) project, which is an asteroid survey in the southern hemisphere with Korea Microlensing Telescope Network (KMTNet). We observed the ecliptic plane near opposition ($2^{\circ}{\times}2^{\circ}$ field of view centering on ${\alpha}=22h40m31s$, ${\delta}=-08^{\circ}22^{\prime}58^{{\prime}{\prime}}$) in August 2018, and identified 464 moving objects by visual inspection. As a result, 266 of 464 moving objects turn out to be previously unknown asteroids, and their signal to noise ratio is below two on numerous occasions. Most of the newly detected objects are main belt asteroids (MBAs), while three Hildas, one Jupiter trojan, and two Hungarias are also identified. In this meeting, we report the differences in the orbital distributions between the previously known asteroids and newly discovered ones using statistical methods. We also talk about the observational bias of this survey and suggest future works.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.129-138
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• 2022

Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.