• ETRI Journal
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• v.41 no.3
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• pp.326-336
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• 2019

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a wide-spread research problem. In this paper, we propose a demand-based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to-be-charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K-means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on-demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.6
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• pp.726-732
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• 2013

It is required for UGV(Unmanned Ground Vehicle) to have a LPP(Local Path Plan) component which generate a local path via the center of road by analyzing binary map to travel autonomously unpaved road in rough environment. In this paper, we present the method of boundary estimation for unpaved road and a local path planning method based on RANGER algorithm using the estimated boundary. In specially, the paper presents an approach to estimate road boundary and the selection method of candidate path to minimize the problem of zigzag driving based on Bayesian probability reasoning. Field test is conducted with scenarios in rough environment in which bush, tree and unpaved road are included and the performance of proposed method is validated.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.736-741
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• 2005

The highly popular algorithm to determine routing path for the multi-hopping wireless sensor network is DSR(Dynamic Source Routing), which is one of the Demand-Driven way to makes the route only when there is a request for sending data. However, because DSR attaches the route's record on the sending packet, the bigger number of sensor node is, the heavier packet in DSR becomes. In this paper, we try to propose the new optimal routing path selecting algorithm which does not make the size of packet bigger by using proper routing table even though the number of sensor node increases, and we try to show our algorithm is more stable and reliable because it is based on the cost function considering some network resources of each sensor node such as power consumption, mobility, traffic in network, distance(hop) between source and destination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1774-1794
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• 2024

In the field of environmental sensing, it is necessary to develop radio planning techniques for the next generation Internet of Things (IoT) networks over mixed terrains. Such techniques are needed for smart remote monitoring of utility supplies, with links situated close to but out of range of cellular networks. In this paper, a three-dimension (3-D) geometric optimization algorithm is proposed, considering the positions of edge IoT devices and antenna coupling factors. Firstly, a multi-level single linkage (MLSL) iteration method, based on geometric objectives, is derived to evaluate the data rates over ISM 915 MHz channels, utilizing optimized power-distance profiles of continuous waves. Subsequently, a federated learning (FL) data selection algorithm is designed based on the 3-D geometric positions. Finally, a measurement example is taken in a meadow biome of the Mexican Colima district, which is prone to fluvial floods. The empirical path loss model has been enhanced, demonstrating the accuracy of the proposed optimization algorithm as well as the possibility of further prediction work.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.133-140
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• 2012

This paper suggests an algorithm that can shorten the complexity $O(n^2)$ of Dijkstra algorithm that is applied to the shortest path searching in real-time GPS Navigation System into an up-to-date O(n). Dijkstra algorithm manipulates the distance of the minimum length path by visiting all the nodes from the starting node. Hence, it has one disadvantage of not being able to provide the information on the shortest path every second, in a city that consists of sophisticated roads, since it has to execute number of node minus 1. The suggested algorithm, firstly, runs by means of organizing the set of out-neighbourhood nodes at each level of the tree, and root node for departure node. It also uses a method of manipulating the distance of the minimum path of all out-neighborhoods and interior of the out-neighborhoods. On applying the suggested algorithm to two sophisticated graphs consisted of bi-direction and uni-direction, we have succeeded to obtain the distance of the minimum length path, just as same as Dijkstra algorithm. In addition, it has an effect of shortening the time taken 4 times from number of node minus1 to number of level minus 1. The satisfaction of the drivers can be increased by providing the information on shortest path of detour, every second, when occurs any rush hour or any traffic congestion due to car accident, by applying this suggested algorithm to the real-time GPS system.

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.93-107
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• 1996

A flexible flow line(FFL) consists of several groups of identical machines. All work-orders flow along the same path through successive machine groups. Thus, we emphasized the balancing of workloads between machine groups in order to maximize total productivity. On the other hand, many different types of work-orders, in varying batch or lot sizes, are produced simultaneously. The mix of work-orders, their lot sizes, and the sequence in which they are produced affect the amount of workload. However, the work-orders and their lot sizes are prefixed and cannot be changed. Because of these reasons, we have developed an optimal route selection model using heuristic search and Min-Max algorithm for balancing the workload between machine groups in the FFL.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.211-223
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• 2018

Mobile relay systems have been adopted by $4^{th}$ generation mobile systems as an alternative method to extend cell coverage as well as to enhance the system throughput at cell-edges. In order to achieve such performance gains, the mobile relay systems require path selection and resource allocation schemes that are specifically designed for these systems which make use of additional radio resources not needed in single-hop systems. This paper proposes an integrated path selection and resource allocation scheme for LTE-Advanced relay systems using collaborative communication. We first define the problem of maximizing the downlink throughput of LTE-Advanced relay systems using collaborative communication and transform it into a multi-dimensional multi-choice backpacking problem. The proposed Lagrange multiplier-based heuristic algorithm is then applied to derive the approximate solution to the maximization problem. It is shown through simulations that the approximate solution obtained by the proposed scheme can achieve a near-optimal performance.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.879-888
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• 2013

This paper addresses the vision based local path planning system for obstacle avoidance. To handle the obstacles which exist beyond the field of view (FOV), we propose a Panoramic Environment Map (PEM) using the MDGHM-SIFT algorithm. Moreover, we propose a Complexity Measure (CM) and Fuzzy logic-based Avoidance Motion Selection (FAMS) system to enable a humanoid robot to automatically decide its own direction and walking motion when avoiding an obstacle. The CM provides automation in deciding the direction of avoidance, whereas the FAMS system chooses the avoidance path and walking motion, based on environment conditions such as the size of the obstacle and the available space around it. The proposed system was applied to a humanoid robot that we designed. The results of the experiment show that the proposed method can be effectively applied to decide the avoidance direction and the walking motion of a humanoid robot.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.28-41
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• 1994

An efficient algorithm is proposed to select the proper tools and generate their paths for NC rough cutting of dies and molds with sculptured surfaces. Even though a milling process consists of roughing, semi-finishing, and finishing, most material is removed by a rough cutting process. Therfore it can be said that the rough cutting process occupy an important portion of the NC milling process, and accordingly, an efficient rough cutting method contributes to an efficient milling process. In order work, the following basic assumption is accepted for the efficient machining. That is, to machine a region bounded by a profile, larger tools should be used in the far inside and the region adjacent to relatively simple portion of the boundary while smaller tools are used in the regions adjacent to the relatively complex protion. Thus the tools are selected based on the complexity of the boundary profile adjacent to the region to be machined. An index called cutting path ratio is proposed in this work as a measure of the relative complexity of the profile with respect to a tool diameter. Once the tools are selected, their tool paths are calculated starting from the largest to the smallest tool.

• 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.