• The Journal of the Korea Contents Association
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• v.8 no.4
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• pp.48-54
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• 2008

This paper analyzes cost tradeoffs between memory usage and computation for window-based operators in data stream environments. It identifies generic operator network constructs, and sets up a cost model for the estimation of the expected memory reduction and the computation overheads when window memory relocations are applied to each operator network construct. This cost model helps to identify the utility of window memory relocations. It also helps to apply window memory relocation to improve a query execution plan to save memory usage. The proposed approach contributes to expand the scope of query processing and optimization in data stream environments. It also provides a basis to develop a cost estimation model for the query optimization using window memory relocations.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.193-206
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• 2002

A signal optimization model is proposed by applying the Cell-Transmission Model(CTM) as an embedded traffic flow model to estimate a system-optimal signal timing plan in a transportation network composed of signalized intersections. Beyond the existing signal-optimization models, the CTM provides appropriate theoretical and practical backgrounds to simulate oversaturation phenomena such as shockwave, queue length, and spillback. The model is formulated on the Mixed-Integer Programming(MIP) theory. The proposed model implies a system-optimal in a sense that traffic demand and signal system cooperate to minimize the traffic network cost: the demand departing from origins through route choice behavior until arriving at destinations and the signal system by calculating optimal signal timings considering the movement of these demand. The potential of model's practical application is demonstrated through a comparison study of two signal control strategies: optimal and fixed signal controls.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1104-1113
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• 2015

In this paper, we propose Two-Dimensional Robust Face Recognition System Realized with the Aid of Facial Symmetry with Illumination Variation. Preprocessing process is carried out to obtain mirror image which means new image rearranged by using difference between light and shade of right and left face based on a vertical axis of original face image. After image preprocessing, high dimensional image data is transformed to low-dimensional feature data through 2-directional and 2-dimensional Principal Component Analysis (2D)2PCA, which is one of dimensional reduction techniques. Polynomial-based Radial Basis Function Neural Network pattern classifier is used for face recognition. While FCM clustering is applied in the hidden layer, connection weights are defined as a linear polynomial function. In addition, the coefficients of linear function are learned through Weighted Least Square Estimation(WLSE). The Structural as well as parametric factors of the proposed classifier are optimized by using Particle Swarm Optimization(PSO). In the experiment, Yale B data is employed in order to confirm the advantage of the proposed methodology designed in the diverse illumination variation

• Smart Structures and Systems
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• v.26 no.2
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• pp.241-251
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• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.207-207
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• 2009

Aspen plus는 Aspentech사에서 개발한 공정모사용 프로그램으로서 다양한 화학종의 열역학적 자료를 기반으로 공정설계, 공정최적화, 공정모니터링 등 공정개발에 활용되고 있다. 연료개질기는 수증기 개질반응, 수성가스전이반응, 선택적화학반응으로 구성된 소규모 수소생산공정에 해당된다. 따라서 Aspen 전산모사를 통해 다양한 조건에서의 운전결과를 모사하여 개질기에 미치는 영향을 분석함으로써 운전조건을 최적화 할 수 있다. 연료개질기의 성능에 영향을 미치는 주요인자는 주로 수증기개질 촉매층 출구온도 및 수증기/탄소 비이다. 수증기개질 촉매층의 출구온도를 $660{\sim}740^{\circ}C$로 변화시키면서 개질가스의 조성, 카본 전환율, 개질효율 등을 비교 분석하였다. 또한 수증기/탄소 비를 3~5의 범위에서 변화시키면서 영향을 살펴보았다. 수증기개질 촉매층의 온도가 높을수록 수소생산량이 증가에 따른 효율 증가가 나타났으며 수증기/탄소 비가 증가할 경우에도 개질효율에 긍정적인 영향을 미치는 것을 확인하였다. 하지만 실제 개질기의 운전에서는 소재의 제약에 따라 운전 온도에 제약이 있으며 수증기/탄소비의 증가 역시 개질기의 부피 증가로 이어지는 단점이 있다는 것을 고려해야 한다. 따라서 반응기 재질, 크기, 운전온도와 개질효율과의 상관관계를 파악하여 개질기의 특성을 최적화 하여야 한다.

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.17-29
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• 2007

This paper addresses a RNG based scatternet topology formation, self-healing, and routing path optimization for small-scale distributed environment, which is called RNG-FHR(Scatternet Formation, self-Healing and self-Routing path optimization) algorithm. We evaluated the algorithm using ns-2 and extensible Bluetoothsimulator called blueware to show that RNG-FHR does not have superior performance, but is simpler and more practical than any other distributed algorithms from the point of depolying the network in the small-scale distributed dynamic environment due to the exchange of fewer messages and local control. As a result, we realized that even though RNG-FHR is unlikely to be possible for deploying in large-scale environment, it surely can be deployed for performance and practical implementation in small-scale environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4661-4676
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• 2014

In Wireless Mesh Networks (WMN), an authentication technique can be compromised due to the distributed network architecture, the broadcast nature of the wireless medium and dynamic network topology. Several vulnerabilities exist in different protocols for WMNs. Hence, in this paper, we propose trust based authentication and key establishment for secure routing in WMN. Initially, a trust model is designed based on Ant Colony Optimization (ACO) to exchange the trust information among the nodes. The routing table is utilized to select the destination nodes, for which the link information is updated and the route verification is performed. Based on the trust model, mutual authentication is applied. When a node moves from one operator to another for accessing the router, inter-authentication will be performed. When a node moves within the operator for accessing the router, then intra-authentication will be performed. During authentication, keys are established using identity based cryptography technique. By simulation results, we show that the proposed technique enhances the packet delivery ratio and resilience with reduced drop and overhead.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.740-743
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• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.266-275
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• 2013

The exact closed-form expressions for outage probability and bit error rate of spectrum sharing-based multi-hop decode-and-forward (DF) relay networks in non-identical Rayleigh fading channels are derived. We also provide the approximate closed-form expression for the system ergodic capacity. Utilizing these tractable analytical formulas, we can study the impact of key network parameters on the performance of cognitive multi-hop relay networks under interference constraints. Using a linear network model, we derive an optimum relay position scheme by numerically solving an optimization problem of balancing average signal-to-noise ratio (SNR) of each hop. The numerical results show that the optimal scheme leads to SNR performance gains of more than 1 dB. All the analytical expressions are verified by Monte-Carlo simulations confirming the advantage of multihop DF relaying networks in cognitive environments.

• Journal of the Korean Geosynthetics Society
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• v.6 no.1
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• pp.39-43
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• 2007

Geocomposites of needle punched and spunbonded nonwovens having the reinforcement and drainage functions were manufactured by use of thermal bonding method. The physical properties (e.g. tensile, tear and bursting strength, permittivity) of these multi-layered nonwovens were dependent on the processing parameters of temperatures, pressures, bonding periods etc. - in manufacturing by use of thermal bonding method. Therefore, it is very meaningful to optimize the processing parameters and physical properties of the geocomposites by thermal bonding method. In this study, an algorithm has been developed to optimize the process of the geocomposites using an artificial neural network (ANN). Geocomposites were employed to examine the effects of manufacturing methods on the analysis results and the neural network simulations have been applied to predict the changes of the nonwovens performances by varying the processing parameters.