• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.515-524
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• 2017

In this paper, we design and implement an expert system for diagnosing ADHD. As a result of the analysis with DSM-IV-TR, the ADHD diagnostic criteria are changed according to the age group. With this analyzed diagnostic, objects and their values are set and rules are created. We design a diagnostic system consisting of 'ADHD diagnostic system engine' and 'user query response program'. The ADHD diagnostic system engine is a rule-based reasoning engine that is implemented in the Prolog language and receives INPUT from the user query response program. By INPUT, the rule is executed based on the ADHD diagnostic criteria and the OUTPUT is sent back to the 'user query response program' by inferring the diagnostic result. The 'user query response program' is implemented in the Python language and serves as an interface for handling conversation with the user. The bridge between 'ADHD diagnostic system engine' and 'user query response program' is performed through the Pyswip library. As a result, the ADHD Diagnostic Expert System will help you plan your treatment with reduced diagnostic costs and use-complexity.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.130.6-130
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• 2001

On-Board diagnostic systems are installed in passenger cars and light trucks on today. During the 1970's and early 1980's manufacturers started using electronic means to control engine functions and diagnose engine problems. This wa primarily to meet EPA emission standards. The CARB requires that, by model year 1996, all vehicle sold in California contain a certain minimum "On-Board Diagnostic" capability to diagnose emissions-related failures of the engine control system. These diagnostic requirements have been designated as OBD with a goal of monitoring all of the emissions-related components on-board the vehicle for proper operation. Part of the intent of CARB´s OBD program is that a single diagnostic tester can be used to read the diagnostic information from any OBD-compliant vehicle. A tester which ...

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.9
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• pp.1157-1165
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• 2000

A classic examples of the abnormal combustions are the knock and misfire, which raise noxious performance and life of the engine. A heavy knock can also cause severe damages to the engine itself, which gives more reason why it must be detected and corrected. With the response of the today's requirements, we have researched the new diagnostic system which uses the breakdown voltage characteristics between electrodes of spark plug. This breakdown voltage depends on the pressure, temperature and even the shape and material of electrodes. But there is no data of breakdown voltage in case of using the spark plug as a electrodes. So, in this study, we show the breakdown voltage characteristic by pressure and temperature in constant volume bomb, which will make it possible to diagnose the engine combustion phenomenon.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.34-42
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• 2009

Gas turbine performance diagnostics is a method for detecting, isolating and quantifying faults in gas turbine gas path components. On-line precise fault diagnosis can promote greatly reliability and availability of gas turbine in real time operation. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module are used for reducing computer calculating time and a signal generation module for simulating real time performance data. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. Evaluation of the proposed on-line diagnostic program is performed through application to the helicopter engine health monitoring.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.112-122
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• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.12
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• pp.1238-1244
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• 2009

This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module for reducing computer calculating time and a signal generation module for simulating real time performance data are used. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. The reliability and capability of the proposed on-line diagnostic program were confirmed through application to the helicopter engine health monitoring.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.6
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• pp.2057-2066
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• 1996

^In this paper, diagnostic technique for detecting the engine faults, especially misfire, are introduced and compared with each other under the same conditions. With all of them the instantaneous angular velocitys, measured at the flywheel, were analyzed. The techniques include the frequency analysis, auto-correlation function, velocity index, acceleration index, maximum acceleration index, and integrated torque index. Since the main driving components for the angular velocity fluctuation are both the pressure and the inertia torque, the component of the inertia torque in it must be excluded to extract the information of the combustion from the angular velocity. To do this, it is required to consider only the first half of the combustion period in the angular velocity fluctuations, which has never been proposed in the existing methods. On the basis of this fact, the results show that the most effective diagnostic technique is maximum acceleration index.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.2
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• pp.1-7
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• 2011

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.