• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1196-1204
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• 2022

In this paper, we propose an optimization algorithm that maximizes the network lifetime in wireless ad-hoc networks using the broadcast transmission method. The optimization algorithm proposed in this paper applies tabu search algorithm, a metaheuristic method that improves the local search method using the memory structure. The proposed tabu search algorithm proposes efficient encoding and neighborhood search method to the network lifetime maximization problem. By applying the proposed method to design efficient broadcast routing, we maximize the lifetime of the entire network. The proposed tabu search algorithm was evaluated in terms of the energy consumption of all nodes in the broadcast transmission occurring in the network, the time of the first lost node, and the algorithm execution time. From the performance evaluation results under various conditions, it was confirmed that the proposed tabu search algorithm was superior to the previously proposed metaheuristic algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.52-65
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• 1997

For the degradation of severe noise and ill-conditioned blur the optimization function has the solution spaces which have many local optima around global solution. General restoration methods such as inverse filtering or gradient methods are mainly dependent on the properties of degradation model and tend to be isolated into a local optima because their convergences are determined in the convex space. Hence we introduce genetic algorithm as a searching method which will search solutions beyond the convex spaces including local solutins. In this paper we introudce improved evaluation square error) and fitness value for gray scaled images. Finally we also proposed the local fine tunign of window size and visit number for delicate searching mechanism in the vicinity of th global solution. Through the experiental results we verified the effectiveness of the proposed genetic operators and evaluation function on noise reduction over the conventional ones, as well as the improved performance of local fine tuning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.9-15
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• 2007

We propose the fast full search algorithm that reduces the computation of the block matching algorithm which is used for motion estimation of the video coding. Since the conventional spiral search method starts searching at the center of the search window and then moves search point to estimate the motion vector pixel by pixel, it is good for the slow motion pictures. However the proposed method is good for the fast and slow motion because it estimates the motion in the new search order after partitioning the search window. Also, when finding the motion vector, this paper presents the method that reduces the complexity by computing the matching error in the order which is determined by local image complexity. The proposed algorithm reduces the computation up to 99% for block matching error compared with the conventional spiral full search algorithm without any loss of image quality.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.135-142
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• 2004

The severe frontal crash tests are NCAP with belted occupant at 35mph and FMVSS 208 with unbelted occupant at 25mph, This paper describes the design process of occupant protection systems, airbag and seat belt, under the two tests. In this study, NCAP simulations are performed by Monte Carlo search method and cluster analysis. The Monte Carlo search method is a global optimization technique and requires execution of a series of deterministic analyses, The procedure is as follows. 1) Define the region of interest 2) Perform Monte Carlo simulation with uniform distribution 3) Transform output to obtain points grouped around the local minima 4) Perform cluster analysis to obtain groups that are close to each other 5) Define the several feasible design ranges. The several feasible designs are acquired and checked under FMVSS 208 simulation with unbelted occupant at 25mph.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.4
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• pp.76-85
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• 1998

Recently, Genetic Algorithm(GA) is widely adopted into a search procedure for structural optimization, which is a stochastic direct search strategy that mimics the process of genetic evolution. This methods consist of three genetics operations maned selection, crossover and mutation. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA, being zero-order method, is very simple. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher probability of converge to global optimum compared to traditional techniques which take one-point search method. In this study, a method of finding the optimum values of suspension parameters is proposed by using the GA. And vehicle is modelled as planar vehicle having 5 degree-of-freedom. The generalized coordinates are vertical motion of passenger seat, sprung mass and front and rear unsprung mass and rotate(pitch) motion of sprung mass. For rapid converge and precluding local optimum, share function which distribute chromosomes over design bound is introduced. Elitist survival model, remainder stochastic sampling without replacement method, multi-point crossover method are adopted. In the sight of the improvement of ride comfort, good result can be obtained in 5-D.O.F. vehicle model by using GA.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.74-76
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• 2007

A hybrid optimization method is proposed for cogging torque reducing in BLDC motor. The proposed hybrid optimization method comprises a response surface method (RSM) and a gradient search method (GSM). The RSM is effective and global method in optimization problem but having large approximation error. The GSM is accurate and fast search method for optimal solution but having local behavior. To reduce approximation error and computation time a hybrid method (RSM+GSM) is proposed method. To illustrate the effectiveness of the proposed method, a comparison between conventional RSM and the proposed hybrid method is made. A simulation results verify that the hybrid method can achieve favorable design performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.91-97
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• 2002

This paper describes a heuristic search technique carrying out collision avoidance for Autonomous Underwater Vehicles(AUVs). Fuzzy relational products are used as the mathematical implement for the analysis and synthesis of relations between obstacles that are met in the navigation environment and available candidate nodes. In this paper, we propose a more effective evaluation function that reflects the heuristic information of domain experts on obstacle clearance, and an advanced heuristic search method performing collision avoidance for AUVs. The search technique adopts fuzzy relational products to conduct path-planning of intelligent navigation system. In order to verify the performance of proposed heuristic search, it is compared with $A^*$ search method through simulation in view of the CPU time, the optimization of path and the amount of memory usage.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.443-448
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• 2013

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.