Earticle

이동통신망에서는 한정된 채널을 효과적으로 할당하기 위한 여러 연구들이 진행되고 있다. 이동국에서 호를 요청하면 교환국에서 각 기지국에 속한 이동국에 채널을 할당한다. 채널할당방식에는 크게 고정채널할당방식, 동적채널할당방식 그리고 이를 조합한 하이브리드방식이 있다. 본 논문에서는 채널을 할당 할 때 채널 간 간섭을 최소로 하고 채널을 할당하기까지의 시간과 횟수를 최소화하는 방안을 제안한다. 본 논문에서는 제안하고자 하는 알고리즘은 기지국, 제어국, 교환국 등 특정 장비당 채널수에 상관이 없이 범용으로 사용할 수 있는 시스템 모델을 기준으로 제안하였으며 기존의 통신사업자들이 통계를 근거로 채널을 할당하는 유사한 고정방식과 할당 시 기존의 방식과는 개선된 방식을 제시한다. 시뮬레이션을 통해 다른 방식과 비교 검토하여 제안 방식의 효율성을 검증한다.

If a mobile station requests a channel allocation in its mobile networks, the switching center assigns a channel to a mobile station that belongs to each base station. There are three kinds of channel allocation schemes; a fixed channel allocation, a dynamic channel allocation and a hybrid combination of these two forms. In assigning a good frequency, it is our intention to provide quality service to our customers as well as to use resources efficiently. This paper proposes methods of assigning frequencies that minimize interference between channels and that also minimize the amount of searching time involved. In this paper, we propose an algorithm to per specific equipment, regardless of the number of channels that can be used as a general-purpose system, such as base stations, control stations, central office model is proposed, the existing operators manner similar to the fixed channel allocation based statistics and assigned when the conventional method and the improved method is proposed. Different ways and compared via simulations to verify the effectiveness of the proposed approach.

This paper presents the hardware-based genetic algorithm, written in VHDL. Due to parallel computation and no function call overhead, a hardware-based GA advantage a speedup over a software-based GA. The proposed architecture is constructed on a field-programmable gate arrays, which are easily reconfigured. Since a general-purpose GA requires that the fitness function be easily changed, the hardware implementation must exploit the reprogrammability.

현재 많은 조직에서 위험 분석을 통해 현재 자신들의 보안상의 문제점을 파악하고 그에 따른 대책을 적용하고 있다. 기존의 보안 대책을 평가하고 새로운 보안 대책을 적용하는 데 않은 어려움이 따르므로 본 연구에서는 Simple Genetic Algorithm을 이용하여 현재 조직의 상황에 적절한 보안 대책을 제시할 수 있는 방법을 연구하고자 한다.

In this study, a multi-step optimization technique combined with a simple genetic algorithm is introduce to the structural design optimization of a high speed machining center. In this case, the design problem is to find out the best design variables which minimize the static compliance, the dynamic compliance, and the weight of the machine structure and meet some design constraints simultaneously. Dimensional thicknesses of the thirteen structural members along the static force loop of the machine structure are adopted as design variables. The first optimization step is a static design optimization, in which the static compliance and the weight are minimized under some dimensional and safety constraints. The second step is a dynamic design optimization, where the dynamic compliance and the weight are minimized under the same constraints. After optimization, the weight of the moving body was reduced to 9.1% of the initial design respectively. Both static and dynamic compliances of the optimum design are also in the feasible range even thought they were slightly increased than before.

In this study, a multi-step optimization technique combined with a simple genetic algorithm is introduced in order to minimize the static compliance, the dynamic compliance, and the weight of a high speed machining center simultaneously. Dimensional thicknesses of the eight structural members on the static force loop are adopted as design variables. The first optimization step is a static design optimization, in which the static compliance and the weight are minimized under some dimensional and safety constraints. The second step is a dynamic design optimization, where the dynamic compliance and the weight are minimized under the same constraints. After optimization, the weight of the moving body only was reduced to 57.75% and the weight of the whole machining center was reduced to 46.2% of the initial design respectively. Both static and dynamic compliances of the optimum design are also in the feasible range even though they were slightly increased than before.