• International Journal of Contents
• /
• 제6권2호
• /
• pp.10-13
• /
• 2010

For applications that are grid enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization. When jobs communicate with each other, the internet, or with storage resources, an advanced scheduler could schedule them to minimize communications traffic or minimize the distance of the communications. We propose an intelligent load distribution algorithm to minimize communications traffic and distance of the communications using genetic algorithm. The experiments show the proposed load redistribution algorithm performs efficiently in the variance of load in grid environments.

• 한국지능시스템학회논문지
• /
• 제9권4호
• /
• pp.384-388
• /
• 1999

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) of nonlinear magnetic bearing system where the gains of FLC are on-line adapted according to the operating point. Specifically the systematic procedure via root locus technique is carried out for the selection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields not only maximization of stability boundary but also better control performance than a single operating point (without gain scheduling)fuzzy controller.

• 한국ITS학회 논문지
• /
• 제6권3호
• /
• pp.12-23
• /
• 2007

본 연구의 목적은 현행 버스 배차 간격 설정 시스템의 문제점을 제시하고, 이용자의 만족도와 버스 회사의 운영 효율성을 동시에 높일 수 있도록 이용자 만족도를 반영한 최적의 버스 배차 간격 설정 모형의 개발에 있다. 본 연구에서는 기존의 버스 운영비용, 승객 대기시간 비용 및 승객 통행시간 비용의 합으로 총 교통비용을 최소화하는 기존 모형에 이용자 만족도를 반영하여 최적 버스 배차 간격 설정 모형을 개발하였다. 본 연구는 최적 배차 간격 설정을 위해 선형계획법을 사용하였고, 이를 위해 선형계획법을 기반한 LINGO 프로그램을 사용하였다. 또한 부산의 일반 사례를 총 교통비용을 최소화하는 기존의 모형 및 현행 버스 배차간격 설정 시스템과 개발한 모형에 각각 적용하여 총 교통비용의 차이를 비교하였다.

• 한국산학기술학회논문지
• /
• 제12권3호
• /
• pp.1336-1340
• /
• 2011

서로 다른 응용을 위한 중복된 유비쿼터스 네트워크는 결국 어느 지점에서는 중복된 채널이 만들어 지며, 채널 혼선으로 인하여 전체 네트워크의 불안정뿐만 아니라, 보안 문제 그리고 기기 오작동 등의 심각한 문제를 야기 시킬 수 있다. 따라서 본 논문에서는 같은 지역내 다른 목적의 중복된 유비쿼터스용 ZigBee 네트워크간의 혼선 문제를 근본적으로 회피하기 위한 지능형 채널 할당 기법을 제안하고, 이러한 제안기법을 응용하여 무선 임베디드 시스템을 개발하였다.

• 대한전기학회논문지
• /
• 제43권3호
• /
• pp.492-503
• /
• 1994

In this study, the control and scheduling of the flexible manufacturing cell (FMC) is discussed, which can perform the mixed production and relieve the effect of machine failure. The control of the FMC isvery complex task due to the property of multiple jobs and the dynamically changing states. For effective control of proposed FMC, the hierarchical scheme is introduced and the functions of each levels are defined. Especially for the control functions of shop floor level and cell level, the intelligent scheduler is implemented. To show the efficiency of the intelligent scheduler, the production method fo the existing assembly lines was evaluated and compared with the proposed intelligent FMC method. The results from the production performance show that the proposed method is superior to the existing method in various performance indices.

• 한국지능시스템학회논문지
• /
• 제11권3호
• /
• pp.191-199
• /
• 2001

유전자알고리듬(Genetic Algorithm)은 확률적인 집단 탐색법이고 적응도함수의 형태에 관계없는 직접 탐색법이기 때문에 최근 최적화 방법으로 주목을 받고 있다. 본 논문에서는 Job-shop Schedule Problem에 대하여 교배방법으로 JOX를 사용하며, 효율적인 탐색을 위하여 탐색범위를 축소시키는 강제조작을 형질유전을 고려한 형질유전GT법을 제안하고, 세대교체에 있어 모집단의 다양성을 유지하기 위하여 집단 내에 동일한 개체를 배제하는 방법을 제안한다. 제안 알고리듬을 Fisher & Thompson의 FT10$\times$10 및 FT20$\times$5 문제에 적용하여 유효성을 실험적으로 검증한다.

• International Journal of Computer Science & Network Security
• /
• 제21권3호
• /
• pp.145-152
• /
• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• International Journal of Control, Automation, and Systems
• /
• 제6권6호
• /
• pp.915-927
• /
• 2008

Networked control systems(NCSs) have gained increasing attention in recent years due to their advantages and potential applications. The network Quality-of-Service(QoS) in NCSs always fluctuates due to changes of the traffic load and available network resources. To handle the network QoS variations problem, this paper presents an intelligent scheduling control method for NCSs, where the sampling period and the control parameters are simultaneously scheduled to compensate the effect of QoS variation on NCSs performance. For NCSs with network-induced delays and packet dropouts, a discrete-time switch model is proposed. By defining a sampling-period-dependent Lyapunov function and a common quadratic Lyapunov function, the stability conditions are derived for NCSs in terms of linear matrix inequalities(LMIs). Based on the obtained stability conditions, the corresponding controller design problem is solved and the performance optimization problem is also investigated. Simulation results are given to demonstrate the effectiveness of the proposed approaches.

• 지능정보연구
• /
• 제1권2호
• /
• pp.123-137
• /
• 1995

Most of solution methods in scheduling attempt to generate good solutions by either developing algorithms or heuristic rules. However, scheduling problems in the real world require considering more factors such as multiple objectives, different combinations of heuristic rules due to problem characteristics. In this respect, the traditional mathematical a, pp.oach showed limited performance so that new a, pp.oaches need to be developed. Expert system is one of them. When an expert system is developed for scheduling one of the most difficult processes faced could be knowledge acquisition on scheduling heuristics. In this paper we propose a method for the acquisition of knowledge on the selection of scheduling heuristics using Dempster-Shafer Theory(DST). We also show the examples in the multi-objectives environment.

• 지능정보연구
• /
• 제2권2호
• /
• pp.69-83
• /
• 1996

The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.