• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.2
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• pp.159-166
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• 2020

In this paper, we propose a heterogenous mission allocation technique for multi-UAV system based on discrete event modeling. We model a series of heterogenous mission creation, mission allocation, UAV departure, mission completion, and UAV maintenance and repair process as a mathematical discrete event model. Based on the proposed model, we then optimize the number of UAVs required to operate in a given scenario. To validate the optimized number of UAVs, the simulations are executed repeatedly, and their results are analyzed. The proposed mission allocation technique can be used to efficiently utilize limited UAV resources, and allow the human operator to establish an optimal mission plan.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.65-80
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• 2012

Mobile IP is a simple and scalable global mobility solution. However, it may cause excessive signaling traffic and long signaling delay. So MN (Mobile Node) to the home network in order to reduce the number of location update signaling is necessary to reduce the delay. In this paper, the signaling overhead to be distributed evenly on the boundary of the regional network, each MN's dynamic mobility and traffic load is adjusted according to the PMIPv6 (Proxy Mobile IPv6) networks in the proposed dynamic regional mobility management scheme (dMMS). Each user in a distributed network system that offers the least amount of signaling traffic is tailored to the optimized system configuration. Signaling cost function in order to propose a new discrete analytical model is proposed, MN's mobility and packet arrival patterns. Location update and packet delivery costs to calculate the total average, the optimized area to determine the size of the network is proposed. The results of mathematical analysis, the proposed technique dMMS in terms of reducing the cost of the entire signaling were found to be excellent performance.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.30-37
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• 2010

Many embedded real - lime systems have adopted processors supported with dynamic voltage scal-ing(DVS) recently. Power is one of the important metrics for Optimization in the design and operation of embedded real-time systems. We can save considerable energy by using slowdown of processor sup-ported with DVS. In this paper, we improved the previous algorithm at a point of view of time complexity to calculate task slowdown factors for an efficient energy consumption in embedded real-time systems with task synchronization. We grasped the properties of the previous algorithm having $O(n^{2})$ time complexity through mathematical analysis and s simulation. Using its properties we proposed the improved algorithms with O(nlogn) and O(n) time complexity which have the same performance as the previous algorithm has.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.833-841
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• 2001

Abstract In this work, we analyze the Levy mutation operations based on the Levy probability distribution in the evolutionary programming via the mean square displacement and the distinctness. The Levy probability distribution is characterized by an infinite second moment and has been widely studied in conjunction with the fractals. The Levy mutation operators not only generate small varied offspring, but are more likely to generate large varied offspring than the conventional mutation operators. Based on this fact, we prove mathematically, via the mean square displacement and the distinctness, that the Levy mutation operations can explore and exploit a search space more effectively. As a result, one can get better performance with the Levy mutation than the conventional Gaussian mutation for the multi-valued functional optimization problems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7B
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• pp.649-658
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• 2009

Due to limited size and limited battery lifetime of sensor node, one has to address the power saving issue in wireless sensor network. The existing power saving algorithm based on data reuse was proposed for the cluster structured wireless sensor network. We state the problem of existing power saving algorithm and propose new power saving algorithm for tree structured wireless sensor network. The proposed algorithm reduces power consumption by buffering the sensed data at the selected relay node for its data lifetime. The optimum buffering node is selected so that the power saving gain is maximized and at the same time, power consumption among sensor nodes are equally distributed in the network. With computer simulations, it is shown that the proposed algorithm outperforms the conventional algorithm in terms of power saving gain.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.91-97
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• 2002

This paper describes a heuristic search technique carrying out collision avoidance for Autonomous Underwater Vehicles(AUVs). Fuzzy relational products are used as the mathematical implement for the analysis and synthesis of relations between obstacles that are met in the navigation environment and available candidate nodes. In this paper, we propose a more effective evaluation function that reflects the heuristic information of domain experts on obstacle clearance, and an advanced heuristic search method performing collision avoidance for AUVs. The search technique adopts fuzzy relational products to conduct path-planning of intelligent navigation system. In order to verify the performance of proposed heuristic search, it is compared with $A^*$ search method through simulation in view of the CPU time, the optimization of path and the amount of memory usage.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.195-208
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• 2009

A network design problem (NDP) formulated as a mathematical program is generally used to find an optimum value to minimize or to maximize some objectives such as total travel time, social benefit, or others. NDP has, however, some limits of describing components of travel patterns like activities and trip generation due to its modeling simplicity, and also it has difficulty in including attributes of regional planning. In order to cope with such limits, this paper extends NDP to the urban planning field and proposes a mathematical program which can describe the interactions between urban social activities and transportation planning. Based on this model the authors try to optimize both urban activities and the transportation system. The model developed in this paper is tested to assess its application with a real-size regional transportation network.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.14-21
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• 2021

Green networking has become a issue to reduce energy wastes and CO2 emission by adding energy managing mechanism to wired data networks. Energy consumption of the overall wired data networks is driven by access networks, expect for end devices. However, on a global scale, it is more difficult to manage centrally energy, measure and model the real energy use and energy savings potential of the access networks. This paper presented the multiple linear regression model to predict energy consumption of wired access networks using supervised learning of machine learning with data collected by existing investigated materials, actual measured values and results of many models. In addition, this work optimized the performance of it by various experiments and predict energy consumption of wired access networks. The performance evaluation of the regression model was achieved by well-knowned evaluation metrics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.433-442
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• 2010

Recent decrease in the availability of experienced skilled labor and a corresponding lack of new entrants has required the need for automating many of the construction equipment used in the construction industry. In particular, excavators are widely used throughout earthwork operations and automating its tasks enables work to be performed with higher productivity and safety. This paper introduces an optimal path generation method which is one of the core technologies required to make "Intelligent" excavators a reality. The method divides a given earthwork area into unit cells, identifies networks created by linking these cells, and identifies the optimal path an excavator should follow to minimize its total transportation costs. In addition, the method also accounts for drainage direction and path continuity to ensure that the generated path considers site specific conditions.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.51-60
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• 2024

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.