• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.65-73
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• 2017

The fifth generation(5G) of wireless networks will connect not only smart phone but also unimaginable things. Therefore, 5G cellular network is facing the soaring traffic demand of numerous user devices. To solve this problem, a huge amount of 5G base stations will need to be installed. The base station positioning problem is an NP-hard problem that does not know how long it will take to solve the problem. Because, it can not find an answer other than to check the number of all cases. In this paper, to solve the NP hard problem, we compare the tabu search and the genetic algorithm using real maps for optimal cell planning. We also perform Monte Carlo simulations to study the performance of the Tabu search and Genetic algorithm for 5G cell planning. As a results, Tabu search required 2.95 times less computation time than Genetic algorithm and showed accuracy difference of 2dBm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1193-1201
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• 2020

Cell planning, which determines the cell structure for location management of mobile terminals in mobile communications, has been dealt with as an important research task to determine network performance. Among the factors influencing the cell structure planning in mobile communication, the signal cost for location management plays the most important role. In this paper, we propose an optimization algorithm that minimizes the location management cost of all the cells used to plan the cell structure in the network with reporting cell structure in mobile communication. The proposed algorithm uses a Tabu search algorithm, which is a meta-heuristic algorithm, and the proposed algorithm proposes a new neighborhood generation method to obtain a result close to the optimal solution. In order to evaluate the performance of the proposed algorithm, the simulation was performed in terms of location management cost and algorithm execution time. The evaluation results show that the proposed algorithm outperforms the existing genetic algorithm and simulated annealing.

• Korean Management Science Review
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• v.26 no.1
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• pp.171-182
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• 2009

We consider the inventory replenishment problem and the vehicle routing problem simultaneously in the vending machine operation. This problem is known as the inventory routing problem. We design a memory cell in the clonal selection algorithm. The memory cell store the best solution of previous solved problem and use an initial solution for next problem. In general, the other clonal selection algorithm used memory cell for reserving the best solution in current problem. Experiments are performed for testing efficiency of the memory cell in demand uncertainty. Experiment result shows that the solution quality of our algorithm is similar to general clonal selection algorithm and the calculations time is reduced by 20% when the demand uncertainty is less than 30%.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.7
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• pp.73-80
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• 2009

This paper describes a path planning algorithm, which is the minimal turning path based on the shape and size of the cell to clean up the whole area with two cleaning robots. Our method divides the whole cleaning area with each cell by cellular decomposition, and then provides some path plans among of the robots to reduce the rate of energy consumption and cleaning time of it. In addition we suggest how to plan between the robots especially when they are cleaning in the same cell. Finally simulation results demonstrate the effectiveness of the algorithm in an unknown area with multiple robots. And then we compare the performance index of two algorithms such as total of turn, total of time.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2315-2317
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• 1998

There has been extensive efforts about robot path planning. Some major approaches are the roadmap approach, potential field approach and the cell decomposition approach. However, most of the path planning methods proposed so far based on above approaches consider the terrains filled with binary obstacles, i.e., if there exists an obstacle, robot simply cannot pass the location. In this paper, A mobile robot path planning method based on the cell decomposition technique for mobile robot that takes account of the terrain with varing degrees of travers-ability is discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1651-1654
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• 2003

The process equipment for handling high level radioactive materials, such as spent nuclear fuel, is operated within a sealed facility, called a hot cell, due to high radioactivity. Thus, this equipment should be maintained and repaired by remotely operated manipulator. In this study, to carry out the sale and effective maintenance of the process equipment installed in the hot cell by a servo type manipulator, a collision free motion planning method of the manipulator using virtual prototyping technology is suggested. To do this, the parts are modelled in 3-D graphics, assembled, and kinematics are assigned and the virtual workcell is implemented in the graphical environment which is the same as the real environment. The method proposed in this paper is to find the optimal path of the manipulator using the function of the collision detection in the graphic simulator. The proposed path planning method and this graphic simulator of manipulator can be effectively used in designing of the maintenance processes for the hot cell equipment and enhancing the reliability of the spent fuel management.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.251-255
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• 2007

In this paper, propagation analysis method in using 3D Ray Tracing propagation model in wireless cell planning is proposed. Through 3D Ray Tracing model, we can predict the distribution of propagation loss of the received signal. For correct and a low complex analysis, Quad Tree and Pre-Ordering and Hash Function algorithms are included in 3D Ray Tracing algorithm. And 3D Ray Tracing model is embodied in CellTREK that is developed by KT and used to plan Wibro system analysis. In CellTREK, propagation analysis is performed and that result is represented in 3D viewer. In numerical results, it is showed that the proposed scheme outperforms Modified HATA model when comparing with measurement data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.15-20
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• 2024

Path planning for unmanned aerial vehicles (UAV) is crucial in avoiding collisions with obstacles in complex environments that include both static and dynamic obstacles. Path planning algorithms like RRT and A^* are effectively handle static obstacle avoidance but have limitations with increasing computational complexity in high-dimensional environments. Reinforcement learning-based algorithms can accommodate complex environments, but like traditional path planning algorithms, they struggle with training complexity and convergence in higher-dimensional environment. In this paper, we proposed a reinforcement learning model utilizing a cell decomposition algorithm. The proposed model reduces the complexity of the environment by decomposing the learning environment in detail, and improves the obstacle avoidance performance by establishing the valid action of the agent. This solves the exploration problem of reinforcement learning and improves the convergence of learning. Simulation results show that the proposed model improves learning speed and efficient path planning compared to reinforcement learning models in general environments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.219-228
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• 1997

The objective of this paper is to provide an alternative framework for the integration of process planning and scheduling in cellular manufacturing. The concept of an integrated cellular manufacturing system is defined and the system architecture is presented. In an integrated cellular manufacturing system, there are three modules : the process planning module, the manufacturing-cell design module, and the cell-scheduling module. For each module, the tasks and their activities are explained.

• The Journal of Korea Robotics Society
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• v.2 no.4
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• pp.304-309
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• 2007

This paper describes an efficient path generation method for area coverage. Its applications include robots for de-mining, cleaning, painting, and so on. Our method is basically based on a divide and conquer strategy. We developed a novel cell decomposition algorithm that divides a given area into several cells. Each cell is covered by a robot motion that requires minimum time to cover the cell. Using this method, completeness and time efficiency of coverage are easily achieved. For the completeness of coverage in dynamic environments, we also propose a path following method that makes the robot cover missed areas as a result of the presence of unknown obstacles. The effectiveness of the method is verified using computer simulations.