• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.1
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• pp.43-50
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• 2007

In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.83-86
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• 2007

The necessity of the high speed exclusive use server that connect digital measure instrument flag to internet and CPU use rate of measure flag uses other imbedded operating system with many general servers to verification & proofreading system in remotion. This research is objective that embody high speed network module for Site-Based remote verification & proofreading system and through internet digital measure instrument flag to remote verification & proofreading, TCP/IP Offload Engine processing and improved that is Imbedded TCP/IP stack for high speed networking.

• Journal of ILASS-Korea
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• v.19 no.4
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• pp.188-196
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• 2014

The spray and combustion characteristics of n-dodecane fuel were investigated in a CVCC (constant volume combustion chamber). The selection of ambient conditions for the spray followed ECN (engine combustion network) guidelines, which simulates the ambient condition of diesel engines at start of fuel injection. ECN is a collaboration network whose main objective is to establish an internet library of well-documented experiments that are appropriate for model validation and the advancement of scientific understanding of combustion at conditions specific to engines. Therefore repeatability of the experiments with high accuracy was important. The ambient temperature was varied from 750 to 930 K while the density was fixed at around $23kg/m^3$. The injection pressure of the fuel was varied from 500 to 1500 bar. The spray was injected in both non-reacting ($O_2$ concentration of 0%) and reacting conditions ($O_2$ concentration of 15%) to examine the spray and the combustion characteristics. Direct imaging with Mie Scattering was used to obtain the liquid penetration length. Shadowgraph was implemented to observe vapor length and lift-off length at non-reacting and reacting conditions, respectively. Pressure data was analyzed to determine the ignition delay with respect to the spray and ambient conditions.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.102-109
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• 2006

In this Paper, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. The system that uses the ANN falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the Separate Learning Algorithm(SLA) of ANN has been proposed by using SVM. This is the method that ANN learns selectively after discriminating the defect position by SVM, then more improved performance estimation can be obtained than using ANN only. The proposed SLA can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1077-1082
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• 2007

The necessity of the high speed exclusive use server that connect digital measure instrument flag to internet and CPU use rate of measure flag uses other imbedded operating system with many general servers to verification & proofreading system in remotion. This research is objective that embody high speed network module for Site-Based remote verification & proofreading system and through internet digital measure instrument flag to remote verification & proofreading, TCP/IP Offload Engine processing and improved that is Imbedded TCP/IP stack for high speed networking.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.11a
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• pp.23-27
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• 2008

In this study, Real Coded Genetic Algorithm(RCGA) and Artificial Neural Network(ANN) are used for developing the defect diagnostics of the aircraft turbo-shaft engine. ANN accompanied with large amount data has a most serious problem to fall in the local minima. Because of this weak point, it becomes very difficult to obtain good convergence ratio and high accuracy. To solve this problem, GA based ANN has been suggested. GA is able to search the global minima better than ANN. GA based ANN has shown the RMS defect error of 5% less in single and dual defect cases.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.8
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• pp.783-790
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• 2010

In this paper, we propose a turbine mass flow rate model for a variable geometry turbocharger (VGT) using an artificial neural network (ANN). The model predicts the turbine mass flow rate using the VGT vane position, engine rotational speed, exhaust manifold pressure, exhaust manifold temperature, and turbine outlet pressure. The ANN is used for the estimation of the effective flow area. In order to validate the results estimated by the proposed model, we have compared estimation results with engine experimental results. The results, in addition, represent improved estimation accuracy when compared with the performance using the turbine map.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.476-481
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• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.5
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• pp.257-266
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• 2006

Recently TCP/IP Offload Engine (TOE) technology, which processes TCP/IP on a network adapter instead of the host CPU, has become an important approach to reduce TCP/IP processing overhead in the host CPU. There have been two approaches to implementing TOE: software TOE, in which TCP/IP is processed by an embedded processor on a network adapter; and hardware TOE, in which all TCP/IP functions are implemented by hardware. This paper proposes a hybrid TOE that combines software and hardware functions in the TOE. In the hybrid TOE, functions that cannot have guaranteed performance on an embedded processor because of heavy load are implemented by hardware. Other functions that do not impose as much load are implemented by software on embedded processors. The hybrid TOE guarantees network performance near that of hardware TOE and it has the advantage of flexibility, because it is easy to add new functions or offload upper-level protocols of TCP/IP. In this paper, we developed a prototype board with an FPGA and an ARM processor to implement a hybrid TOE prototype. We implemented the hardware modules on the FPGA and the software modules on the ARM processor. We also developed a coprocessing mechanism between the hardware and software modules. Experimental results proved that the hybrid TOE prototype can greatly reduce the load on a host CPU and we analyzed the effects of the coprocessing mechanism. Finally, we analyzed important features that are required to implement a complete hybrid TOE and we predict its performance.