• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.192-202
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• 2009

Disassembly of products at their end-of-life (EOL) is a prerequisite for recycling or remanufacturing, since most products should be disassembled before being recycled or remanufactured as secondary parts or materials. In disassembly sequence planning of EOL products, considered are the uncertainty issues, i.e., defective parts or joints in an incoming product, disassembly damage, and imprecise net profits and costs. The paper deals with the problem of determining the disassembly level and corresponding sequence, with the objective of maximizing the overall profit under uncertainties in disassembly cost and/or revenue. The solution is represented as the longest path on a directed acyclic graph where parameter (arc length) uncertainties are modeled in the form of intervals. And, a heuristic algorithm is developed to find a path with the minimum worst case regret, since the problem is NP-hard. Computational experiments are carried out to show the performance of the proposed algorithm compared with the mixed integer programming model and Conde's heuristic algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.477-493
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• 2011

The IEEE 802.16 mobile multi-hop relay (MMR) task group 'j' (TGj) has introduced the multi-hop relaying concept in the IEEE 802.16 Wireless MAN, wherein a relay station (RS) is employed to improve network coverage and capacity. Several RSs can be deployed between a base station and mobile stations, and configured to form a tree-like multi-hop topology. In such architecture, we consider the problem of a path selection through which the mobile station in and outside the coverage can communicate with the base station. In this paper, we propose a new path selection algorithm that ensures more efficient distribution of resources such as bandwidth among the relaying nodes for improving the overall performance of the network. Performance of our proposed scheme is compared with the path selection algorithms based on loss rate and the shortest path algorithm. Based on the simulation results using ns-2, we show our proposal significantly improves the performance on throughput, latency and bandwidth consumption.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.90-96
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• 2011

Finding the critical path (or the longest path) on acyclic directed graphs, which is well-known as PERT/CPM, the ambiguity of each acr's length can be modeled as a range or an interval, in which the actual length of arc may realize. In this case, the min-max regret criterion, which is widely used in the decision making under uncertainty, can be applied to find the critical path minimizing the maximum regret in the worst case. Since the min-max regret critical path problem with the interval arc's lengths is known as NP-hard, this paper proposes a heuristic algorithm to diminish the maximum regret. Then the computational experiments shows the proposed algorithm contributes to the improvement of solution compared with the existing heuristic algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.79-84
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• 1992

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the optimal collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy and a traveling salesman problem strategy (TSP). The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Hopfield Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is used to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.596-604
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• 2020

Previous studies on pipe auto-routing algorithms generally used such algorithms as A*, Dijkstra, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization, to satisfy the relevant constraints of its own field and improve the output quality. On the other hand, this study aimed to significantly improve path-finding speed by applying the Jump Point Search (JPS) algorithm, which requires lower search cost than the abovementioned algorithms, for pipe routing. The existing JPS, however, is limited to two-dimensional spaces and can only find the shortest path. Thus, it requires several improvements to be applied to pipe routing. Pipe routing is performed in a three-dimensional space, and the path of piping must be parallel to the axis to minimize its interference with other facilities. In addition, the number of elbows must be reduced to the maximum from an economic perspective, and preferred spaces in the path must also be included. The existing JPS was improved for the pipe routing problem such that it can consider the above-mentioned problem. The fast path-finding speed of the proposed algorithm was verified by comparing it with the conventional A* algorithm in terms of resolution.

• ETRI Journal
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• v.31 no.1
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• pp.31-41
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• 2009

Optical burst switching is a promising switching paradigm for the next IP-over-optical network backbones. However, its burst loss performance is greatly affected by burst contention. Several methods have been proposed to address this problem, some of them requiring the network to be flooded by frequent state dissemination signaling messages. In this work, we present a traffic engineering approach for path selection with the objective of minimizing contention using only topological information. The main idea is to balance the traffic across the network to reduce congestion without incurring link state dissemination protocol penalties. We propose and evaluate two path selection strategies that clearly outperform shortest path routing. The proposed path selection strategies can be used in combination with other contention resolution methods to achieve higher levels of performance and support the network reaching stability when it is pushed under stringent working conditions. Results show that the network connectivity is an important parameter to consider.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.88-92
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• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.453-461
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• 2010

A critical issue in wireless sensor networks is an energy-efficiency since the sensor batteries have limited energy power and, in most cases, are not rechargeable. The most practical manner relate to this issue is to use a node wake-up scheduling protocol that some sensor nodes stay active to provide sensing service, while the others are inactive for conserving their energy. Especially, CTC (Connected Target Coverage) problem has been considered as a representative energy-efficiency problem considering connectivity as well as target coverage. In this paper, we propose a new energy consumption model considering multiple-targets and create a new problem, CMTC (Connected Multiple-Target Coverage) problem, of which objective is to maximize the network lifetime based on the energy consumption model. Also, we present SPT (Shortest Path based on Targets)-Greedy algorithm to solve the problem. Our simulation results show that SPT-Greedy algorithm performs much better than previous algorithm in terms of the network lifetime.

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.386-396
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• 2002

This paper presents a neural network-based near-optimal routing algorithm. It employs a modified Hopfield Neural Network (MHNN) as a means to solve the shortest path problem. It uses every piece of information that is available at the peripheral neurons in addition to the highly correlated information that is available at the local neuron. Consequently, every neuron converges speedily and optimally to a stable state. The convergence is faster than what is usually found in algorithms that employ conventional Hopfield neural networks. Computer simulations support the indicated claims. The results are relatively independent of network topology for almost all source-destination pairs, which nay be useful for implementing the routing algorithms appropriate to multi -hop packet radio networks with time-varying network topology.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.210-217
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• 2010

This paper presents integrated path planning and collision avoidance for an omni-directional mobile robot. In this scheme, the autonomous mobile robot finds the shortest path by the descendent gradient of a navigation function to reach a goal. In doing so, the robot based on the proposed approach attempts to overcome some of the typical problems that may pose to the conventional robot navigation. In particular, this paper presents a set of analysis for an omni-directional mobile robot to avoid trapped situations for two representative scenarios: 1) Ushaped deep narrow obstacle and 2) narrow passage problem between two obstacles. The proposed navigation scheme eliminates the nonfeasible area for the two cases by the help of the descendent gradient of the navigation function and the characteristics of an omni-directional mobile robot. The simulation results show that the proposed navigation scheme can effectively construct a path-planning system in the capability of reaching a goal and avoiding obstacles despite possible trapped situations under uncertain world knowledge.