• 경영과학
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• 제18권1호
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• pp.41-54
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• 2001

In this paper, we proposed a method to determine optimal wave source for mobile telephone communication. The approach is based on wave propagation simulation. Given a wave source we can determine wave propagation effects on every surfaces of wave simulation environment. The effect is evaluated as a cost function while the source’s position x, y, z work as variables for a parameter optimization. Wave propagated 3 dimensional space generates reflected waves whenever it hits boundary surface, it receives multiple waves which are reflected from various boundary surfacers in space. Three algorithms being implemented in this paper are based on a raytracing theory. If we get 3 dimensional geometry input as well as wave sources, we can compute wave propagation effects all over the boundary surfaces. In this paper, we present a new approach to compute wave propagation. First approach is tracing wave from a source. Source is modeled as a sphere casting vectors into various directions. This approach has limit in computing necessary wave propagation effects on all terrain surfaces. The second approach proposed is tracing wave backwards : tracing from a wave receiver to a wave source. For this approach we need to allocate a wave receiver on every terrain surfaces modeled, which requires enormous amount of computing time. But the second approach is useful for indoor wave propagation simulation. The last approach proposed in this paper is tracing sound by geometric computation. We allow direct, 1-relfe tion, and 2-reflection propagation. This approach allow us to save in computation time while achieving reasonable results. but due to the reflection limitaion, this approach works best in outdoor environment.

• Journal of Communications and Networks
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• 제12권4호
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• pp.334-345
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• 2010

In this paper, we study optimal tradeoffs of achievable throughput versus consumed power in wireless ad-hoc networks formed by a collection of multiple antenna nodes. Relying on adaptive modulation and/or dynamic channel coding rate allocation techniques for multiple antenna systems, we examine the maximization of throughput under power constraints as well as the minimization of transmission power under throughput constraints. In our examination, we also consider the impacts of enforcing quality of service requirements expressed in the form of channel coding block loss constraints. In order to properly model temporally correlated loss observed in fading wireless channels, we propose the use of finite-state Markov chains. Details of fading statistics of signal-to-interference-noise ratio, an important indicator of transmission quality, are presented. Further, we objectively inspect complexity versus accuracy tradeoff of solving our proposed optimization problems at a global as oppose to a local topology level. Our numerical simulations profile and compare the performance of a variety of scenarios for a number of sample network topologies.

• 대한조선학회논문집
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• 제47권1호
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• pp.58-66
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• 2010

Topology design optimization is a method to determine the optimal distribution of material that yields the minimal compliance of structures, satisfying the constraint of allowable material volume. The method is easy to implement and widely used so that it becomes a powerful design tool in various disciplines. In this paper, a large-scale topology design optimization method is developed using the efficient adjoint sensitivity and optimality criteria methods. Parallel computing technique is required for the efficient topology optimization as well as the precise analysis of large-scale problems. Parallelized finite element analysis consists of the domain decomposition and the boundary communication. The preconditioned conjugate gradient method is employed for the analysis of decomposed sub-domains. The developed parallel computing method in topology optimization is utilized to determine the optimal structural layout of human powered vessel.

• Journal of Advanced Marine Engineering and Technology
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• 제40권2호
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• 인터넷정보학회논문지
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• 제22권6호
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2009년도 춘계 종합학술대회 논문집
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• pp.17-22
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• 2009

다빈치 프로세서는 임베디드 멀티미디어 응용 구현 프로세서로 많이 사용된다. ARM 9 코어 및 DSP 코어의 듀얼 코어로 되어 있어 ARM 코어 에서는 주변 장치 제어, 비디오 입출력 제어, 네트워킹 등을 지원하며, DSP 코어는 보다 효율적인 디지털 신호 처리 연산을 지원한다. 본 논문에서는 본 저자들의 연구실에서 만들고 있는 다빈치 프로세서 기반의 스마트 카메라에 있어서 객체 추적 알고리즘의 최적 구현 방안 노력을 기술한다. 본 논문의 스마트 카메라는 입력 영상에서 관심 객체를 검출하고 이를 추적하며, 분류하고 감시구역에 침입한 경우 이를 IP 프로토콜로 원격 클라이언트에게 통보하는 기능을 보유한다. 객체 추적은 전방 마스크 추출, 전방 마스크 교정, 연결 요소 레이블링, 블롭 지역 계산 등 계산량이 많은 절차들로 구성되어 효율적으로 구현되지 않으면 실시간 처리가 힘들다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2422-2443
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• 2020

