• Management Science and Financial Engineering
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• 제11권2호
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• pp.85-103
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• 2005

In this paper, we consider the routing and wavelength assignment problem in survivable WDM transport network without wavelength conversion. We assume the single-link failure and a path protection scheme in optical layer. When a physical network and a set of working paths are given, the problem is to select a link-disjoint protection path for each working path and assign a wavelength for each working and protection path. We give an integer programming formulation of the problem and propose an algorithm to solve it. Though the formulation has exponentially many variables, we solve the linear programming relaxation of it by using column generation technique. We devise a branch-and price algorithm to solve the column generation problem. After solving the linear programming relaxation, we apply a variable fixing procedure combined with the column generation to get an integral solution. We test the proposed algorithm on some randomly generated data and test results show that the algorithm gives very good solutions.

• Journal of Communications and Networks
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• 제8권1호
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• pp.85-92
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• 2006

In this paper, we study a wireless cellular network design problem. It consists of selecting the location of the base station controllers and mobile service switching centres, selecting their types, designing the network into a tree-topology, and selecting the link types, while considering the location and the demand of base transceiver stations. We propose a constraint programming model and develop a heuristic combining local search and constraint programming techniques to find very good solutions in a reasonable amount of time for this category of problem. Numerical results show that our approach, on average, improves the results from the literature.

• 대한전자공학회논문지
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• 제11권2호
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• pp.1-4
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• 1974

큰 규모의 계통이나 회로망의 해석익 있어서 0이 대부분 포함되어 있는 행렬을 반전하여 해를 구하는 것은 대단히 비능룰적이다. 이러한 계통을 Sparse행렬을 이용하여 풀면 계산시간이 적게 들고 기억용량이 감소되며 둥근(round-off)오차를 줄일 수 있다. 본논문은 Sparse 행렬를 이용하여 회로망을 푸는 방법고ㅘ 전산 프로그래밍을 제공한다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.660-667
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• 2003

In this study, a Multiple Objective Mixed Integer Programming (MOMIP) Model is developed for sewer rehabilitation planning by considering cost, inflow/infiltration. A sewer rehabilitation planning model is required to decide the economic life of the sewer by considering trade-off between cost and inflow/infiltration. And it is required to find the optimal rehabilitation timing, according to the cost effectiveness of each sewer rehabilitation within the budget. To develop such a model, a multiple objective mixed integer programming model is formulated based on network flow optimization. The network is composed of state nodes and arcs. The state nodes represent the remaining life and the arcs represent the change of the state. The model consider multiple objectives which are cost minimization and minimization of inflow/infiltration. Using the multiple objective optimization, the trade-off between the cost and inflow/infiltration is presented to the planner so that a proper sewer rehabilitation plan can be selected.

• 대한교통학회지
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• 제5권1호
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• pp.47-58
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• 1987

A commodity distribution problem with intertemporal storage facilities and dynamic transportation networks is proposed. mathematical integer programming methods and multiobjective programming techniques are used in the model formulation. Dynamic characteristics of commodity distribution problems are taken into account in the model formulation. storage facility location problems and transportation link addition problems are incorporated into the intertemporal multicommodity distribution problem. The model is capable of generating the most efficient and rational commodity distribution system. Therefore it can be utilized to provided the most effective investment plan for the transportation infrastructure development as well as to evaluate the existing commodity distribution system. The model determines simultaneously the most efficient locations, sizes, and activity levels of storage facilities as well as new highway links. It is extended to multiobjective planning situations for the purpose of generating alternative investment plans in accordance to planning situations. sine the investment in transportation network improvement yields w\several external benefits for a regional economy, the induced benefit maximization objective is incorporated into the cost minimization objective. The multiobjective model generates explicitly the trade-off between cost savings and induced benefits of the investment in transportation network improvement.

• 한국경영과학회지
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• 제17권1호
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• pp.3-16
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• 1992

We present a modified Tank and Hopfield's neural network model for solving Linear Programming problems. We have found the fact that the Tank and Hopfield's neural circuit for solving Linear Programming problems has some difficulties in guaranteeing convergence, and obtaining both the primal and dual optimum solutions from the output of the circuit. We have identified the exact conditions in which the circuit stops at an interior point of the feasible region, and therefore fails to converge. Also, proper scaling of the problem parameters is required, in order to obtain a feasible solution from the circuit. Even after one was successful in getting a primal optimum solution, the output of the circuit must be processed further to obtain a dual optimum solution. The modified model being proposed in the paper is designed to overcome such difficulties. We describe the modified model and summarize our computational experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4453-4468
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• 2015

This paper investigates optimization-based base station (BS) placement. An optimization model is defined and the BS placement problem is transformed to a lexicographical stratified programming (LSP) model for a given trajectory, according to different accuracy requirements. The feasible region for BS deployment is obtained from the positioning system requirement, which is also solved with signal coverage problem in BS placement. The LSP mathematical model is formulated with the average geometric dilution of precision (GDOP) as the criterion. To achieve an optimization solution, a tolerant factor based complete stratified series approach and grid searching method are utilized to obtain the possible optimal BS placement. Because of the LSP model utilization, the proposed algorithm has wider application scenarios with different accuracy requirements over different trajectory segments. Simulation results demonstrate that the proposed algorithm has better BS placement result than existing approaches for a given trajectory.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• 전기학회논문지
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• 제67권6호
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.