• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2033-2044
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• 2018

This study presents states-of-the art overview of the system level electrostatic discharge (ESD) analysis and testing. After brief description of ESD compliance standards and ESD coupling mechanisms, the study provides an in-depth review and comparison of the various techniques for the system level ESD coupling analysis using time and frequency domain techniques, full wave electromagnetic modeling and hybrid modeling. The methods used for improving system level ESD testing using troubleshooting and determining the root causes of soft failures, the optimization of ESD testing and the countermeasures to mitigate ESD problems are also discussed.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.275-282
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• 2008

This paper presents numerical investigation of multi-phase flow in solid rocket motor nozzle and effect of multi-phases on the performance prediction of the Solid Rocket Motor. Aluminized propellants are frequently used in solid rocket motors to increase specific impulse. An Eulerian-Lagrangian description has been used to analyze the motion of the micrometer sized and discrete phase that consist of the larger particulates present in the Solid Rocket Motor. Uniform particles diameters and Rosin-Rammler diameter distribution method has been used for the simulation of different burning of aluminum droplets generating aluminum oxide smokes. Roe-FDS scheme has been used to simulate the effects of the multi-phase flow. The results obtained show the sensitivity of this distribution to the nozzle flow dynamics, primarily at the nozzle inlet and exit. The analysis also provides effect of two phases on performance prediction of Solid Rocket Motor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4706-4726
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• 2019

The paper is devoted to the detailed description of the distributed system for gathering data from Windows-based workstations and servers. The research presented in the beginning demonstrates that neither a solution for gathering data on attacks against Windows based PCs is available at present nor other security tools and supplementary programs can be combined in order to achieve the required attack data gathering from Windows computers. The design of the newly proposed system named Colander is presented, too. It is based on a client-server architecture while taking much inspiration from previous attempts for designing systems with similar purpose, as well as from IDS systems like Snort. Colander emphasizes its ease of use and minimum demand for system resources. Although the resource usage is usually low, it still requires further optimization, as is noted in the performance testing. Colander's ability to detect threats has been tested by real malware, and it has undergone a pilot field application. Future prospects and development are also proposed.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.9
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• pp.116-123
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the closed loop porblem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and effi- ciently. This algorithm is a hybrid type of using both declarative description (constraint representation) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.552-562
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• 1997

This paper proposes a formal method on confromance testing for INAP(AIN) test sequence generation by optimization technique.In order to implement and prove the dffectiveness of the proposed method,we specify the SRSM of INAP protocol SRF in SDL and generate I/O FSM by using our S/W tool. We generate an opti-mal test sequence by applying our method our method to this reference I/O FSM. We prove experimentally that the length of the generated test sequence by our method is more effective and shorter(i.e 32% improved)than the one geverated by UIO method,and estimate that The test coverage space of our test sequence is larger that of UIO method.

• Geomechanics and Engineering
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• v.32 no.6
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.11a
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• pp.189-192
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• 2008

Modern broadcasting/multicasting networks has the heterogeneous nature in terms of terminals and available bandwidth. Such heterogeneity could be coped by scalable video coding (SVC) standard developed recently. More specifically, spatial layers of an SVC bitstream can be consumed by different terminals and SNR (and temporal) scalability can be used to cope with bandwidth heterogeneity. In this work, we tackle the problem of SVC adaptation for different user groups receiving the same broadcast/multicast video, so as to provide a flexible tradeoff between the groups while also maximizing the overall quality of the users. The adaptation process to truncate an SVC bitstream is first formulated as an optimization problem. Then the problem is represented by MPEG-21 DIA description tools, which can be solved by a universal processing. The results show that MPEG-21 DIA is useful to enable automatic and interoperable adaptation in our scenario.