• The Journal of Korea Robotics Society
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• v.11 no.3
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• pp.127-132
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• 2016

For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.12
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• pp.409-414
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• 2015

The energy conservation is the most important subject for long run operation of the sensor nodes with limited power resources. Clustering is one of most energy efficient technique to grouped the sensor nodes into distinct cluster. But, in a cluster based WSN, CHs and gateways bear an extra work load to send the processed data to the sink. The inappropriate cluster formation may cause gateways overloaded and may increase latency in communication. In this paper, we propose a novel load balanced clustering model for improving energy efficiency and giving a guarantee of long network lifetime. We show the result of performance measurement experiments that designs using a branch and bound algorithm and a multi-start local search algorithm to compare with the existing load balanced clustering model.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.121-129
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• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.

• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.77-90
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• 2000

Real-Time verification is a procedure that verifies the correctness of specification related to requirement in time as well as in logic. One serious problem encountered in the verification task is that the state space grows exponentially owing to the unboundedness of time, which is termed the state space explosion problem. In this paper, we propose a real-time verification technique checking the correctness of specification by showing that a system model described in timed automata is equivalent to the characteristic of system property specified in timed modal-mu calculus. For this, we propose a local model checking method based on the value of the formula in initial state with constructing product graph concerned to only the nodes needed for verification process. Since this method does not search for every state of system model, the state space is reduced drastically so that the proposed method can be applied effectively to real-time system verification.

• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.143-155
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• 2001

Generally, truss design has been determined by the designer's experience and intuition. But if we perform the most economical structural design we must consider not only cross-sections of members but also configurations(howe, warren and pratt types etc.) of single truss as the number of panel and truss height. The purpose of this study is to develope automated optimum design techniques for steel truss structures considering cross-sections of members and shape of trusses simultaneously. As the results, it could be possible to find easily the optimum solutions subject to design conditions at the preliminary structural design stage of the steel truss structures. In this study, the objective function is expressed as the whole member weight of trusses, and the applied constraints are as stresses, slenderness ratio, local buckling, deflection, member cross-sectional dimensions and truss height etc. The automated optimum design algorithm of this study is divided into three-level procedures. The first level on member cross-sectional optimization is performed by the sequential unconstrained minimization technique(SUMT) using dynamic programming method. And the second level about truss height optimization is applied for obtaining the optimum truss height by three-equal interval search method. The last level of optimization is applied for obtaining the optimum panel number of truss by integer programming method. The algorithm of multi-level optimization programming technique proposed in this study is more helpful for the economical design of plane trusses as well as space trusses.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.181-197
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• 2003

Notion Compensation(MC) technique using Sub-Band Coding with the hierarchical structure is efficient to estimate real motion. In the hierarchical pyramid method, low-band MC pyramid method is popular, where the upper layer estimate the glover motion and next lower layer estimate the local motion. The low-band MC pyramid scheme has two problems. First, because the quantization errors at lower layer are accumulated when using coding and quantizing, it is impossible to search the exact Motion Vector(MV) Second, because of the top-down search problem in the hierarchical structure, MV mismatch in upper layer causes serious MV in lower layer So. we propose new hierarchical MC pyramid method based on edge classification. In this Paper, we show that the performance of proposed Pass-band motion compensation pyramid technique is better than low-band motion compensation pyramid. Also, in the pyramid motion estimation, we propose initial MV estimation scheme based on the edge-pattern classification. As a result, we find that PSNR was increased.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1048-1055
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• 2002

The problem of generating torch paths for 2D laser cutting of a stock plate nested with a set of free-formed parts is investigated. The objective is to minimize the total length of the torch path starting from a blown depot, then visiting all the given Parts, and retuning back to the depot. A torch Path consists of the depot and Piercing Points each of which is to be specified for cutting a part. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem To solve the problem, a hybrid genetic algorithm is proposed. In order to improve the speed of evolution convergence, the algorithm employs a genetic algorithm for global search and a combination of an optimization technique and a genetic algorithm for local optimization. Traditional genetic operators developed for continuous optimization problems are used to effectively deal with the continuous nature of piercing point positions. Computational results are provided to illustrate the validity of the proposed algorithm.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2034-2042
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• 2006

In this study, a complete 3D surface reconstruction method is proposed based on the concept that the vertices, of surface model can be completely matched to the unstructured point cloud. In order to generate the initial mesh model from the point cloud, the mesh subdivision of bounding box and shrink-wrapping algorithm are introduced. The control mesh model for well representing the topology of point cloud is derived from the initial mesh model by using the mesh simplification technique based on the original QEM algorithm, and the parametric surface model for approximately representing the geometry of point cloud is derived by applying the local subdivision surface fitting scheme on the control mesh model. And, to reconstruct the complete matching surface model, the insertion of isolated points on the parametric surface model and the mesh optimization are carried out. Especially, the fast 3D surface reconstruction is realized by introducing the voxel-based nearest-point search algorithm, and the simulation results reveal the availability of the proposed surface reconstruction method.

• Genomics & Informatics
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• v.4 no.4
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• pp.173-181
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• 2006

Pattern finding is one of the important tasks in a protein or DNA sequence analysis. Alignment is the widely used technique for finding patterns in sequence analysis. BLAST (Basic Local Alignment Search Tool) is one of the most popularly used tools in bio-informatics to explore available DNA or protein sequence databases. BLAST may generate a huge output for a large sequence data that contains various sequence patterns. However, BLAST does not provide a tool to summarize and analyze the patterns or matched alignments in the BLAST output file. BLAST lacks of general and robust parsing tools to extract the essential information out from its output. This paper presents a pattern summary system which is a powerful and comprehensive tool for discovering pattern structures in huge amount of sequence data in the BLAST. The pattern summary system can identify clusters of patterns, extract the cluster pattern sequences from the subject database of BLAST, and display the clusters graphically to show the distribution of clusters in the subject database.