• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.217-228
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• 2020

The ROK Army must detect the enemy's location and the type of artillery weapon to respond effectively at wartime. This paper proposes a radar positioning model by applying a scenario-based robust optimization method i.e., binary integer programming. The model consists of the different types of radar, its available quantity and specification. Input data is a combination of target, weapon types and enemy position in enemy's attack scenarios. In this scenario, as the components increase by one unit, the total number increases exponentially, making it difficult to use all scenarios. Therefore, we use partial scenarios to see if they produce results similar to those of the total scenario, and then apply them to case studies. The goal of this model is to deploy an artillery locating radar that maximizes the detection probability at a given candidate site, based on the probability of all possible attack scenarios at an expected enemy artillery position. The results of various experiments including real case study show the appropriateness and practicality of our proposed model. In addition, the validity of the model is reviewed by comparing the case study results with the detection rate of the currently available radar deployment positions of Corps. We are looking forward to enhance Korea Artillery force combat capability through our research.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.853-858
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• 2019

One of the methods which can enhance the efficiency of railroad network is construction of interconnection branch line for several route to share one railway. In Korea, this method already has been implemented or excuted as project level. This study suggests a network design model and a solution algorithm to choice most proper site to construction it and determine the priority of branch lines which can be considered in planning level, not project level. The model is a non-linear optimization program which minimize total cost-construction cost, operating cost and passengers' travel cost. The decision variables are a binary variable to explain whether construction or not and its direction and a integer variable of the frequencies of travel routes. The solution algorithm-problem solution and route choice and also the result of implementation for example network are suggested. This result can be more advanced after application in real network and calibration of parameters.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.108-114
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• 1989

Introducing and optimizing bit-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a $6{}8$ node system. Unlike many other neural network models, this model has stronger error correction capability for correlated images such as "6","8","3", and "9". The bit significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with Widrow-Hoff neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2007.11a
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• pp.164-169
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• 2007

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.225-228
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• 2001

DNA computing has been applied to the problem of getting an optimal solution since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems. This paper presents DNA coding method for finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms (GA). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses a tool of calculation or Information store with DNA molecules and four-type bases denoted by the symbols of A(Ademine), C(Cytosine), G(Guanine) and T(Thymine). The same operators, selection, crossover, mutation, are applied to the both DNA coding algorithm and genetic algorithms. The results show that the DNA based algorithm performs better than GA.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.93-96
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• 2003

The generalized knapsack problem, or gknap is the combinatorial optimization problem of optimizing a nonnegative linear functional over the integral hull of the intersection of a polynomially separable 0 - 1 polytope and a knapsack constraint. Among many potential applications, the knapsack, the restricted shortest path, and the restricted spanning tree problem are such examples. We establish some necessary and sufficient conditions for a gknap to admit a fully polynomial approximation scheme, or FPTAS, To do so, we recapture the scaling and approximate binary search techniques in the framework of gknap. This also enables us to find a condition that a gknap does not have an FP-TAS. This condition is more general than the strong NP-hardness.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.10
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• pp.27-35
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• 1997

In this paper, we propose a new scheme for the generation of partial products for VLSI fast parallel multiplier. It adopts a new encoding method which halves the number of partial products using 2x2 submultipliers and rearrangement of primitive partial products. The true 4-input CSA can be achieved with appropriate rearrangement of primitive partial products out of 2x2 submultipliers using the newly proposed theorem on binary number system. A 16bit x 16bit multiplier has been desinged using the proposed method and simulated to prove that the method has comparable speed and area compared to booth's encoding method. Much smaller and faster multiplier could be obtained with far optimization. The proposed scheme can be easily extended to multipliers with inputs of higher resolutions.

• Smart Structures and Systems
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• v.19 no.3
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• pp.279-288
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• 2017

Magnetorheological damper has received significant attention in recent years due to the reason that it can offer adaptability of active control devices without requiring the associated large power sources. In this paper, performance tests on a MR damper are carried out under different currents, excitation amplitudes and frequencies, the damping characteristics and energy dissipation capacity of the MR damper are analyzed. Elasto-plastic dynamic analysis on a space frame structure incorporated with MR dampers is conducted, and numerical analysis results show that MR dampers can significantly mitigate the structural vibration responses. Finally, the genetic algorithm with the improved binary crossover and mutation technique is adopted to optimize the arrangement of MR dampers. Numerical results show that dynamic responses of the optimal controlled structure are mitigated more effectively.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.412-430
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• 2003

In many practical problems in signal processing and control, the signal values are often restricted to belong to a finite number of levels. These questions are generally referred to as "finite alphabet" problems. There are many applications of this class of problems including: on-off control, optimal audio quantization, design of finite impulse response filters having quantized coefficients, equalization of digital communication channels subject to intersymbol interference, and control over networked communication channels. This paper will explain how this diverse class of problems can be formulated as optimization problems having finite alphabet constraints. Methods for solving these problems will be described and it will be shown that a semi-closed form solution exists. Special cases of the result include well known practical algorithms such as optimal noise shaping quantizers in audio signal processing and decision feedback equalizers in digital communication. Associated stability questions will also be addressed and several real world applications will be presented.