• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.173-180
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• 2016

Fault detection and diagnosis is a task to monitor the occurrence of faults and pinpoint the exact location of faults in the system. Fault detection and diagnosis is gaining importance in development of efficient, advanced and safe industrial systems. Three phase inverter is one of the most common and excessively used power electronic system in industries. A fault diagnosis system is essential for safe and efficient usage of these inverters. This paper presents a fault detection technique and fault classification algorithm. A new feature extraction approach is proposed by using three-phase load current in three-dimensional space and neural network is used to diagnose the fault. Neural network is responsible of pinpointing the fault location. Proposed method and experiment results are presented in detail.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.3
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• pp.571-588
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• 2001

Purpose : This study was to develop the nursing database for gastric cancer patients for clinical application. Method : Nursing data that development of this data base is comprehensive connected with gastric cancer patient nursing process frame to foundation as classification. Result : Each stage was processed based on the System Development Life Cycle. At the Strategy Planning stage, gastric cancer patient nursing process were analyzed. At the system Analysis Stage, database flowchart was drawn up based on frame of nursing process was drawn up. At the system Design Stage, a system was developed based on the flowchart and named the Nursing Database. The Nursing Database consisted of the patient's Basic Information, Patient's Nursing History, Discharge summary, Nursing Assessment, Nursing Diagnosis, Nursing Intervention/activity, Nursing Evaluation, Statics, Code Registration. Each element in flowchart was coded and made into a database. Nursing Assessment classified according to Gorden's Health Pattern Typology, and nursing diagnosis draws the standard 27 name of Hanguls and connected with nursing assessment. Nursing intervention and nursing activity draw 192 of thing that present in NIC, connected this with nursing assessment. Nursing evaluation is linked with nursing assessment, diagnosis and intervention by achievement availability of nursing goals. Conclusion : The biggest advantage of this database nursing process that can manage nursing information exactly and rapidly to foundation be.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.1
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• pp.202-208
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• 2006

The goal of this study is to develop knowledge representation method for the construction and evaluation of ontology for diagnosis in oriental medicine. To develop the expert system for decision making on diagnosis and treatment, the systematic and structural knowledge which can be processible in EMR(Electronic Medical Record) must be precedent, and the Computational Process which control the system as well. This study set up an ontology as a trial model to represent the oriental medical knowledge into the machine processible one. Protege 2.1 has been used to build the ontology, and the serialization format of our ontology is the XML document based on OWL. The components of oriental medical diagnosis was arranged with the combination of symptoms which belong to the certain symptom patterns. Then natural language which expresses the oriental medical diagnosis components were converted into the logical sentence, and individual characteristic symptoms into each values of specific properties. In addition to the study, the diagnosis software for oriental medicine was developed and it used the ontology which we developed. Sequently, we tested the software to confirm the appropriateness of ontology. The result of the test shows that diagnostic questions are automatically formulated according to the diagnosis components of this ontology and that as such diagnostic results are induced. Therefore, the ontology system in this study will be efficient to develop the diagnosis program and useful as a tool for doctors to make decision. But, it is not recommendable to apply the system to the clinical environment until the clear diagnosis standards are introduced, and the more reliable diagnosis program can be developed based on the more appropriate ontology mentioned above.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.286-291
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• 1990

We studied about the adopted system to POSCO facility which is very complexity and various as an example for studying a facility diagnosis and management system that is starting to be distinguished in the nowadays. At the first, explain what's the background why POSCO adopted the system, and it's goal, and it's promoting direction and ways. And there after studied it's results and effective analysis. Finally inquiry about the future improvement and development items through problems which become known during the project periods.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1942-1944
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• 2000

On-line diagnosis system has been developed and is now applied the detection of shorted-turn in the field winding of large generator. This system consists of data aquisition system and display PC. The data aquisition system detects voltage waveform from flux probe sensor installed in the stator slot. The display PC shows the shorted-turn situation of generator rotor winding.

• Journal of Biomedical Engineering Research
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• v.30 no.1
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• pp.41-48
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• 2009

In this paper, we present the diagnosis system for swallowing disorder. There are some types of diagnosis device for swallowing disorder, for example, the video fluoroscopy, the nuclear medicine inspection, the endoscopy, EMG and motion analysis. But these systems need heavy devices or have dangerous nuclear exposure, so are uncomfortable for handicapped person. Our system has advantages of simplicity, accuracy and quantitative analysis. In addition to the diagnosis aspect, this system can be used to biofeedback treatment.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.496-507
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• 2001

An expert system for the diagnosis and indication of hypertension is implemented through HTML-based backward inference. HTML-based backward inference is performed using the hypertext function of HTML, and many HTML files, which are hyperlinked to each other based on the backward rules, should be prepared beforehand. The development and maintenance of the HTML files are conducted automatically using the decision graph. Still, the drawing and input of the decision graph is a time consuming and tedious job if it is done manually. So, automatic generator of the decision graph for the diagnosis and indication of hypertension was implemented. The HTML-based backward inference ensures accessibility, multimedia facilities, fast response, stability, easiness, and platform independency of the expert system. So, this research reveals that HTML-based inference approach can be used for many Web-based intelligent site with fast and stable performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.7-10
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• 2002

The diagnosis of faults of machine tool, which is controlled by CNC and PLC, is generally based on ladder diagram of PLC. Because sequential controls for CNC and servo motor are mostly processed in PLC. However, when fault is occurred, a searching for logical relation to fault reasons is required a lot of fault experiences and times, because PLC program has step structure. In this paper, FDS(Fault Diagnosis System) is developed and implemented to machine tool with open architecture controller in order to find the reason of fault lastly and correctly. The diagnosed reasons for fault are tele-serviced on web through developed RSS(Remote Service System). The operationability and usefulness of developed system are evaluated on specially manufactured machine tool with open architecture CNC. The results of this research can be the model of remote monitoring and fault diagnosis system of machine tool with open architecture CNC.