• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.134-143
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• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• 대한안전경영과학회지
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• 제2권4호
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• pp.59-70
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• 2000

To keep enterprise's competitiveness on condition of the automatic manufacturing system such as FA, FMS and CIM, all the maintenance problems should be considered seriously in not only production and maintenance but also related industrial safety. As we analyze in the surveys for the maintenance management of domestic enterprises and the causes of Industrial accident, there will be necessity of drawing up countermeasures for prevention of industrial accidents and for ensuring expertise maintenance technologies. Based on these analyses, this study studied the safety information system, maintenance management information system, and the machinery condition diagnosis technique by using of the knowledge-based system under the internet environment. This web based knowledge-based diagnosis system can easily provide not only the knowledge of expert about deterioration phenomenon of industrial robot, but also the knowledge of relating safety and facility on everywhere, everytime. Therefore, when we use this system, it is expected to improve the efficiency of business processes in the production and safety.

• Journal of Biosystems Engineering
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• 제24권3호
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• pp.267-274
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• 1999

An internet and network based software and related interface have been developed, which can remotely control and manage an on-site operating system. Developed software modules were composed of two parts: monitoring/management modules and control/diagnosis modules were developed for the network status, warehouse, production and selling status. Modules of control with diagnosis were developed for the on-site operating system and interface. Each module was integrated and the whole modules have been tested with an automatic mushroom grading/sorting system which was built in a laboratory. Developed software modules worked successfully without any uncommon situations such as system down caused by the software or data transfer error. Each software module was developed independently in order to apply easily to other existing on-site systems such as rice processing centers, fruit and vegetable sorting, packaging and distribution centers scattered over the country.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.410-413
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• 1997

The most important wave set in ECG is the QRS complex. Automatic classification of the QRS complex is very useful in the diagnosis of cardiac dysfunction. Also, diagnosis is influenced by selection of dominant beat. In this paper, we propose simple algorithm for QRS detection. And we determine correlation between significan attributes of QRS complexs. We evaluated the efficiency of proposed method with the CSE database.

• 대한전자공학회논문지
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• 제26권8호
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• pp.1217-1226
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• 1989

This paper describes a design of hardware system for real time automatic diagnosis of ECG arrhythmia based on pipeline processor consisting of three microcomputer. ECG data is acquisited by 12 bit A/D converter with hardware QRS triggered detector. Four diagnostic parameters-heart rate, morpholigy, axis, and ST segment-are used for the classification and the diagnosis of arrhythmia. The functions of the main CPU were distributed and processed with three microcomputers. Therefore the effective data process and the real time process using microcomputer can be obtained. The interconnection structure consisting of two common memory unit is designed to decrease the delay time caused by data transfer between processors and be which the delay time can be taken 1% of one clock period.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2000년도 추계학술발표논문집
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• pp.269-279
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• 2000

To keep enterprise's competitiveness on condition of the automatic manufacturing system such as FA, FMS and CIM, all the maintenance problems should be considered seriously in not only production and maintenance but also related Industrial safety. As we analyze in the surveys for the maintenance management of domestic enterprises and the causes of industrial accident, there will be necessity of drawing up countermeasures for prevention of industrial accidents and for ensuring expertise maintenance technologies. Based on these analyses, this study studied the safety information system, maintenance management information system, and the machinery condition diagnosis technique by using of the knowledge-based system under the internet environment. This web based knowledge-based diagnosis system can easily provide not only the knowledge of expert about deterioration phenomenon of industrial robot, but also the knowledge of relating safety and facility on everywhere, everytime. Therefore, when we use this system, it is expected to improve the efficiency of business processes in the production and safety.

• 유공압시스템학회:학술대회논문집
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• 유공압시스템학회 2010년도 춘계학술대회
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• pp.56-61
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• 2010

Simulator is a development equipment which enables the ECU to operate in normal mode by simulating the interface signal between ECU and mechanical system electrically. Embedded simulator means simulation function is embedded in ECU firmware, hence the electrical signal interface is replaced by the substitution of information at system program level. This paper explains the development of embedded transmission simulator for the verification of TCU firmware function which covers shifting control and on-board diagnosis. The embedded simulation program is executed in TCU processor along with the TCU firmware and it provides TCU firmware with not only the speed information those are appropriate both in driving and shifting conditions, but also the fault detection signals. Experimental results show that the validity of embedded simulator and its usefulness to the TCU firmware development and verification.

• 한국자동차공학회논문집
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• 제1권2호
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• pp.49-59
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• 1993

A new signal processing technique is applied to four-cylinder spark and compression ignition engines for the diagnosis of power faults inside the cylinders. This technique utilizes two-sided directional power spectra(예S) of complex vibration signals measured from engine blocks as the patterns for engine cylinder power faults. The dPSs feature that they give not only the frequency contents but also the directivity of the engine block motion. For the automatic detection/diagnosis of cylinder power faults, pattern recognition method using multi-layer neural networks is employed. Experimental results show that the sucess rate for diagnosis of cylinder power faults using dPSs is higher than that using the conventional one-sided power spectra. The proposed technique is also tested to check the robustness to the sensor position and the engine rotational speed.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.