This paper studies a single-user Mobile Edge Computing (MEC) system where mobile device (MD) includes an application consisting of multiple computation components or tasks with dependencies. MD can offload part of each computation-intensive latency-sensitive task to the AP integrated with MEC server. In order to accomplish the application faultlessly, we calculate out the optimal task offloading strategy in a time-division manner for a predetermined execution order under the constraints of limited computation and communication resources. The problem is formulated as an optimization problem that can minimize the energy consumption of mobile device while satisfying the constraints of computation tasks and mobile device resources. The optimization problem is equivalently transformed into solving a nonlinear equation with a linear inequality constraint by leveraging the Lagrange Multiplier method. And the proposed dual Bi-Section Search algorithm Bi-JOTD can efficiently solve the nonlinear equation. In the outer Bi-Section Search, the proposed algorithm searches for the optimal Lagrangian multiplier variable between the lower and upper boundaries. The inner Bi-Section Search achieves the Lagrangian multiplier vector corresponding to a given variable receiving from the outer layer. Numerical results demonstrate that the proposed algorithm has significant performance improvement than other baselines. The novel scheme not only reduces the difficulty of problem solving, but also obtains less energy consumption and better performance.

• 한국컴퓨터정보학회논문지
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• 제19권8호
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• pp.11-18
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• 2014

본 논문은 무감독학습을 통한 데이터 분류기법인 ART에서 퍼지이론을 이용한 결합형 데이터 분류 방법을 제안한다. 무감독학습기법 기반의 데이터 분류 기술은 분류기술의 향상의 장점이 있지만, 처리성능이 저하된다는 단점이 있다. 민첩성 있는 대용량데이터 처리와 분류인식률을 만족하는 최적의 임계값 결정기법이 필요하지만, 이는 불확실성이 많이 따르기 때문에 두 가지를 고려하여 상호보완 할 수 있는 처리기법이 필요하다. 제안하는 기법은 무감독학습을 하기 위해 퍼지매개변수와 퍼지 규칙을 설계하여 최적의 임계값을 도출한다. 제안하는 기법의 성능평가를 위해 클라우드 컴퓨팅환경에서 G 단백질 연결 수용체(G protein coupled receptor, GPCR)데이터를 이용하여 실험하였으며, 실험결과는 높은 인식률과 낮은 처리시간을 통해 결합형 데이터 분류에 효과적임을 입증하였다.

• 대한기계학회논문집B
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• 제27권2호
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• pp.155-162
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• 2003

Design optimization of a transonic compressor rotor (NASA rotor 37) using response surface method and three-dimensional Navier-Stokes analysis has been carried out in this work. Baldwin-Lomax turbulence model was used in the flow analysis. Three design variables were selected to optimize the stacking line of the blade. Data points for response evaluations were selected by D-optimal design, and linear programming method was used for the optimization on the response surface. As a main result of the optimization, adiabatic efficiency was successfully improved. It is also found that the design process provides reliable design of a turbomachinery blade with reasonable computing time.

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.116-124
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• 1999

In most conventional design optimization of dynamic system, design sensitivities are utilized. However, design sensitivities based optimization method has numbers of drawback. First, computing design sensitivities for dynamic system is mathematically difficult, and almost impossible for many complex problems as well. Second, local optimum is obtained. On the other hand, Genetic Algorithm is the search technique based on the performance of system, not on the design sensitivities. It is the search algorithm based on the mechanics of natural selection and natural genetics. GA search, differing from conventional search techniques, starts with an initial set of random solutions called a population. Each individual in the population is called a chromosome, representing a solution to the problem at hand. The chromosomes evolve through successive iterations, called generations. As the generation is repeated, the fitness values of chromosomes were maximized, and design parameters converge to the optimal. In this study, Genetic Algorithm is applied to the actual dynamic optimization problems, to determine the optimal design parameters of the dynamic system.