• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.125-134
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• 2022

Due to the development of smartphone technology, as of 2020, 91.9% of people use the Internet[1] to frequently acquire information through websites and mobile apps. As the number of homepages in charge of providing information is increasing every year, the number of applications for web vulnerability diagnosis, which diagnoses the safety of homepages, is also increasing. In the existing web vulnerability check, the number of diagnostic personnel should increase in proportion to the number of homepages that need diagnosis because the diagnosticians manually test the homepages for vulnerabilities. In reality, however, there is a limit to securing a web vulnerability diagnosis manpower, and if the number of diagnosis manpower is increased, a lot of costs are incurred. To solve these problems, an automatic diagnosis tool is used to replace a part of the manual diagnosis. This paper explores a new method to expand the current automatic diagnosis range. In other words, automatic diagnosis possible items were derived by analyzing the impact of web vulnerability diagnosis items. Furthermore, automatic diagnosis identified possible items through comparative analysis of diagnosis results by performing manual and automatic diagnosis on the website in operation. In addition, it is possible to replace manual diagnosis for possible items, but not all vulnerability items, through the improvement of automatic diagnosis tools. This paper will explore some suggestions that can help improve plans to support and implement automatic diagnosis. Through this, it will be possible to contribute to the creation of a safe website operating environment by focusing on the parts that require precise diagnosis.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.8
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• pp.1787-1798
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• 1995

Monitoring and diagnosis of the operating machine mean evaluating the condition of a machine such as the detection of the defects and the prediction of the time to failure in the machine elements, while it is running. In this study, a technique of automatic diagnosis using probability concept is studied and the analyses of the pattern comparison are introduced. An expert system, which is able to analyze the automatic identification of the multiple defects in the ball bearings, is also developed. Finally, to confirm the effectiveness of the programmed algorithms, some tests were made with specimens of the ball bearings involving the multiple defects. The proposed system reasonably predicts the defects.

• Journal of KSNVE
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• v.5 no.3
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• pp.353-360
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• 1995

A new automatic diagnostic system for predicting multiple defects in rolling element bearings is developed by taking probbability into account. A database is constructed from the frequency characteristics of tested bearings with various types of defects. The proposed algorithms for the automatic diagnosis of bearing defects are shown to be satisfactory through the experiments. This method can be effectively used for quality control of the rolling bearing in plants.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1103-1107
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• 2016

The automatic tensioning device of the catenary system constantly maintains the tension of the trolley wire by absorbing the variations due to the elasticity of the line caused by temperature variation. The tension plays an important role in affecting the electric motorcar operation directly. This paper suggests the methodology of the life cycle extension and the maintenance of the automatic tensioning device by means of performance diagnosis of the pulley type automatic tensioning device which has been commonly used for the electric railway system. Through conducting performance diagnosis and comparative test for the wornout pulley type automatic tensioning device by replacing the components such as the bearing and the bearing shaft without replacing all the assembly, the tensioning device is analyzed whether it is properly functioned. Provided that the maintenance regulation is reinforced so as to implement the bearing replacement through periodical precise inspection along with random check-up inspection which is now carried out by the operating organizations, it is ensured that the life cycle extension and the reduction of maintenance cost of the tensioning device could be achieved.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.349-352
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• 1988

In this study, a structured supervisory control for automatic assembly system is developed. And also diagnosis and fault-recovery capability of this supervisory control are discussed. This structured supervisory control is actually applied to Die Bonding Machine and is proved to be useful and to work well.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.706-712
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• 2009

In this paper, a fault diagnosis method is proposed to detect and classifies faults that occur in press process line. An oil level automatic monitoring method is also presented to detect oil level. The FFT(fast fourier transform) frequency analysis and ART2 NN(adaptive resonance theory 2 neural network) with uneven vigilance parameters are used to achieve fault diagnosis in proposing method, and GUI(graphical user interface) program for fault diagnosis and oil level automatic monitoring using LabVIEW is produced and fault diagnosis was done. The experiment results demonstrate the effectiveness of the proposed fault diagnosis method of induction motors and oil level automatic monitor system.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1326-1328
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• 1987

This paper describes the design of digital signal processing system for the automatic diagnosis of ECG. The system comprises analog hardware, digital hardware, and control system by microcomputer. Also, since digital signal processing system can be equipped easily in microcomputer for the compact size(Single board), We expect to develop the Portable ECG Automatic Diagnosis System using this System.

• Phonetics and Speech Sciences
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• v.2 no.2
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• pp.69-75
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• 2010

We propose an automatic pronunciation diagnosis system to evaluate the pronunciation of a foreign language without the uttered text. We recorded English utterances spoken by native and Korean speakers, and utterances spoken by Koreans are evaluated by native speakers based on three criteria: fluency, accuracy of phones and intonation. The system evaluates the utterances of test Korean speakers based on the differences of log-likelihood given two models: one is trained by English speech uttered by native speakers, and the other is trained by English speech uttered by Korean speakers. We also applied purification algorithm to increase class differentiability. The purification can detect and eliminate the non-speech frames such as short pauses, occlusive silences that do not help to discriminate between utterances. As the results, our proposed system has higher correlation with the human scores than the baseline system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.279-284
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• 2007

Nowadays, due to development of automatic control devices and various sensors, one operator can freely handle several remote plants and processes. Automatic diagnosis and warning systems have been adopted in various fields, in order to prepare an operator's absence for patrolling plants. In this paper, a Bayesian networks based on-line diagnosis system is proposed for a wastewater treatment process. Especially, the suggested system is included learning structure, which can continuosly update conditional probabilities in the networks. To evaluate performance of proposed model, we made a lab-scale five-stage step-feed enhanced biological phosphorous removal process plant and applied on-line diagnosis system to this plant in the summer.