• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.269-279
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• 1999

본 연구는 Web상에서 원격 협력 학습을 위한 수준별 학습자 모집 스케줄링 에이전트의 설계와 구현에 관해 제안한다. 본 시스템의 구조는 원격 교사 모듈과 여러 명의 학습자, 그리고 이를 연결해 주는 스케줄링 Agents, 학습자를 진단할 수 있는 진단 Agent로 구성된다. 컴퓨터가 분산환경으로 발전됨에 따라서 교육의 변화도 가속화되었고, 지식의 공유와 정보의 공유가 원격 협력학습에 의하여 절실히 필요하게 되었다. 원격 협력 학습에서의 학습자는 동일한 과목과 주제에 흥미를 느끼는 여러 명의 아동이 동시에 학습할 수 있는 상황이 필요하며, 선행 지식 또한 비슷한 수준이어야 동일한 주제로 학습의 효과가 있다. 이런 학습자를 판단하기 위해서 진단 Agent가 학습자를 진단하며 스케줄링 Agents의 학습자 지식에 추가한 후 스케줄링 Agents가 학습자의 기본 사항과 요구 내용을 추론하여 비슷한 수준의 학습자를 연결한다. 교사 모듈은 전통적인 ITS의 구조의 교수 학습 모듈, 전문가모듈로 구성되어 교수 학습을 할 수 있다. 이렇게 여러 명의 학습자를 연결하여 협력학습을 하기 위해서는 학습자간의 요구사항과 지식 수준 그리고 학습 가능한 시간이 같아야 하는데 이를 위해 시간을 자원으로 하는 동적 자원 스케줄링(Dynamic Resource Scheduling)으로 모델링 하였다. 본 연구에서 도형학습을 기반으로 하는 실험을 통해 구현한 원격 협력학습을 위한 지능형 스케줄링 에이전트를 평가하였다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.9
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• pp.351-358
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• 2006

In this paper, DSPOCS(Digital Switchgear-Panel Operation and Control Solution) is designed, which is the intelligent inference based operation and control solution to obtain the safety and reliability of electric power supply in substation based on IED. DSPOCS is designed as a scheduled monitoring and control task and a real-time alarm inference task, and is interlinked with BRES(Bus Reconfiguration Expert System) in the required case. The intelligent alarm inference task consists of the alarm knowledge generation part and the real-time pattern matching part. The alarm knowledge generation part generates automatically alarm knowledge from DB saves it in alarm knowledge base. On the other hand, the pattern matching part inferences the real-time event by comparing the real-time event information furnished from IEDs of substation with the patterns of the saved alarm knowledge base.; Especially, alarm knowledge base includes the knowledge patterns related with fault alarm, the overload alarm and the diagnosis alarm. In order to design the database independently in substation structure, busbar is represented as a connectivity node which makes the more generalized graph theory possible. Finally, DSPOCS is implemented in MS Visual $C^{++}$, MFC, the effectiveness and accuracy of the design is verified by simulation study to the typical distribution substation.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.12
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• pp.578-584
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• 2005

This paper describes development of management software for transformers based on artificial intelligent analysis technology of dissolved gases in oil. Fault interpretation using the artificial intelligent analysis is performed by the artificial neural network and a rule based on the analysis of dissolved gases. The used gases are acetylene($C_{2}H_{2}$), hydrogen($H_2$), ethylene($C_{2}H_{4}$), methane($CH_4$), ethane($C_{2}H_{6}$), carbon monoxide(CO) and carbon dioxide($CO_2$). This software is mainly composed of gases input, fault's causes, expected fault's phenomena in detail, the decision on maintenance as well as report and gas trend windows. It is indicated that this is very powerful software for the efficient management of oil-immersed transformers using data analysis of gas components.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.15-20
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• 2016

This paper describes the construction of knowledge data retrieval management system based on medical image CT. The developed system is aimed to improve the efficiency of the hospital by reading the medical images using the intelligent retrieval technology and diagnosing the patient 's disease name. In this study, the medical image DICOM file of PACS is read, the image is processed, and feature values are extracted and stored in the database. We have implemented a system that retrieves similarity by comparing new CT images required for medical treatment with the feature values of other CTs stored in the database. After converting 100 CT dicom provided for academic research into JPEG files, Code Book Library was constructed using SIFT, CS-LBP and K-Mean Clustering algorithms. Through the database optimization, the similarity of the new CT image to the existing data is searched and the result is confirmed, so that it can be utilized for the diagnosis and diagnosis of the patient.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.116-124
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• 2009

This paper proposes a new Artificial-Intelligent(AI) Power Quality(PQ) diagnosis algorithm using Discrete Wavelet Transform(DWT), Fast Fourier Transform(FFT), Root-Mean-Square(RMS) value. The developed algorithm is able to detect and classify the PQ problems such as the transient, the voltage sag, the voltage swell, the voltage interruption and the total harmonics distortion. The 15.36[kHz] sampling frequency is used to measure the voltages in a power system. The measured signals are used for DWT, FFT, RMS calculation. For AI diagnosis of the PQ problems, a simple multi-layered Artificial Neural Network(ANN) with the back-propagation algorithm is adopted, programmed in C++ and tested in PSIM simulation studies. Finally, the algorithm, which is installed in MP PQ+256 with TI DSP320C6713, is proved to diagnose the PQ problems efficiently.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.137-142
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• 2012

The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.367-372
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• 2008

In this paper we proposed a model of a fault diagnosis expert system with high reliability to compare identical well-functioning motors. The purpose of the survey was to determine if any differences exit among these identical motors and to identify exactly what these differences were, if in fact they were found. Using measured data for many identical brushless dc motors, this study attempted to find out whether normal and fault can be classified by each other. Measured data was analyzed using the State Transition Model (STM). Based on a proposed STM method, the effect of a different normal state is minimized and the detection of fault is improved in identical motor system. Experimental results are presented to prove that STM method could be a useful tool for diagnosing the condition of identical BLDE motors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.80-95
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• 2019

Recently, with the development of sensor and communication technology, researchers at home and abroad have actively conducted research on methodologies for determining maintenance through diagnosis and prediction techniques by collecting information on the status of equipment or systems. Based on the status of vehicle parts at this point in time, this study presented a system framework for making maintenance decisions by predicting the change in vehicle part status to a future date based on the current state of vehicle parts. In addition, condition diagnosis and predictive data adjustment was configured through tracking the status of vehicle parts before and after maintenance activities. We hope that the application of the results of this study will contribute a little to the safety of citizens using public buses and to the activation of the condition-based maintenance system of vehicles.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.3
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• pp.1-7
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• 2011

As the technologies of electronic device have advanced these days, most of mechanical systems are designed with electronic control unit to take advantage of control parameter adaption to operating conditions and firmware flexibilities as well. On-board diagnosis, which detects the system malfunction and identifies potential source of error with its own diagnostic criteria, and fail-safe that can switch the mode of operation in view of recognized error characteristics enables easy maintenance and troubleshooting as well as system protection. This paper dealt with the development of diagnosis and fail-safe function for proportional flow control valve. All type of errors related to valve control system components are investigated and assigned to a specific hexadecimal codes. Cumulative error detection algorithm is applied in order for the sensitivity and reliability to be appropriate. Embedded simulator which runs simultaneously with system program provides the virtual error simulation environment for expeditious development of error detection algorithm. The diagnosis function was verified both with solenoid valve and embedded simulator test and it will enhance the valve control system monitoring function.