• Journal of Korean Institute of Industrial Engineers
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• v.19 no.4
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• pp.97-104
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• 1993

This paper deals with the design problem of a man-on-board(MOB) storage and retrieval warehousing system which is suitable for storing items of small size and light weight. It is assumed that the operator carries out a sequence of retrieving(or storing) operations traveling on a specially designed truck. Considering the operating characteristics of the man-on-board system, an optimal design model is developed in which the investment and maintenance costs of the system are minimized over a time horizon satisfying a set of constructional restrictions. The model is formulated as a nonlinear integer program and a search algorithm is proposed to find an optimum solution.

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.67-72
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• 2000

Particularly based on the signal bias removal(SBR) algorithm for compensating the corrupted speech, this paper presents a new algorithm which is independent of environments, minimizes the amount of computation, and is readily applicable to the conventional recognition system. To this end, a multiple-bias algorithm and a partial codebook search algorithm have been added to the conventional SBR algorithm. The simulation results show that combining the two algorithms proposed in this paper provides a reduction of computation time to 1/8 times as well as an improvement of the recognition rate from 77.58% of the conventional system to 81.32%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1873-1893
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• 2023

The development of lightweight, low energy and small-sized sensors incorporated with the wireless networks has brought about a phenomenal growth of Wireless Sensor Networks (WSNs) in its different fields of applications. Moreover, the routing of data is crucial in a wide number of critical applications that includes ecosystem monitoring, military and disaster management. However, the time-delay, energy imbalance and minimized network lifetime are considered as the key problems faced during the process of data transmission. Furthermore, only when the functionality of cluster head selection is available in WSNs, it is possible to improve energy and network lifetime. Besides that, the task of cluster head selection is regarded as an NP-hard optimization problem that can be effectively modelled using hybrid metaheuristic approaches. Due to this reason, an Improved Coyote Optimization Algorithm-based Clustering Technique (ICOACT) is proposed for extending the lifetime for making efficient choices for cluster heads while maintaining a consistent balance between exploitation and exploration. The issue of premature convergence and its tendency of being trapped into the local optima in the Improved Coyote Optimization Algorithm (ICOA) through the selection of center solution is used for replacing the best solution in the search space during the clustering functionality. The simulation results of the proposed ICOACT confirmed its efficiency by increasing the number of alive nodes, the total number of clusters formed with the least amount of end-to-end delay and mean packet loss rate.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.1-9
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• 2023

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.299-306
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• 2012

To fulfill the complex user requirement, composition web service comprised of existing services is considered from the efficient and reusable point of view instead of making entirely new web service. However, with the growing the number of web services which provide the same functionality but differ in quality value, the service composition becomes a decision problem on which component services should be selected such that end-to-end QoS constraints by the client and overall QoS of the composition service are satisfied. QoS of service aspects is a determinant factor for selecting the services, since the performance of the composed service is determined by the performance of the involved component web service. In this paper, hybrid genetic algorithm is presented to select component services to take part in the QoS-aware composition. The local search method is used to be combined with the genetic algorithm to improve the individuals (component service) in population as well as composed service. The paper also presents a set of experiments conducted to evaluate the efficiency of selection algorithm using the real web service data.

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.1-11
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• 2003

In this paper, we focus an optimal policy focus optimal class of (s, S) inventory control systems. To this end, we use the perturbation analysis and apply a stochastic optimization algorithm to minimize the average cost over a period. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. Our simulation results indicate that the optimal estimates of s and S obtained from a stochastic optimization algorithm are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and review period. Another directions involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.624-632
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• 2022

Relationship networks such as an interpersonal relationship network or navigation route search can be expressed in graph form. However, as the amount of data increase, there is a problem that it is difficult to search for the desired data when it is displayed on one screen. In this paper, we propose a visualization method for searching for people, searching for the shortest path between people, and using graphs to express an interpersonal relationship network with many nodes. Unlike the search for the shortest path in the routing table, the shortest path in the interpersonal relationship network should be changeable according to the intension or importance of the researcher or user who is analyzing it. To this end, the BFS algorithm was modified to apply the characteristics of the interpersonal relationship network. For the verification of the results, the data in the character relationship information of the Korean Classics DB in the Korean Classics Translation Institute was used.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3042-3044
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• 1999

This paper presents a method for solving the path planning problem for robot manipulators. The technique allows manipulators to move from a specified starting point to a goal without colliding with objects in two dimensional environment. Approximate cell decomposition with a greedy depth-first search algorithm is used to guide the end effector though Cartesian space and genetic algorithms are used to solve the joint variable for the robot manipulators.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.406-411
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• 2005

In this paper, we present the area-based Q-learning to search a target object using multiple robot. To search the target in Markovian space, the robots should recognize their surrounding at where they are located and generate some rules to act upon by themselves. Under area-based Q-learning, a robot, first of all, obtains 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to search for a target object while navigating in a unknown hallway where some obstacles were placed. In the end of this paper, we presents the results of three algorithms - a random search, area-based action making (ABAM), and hexagonal area-based Q-teaming.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4167-4173
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• 2010

ICT education guidelines revised in 2005 reinforce computer science elements such as algorithm, data structure, and programming covering all schools. It means that goal of computer education is improving problem-solving abilities not using of commercial software. So this paper suggests problem-solving method of maze escape through robots' cooperation in an effort of learning these elements. Problems robots should solve are first-search and role-exchange. First-search problem is that first robot searches maze and send informations about maze to the second robot in real time. Role-exchange problem is that first robot searches maze, but loses its function at any point. At this time second robot takes a role of first robot and performs first robot's missions to the end. To solve these two problems, it goes through four steps; problem analysis, algorithm description, flowchart and programming. Additional effects of our suggestion are chance of cooperation among students and use of queue in data structure. Further researches are use of more generalized mazes, application to real field and a talented curriculum.