• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.587-591
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• 2003

This study develops a neural network for solving optimization problems. Hopfield network has been used for such problems, but it frequently gives abnormal solutions or non-optimal solutions. Moreover, it takes much time for solving a solution. To overcome such disadvantages, this study adopts a neural network whose output nodes change with a small value at every evolution, and the proposed neural network is applied to solve ALB (Assembly Line Balancing) problems . Given a precedence diagram and a required number of workstations, an ALB problem is solved while achieving even distribution of workload among workstations. Here, the workload variance is used as the index of workload deviation, and is reflected to an energy function. The simulation results show that the proposed neural network yields good results for solving ALB problems with high success rate and fast execution time.

• 한국수자원학회논문집
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• 제35권2호
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• pp.187-194
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• 2002

하천유역 면적강우량 산정의 정확도를 개선하기 위하여 기존 강우관측자료의 통계적 특성을 이용한 강우관측망의 최적설계방법을 연구하였다. 최적설계를 위한 목적함수는 면적강우량의 추정오차 및 지점강우량 관측비용의 항으로 구성하고, 그 값이 최소인 관측망은 선정하였다. 통계f7파의 추정방법으로는 통계적 분산 산정방법인 크리깅 모형을 채택하였다. 비용은 강우관측소의 설치비와 연간운영 비론 적용하고, 오차항과 비용항의 통합에는 등치매개변수를 이용하였다. 연구된 최적설계방법을 댐 신설로 강우관측소 증설이 필요한 용담댐 유역에 적용하여, 대상유역의 최적 강우관측망을 제안하였다.

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.553-559
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• 2004

In todays security industry, personal identification is also based on biometric. Biometric identification is performed basing on the measurement and comparison of physiological and behavioral characteristics, Biometric for recognition includes voice dynamics, signature dynamics, hand geometry, fingerprint, iris, etc. Iris can serve as a kind of living passport or living password. Iris recognition system is the one of the most reliable biometrics recognition system. This is applied to client/server system such as the electronic commerce and electronic banking from stand-alone system or networks, ATMs, etc. A new algorithm using nonlinear function in recognition process is proposed in this paper. An algorithm is proposed to determine the localized iris from the iris image received from iris input camera in client. For the first step, the algorithm determines the center of pupil. For the second step, the algorithm determines the outer boundary of the iris and the pupillary boundary. The localized iris area is transform into polar coordinates. After performing three times Wavelet transformation, normalization was done using sigmoid function. The converting binary process performs normalized value of pixel from 0 to 255 to be binary value, and then the converting binary process is compare pairs of two adjacent pixels. The binary code of the iris is transmitted to the by server. the network. In the server, the comparing process compares the binary value of presented iris to the reference value in the University database. Process of recognition or rejection is dependent on the value of Hamming Distance. After matching the binary value of presented iris with the database stored in the server, the result is transmitted to the client.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.193-196
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• 2000

ln this paper, we consider to apply of 2-DOF (Degree of Freedom) PID controller at D.C servo motor system. Many control system use I-PD, PIB control system. but the position control system have difficulty in controling variable load and changing parameter We propose neural network 2-DOF PID control system having feature for removal disturbrances and tracking function in the target value point.

• ETRI Journal
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• 제28권1호
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• pp.59-66
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• 2006

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF-SVD-SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine-tuning of centers and widths still shows ill-behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center-gradient variance of the RBFN-SVD-SG algorithm. We found analytically that the steadystate weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center-gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady-state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.

• Advances in Computational Design
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• 제1권3호
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• 융합보안논문지
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• 제11권3호
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• pp.55-66
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• 2011

스마트폰(smartphone)은 PC와 같은 기능과 더불어 급 기능을 제공하는 휴대전화이다. 소비자들이 어떤 제품이나 서비스를 선택하는데 있어서 기본적으로 고려하는 것이 제품이나 서비스의 본질적 가치라고 한다면 네트워크 외부성 측면에서의 가치도 소비자의 제품 또는 서비스의 선택에 큰 영향을 미치고 있다. 지금까지 네트워크 외부성에 대한 경제학적 측면의 연구가 진행되어왔으나 마케팅적 측면에서의 연구는 부족한 편이다. 따라서 본 연구는 소비자(제품사용자)의 만족도와 지속적 사용의도에 스마트폰의 본질적 가치, 네트워크 가치 등의 제품의 가치가 영향을 미치는지 연구하였다. 전반적인 분석 결과에서 나타난 것은 전통적으로 모바일폰의 구입이나 재사용에서 중요하다고 할 수 있는 본질적 가치보다는 스마트폰에서는 네트워크 가치가 영향요인으로 파악되었다. 본 연구에서는 스마트폰의 기능을 많이 사용하고 또 잘 사용할 것으로 판단하여 20대 대학생들을 대상으로 연구하여 스마트폰의 가치가 사용만족도와 지속적 사용 의도를 파악할 수 있었다. 스마트폰의 통화료는 사용자만족도에 영향을 미치는 것으로 나타났다. 스마트폰의 액정도 사용자만족도에 영향을 미치는 것을 나타났고 크기와 무게는 스마트폰의 사용자만족도에 영향을 미치지 않는 것으로 나타났다. 액정의 해상도 및 디자인 등이 더 중요하다는 것으로 분석된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.275-280
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• 2005

The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2796-2813
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• 2014

This paper presents a new algorithm called the adaptive logarithmic increase and adaptive decrease algorithm (A-LIAD), which mainly addresses the Round-Trip Time (RTT) fairness problem in satellite networks with a very high propagation delay as an alternative to the current TCP congestion control algorithm. We defined a new increasing function in the fashion of a logarithm depending on the increasing factor ${\alpha}$, which is different from the other logarithmic increase algorithm adopting a fixed value of ${\alpha}$ = 2 leading to a binary increase. In A-LIAD, the ${\alpha}$ value is derived in the RTT function through the analysis. With the modification of the increasing function applied for the congestion avoidance phase, a hybrid scheme is also presented for the slow start phase. From this hybrid scheme, we can avoid an overshooting problem during a slow start phase even without a SACK option. To verify the feasibility of the algorithm for deployment in a high-speed and long-distance network, several aspects are evaluated through an NS-2 simulation. We performed simulations for intra- and interfairness as well as utilization in different conditions of varying RTT, bandwidth, and PER. From these simulations, we showed that although A-LIAD is not the best in all aspects, it provides a competitive performance in almost all aspects, especially in the start-up and packet loss impact, and thus can be an alternative TCP congestion control algorithm for high BDP networks including a satellite network.