• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.192-195
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• 2008

Radio occultation (RO) has been used in the planetary science since Microlab-1 was launched in 1995. With the RO technique, the profiles of atmosphere and the global atmospheric data can be obtained. In 2006, Taiwan launched six low Earth orbit (LEO) satellites as the RO constellation mission, known as FORMOSAT-3. In order to retrieve the RO data from original data, a retrieval algorithm, NCURO, is developed. The input of NCURO algorithm is mainly the excess phase of GPS signal, and the output is the dry pressure and dry temperature. Using temperature profiles retrieved by NCURO algorithm, temperature perturbation and potential energy of gravity wave have been evaluated. In this paper, the retrieval algorithm and the global distribution of energy of gravity waves are described and demonstrated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8C
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• pp.793-800
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• 2005

Computation of multimedia data increases in portable devices with the advances of the mobile and personal communication services. The energy management of such devices is very important for the battery-powered operation hours. The motion estimation in a video encoder requires huge amount of computation, and hence, consumes the largest portion of the energy consumption. In this paper, we propose a novel architecture that a low energy management scheme can be applied with several fast-search algorithms. The energy-constrained Vdd hopping (ECVH) technique reduces power consumption of the motion estimation by adaptively changing the search algorithm, the operating frequency, and the supply voltage using the remaining slack time within given power-budget. We show that the ECVH can be applied to the architecture, and that the power consumption can be efficiently reduced.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.731-742
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• 2001

A Gaussian smoothing algorithm obtained from a cascade of convolutions with a seven-point kernel is described. We prove that the change of local sums after applying our algorithm to sinusoidal signals is reduced to about two thirds of the change by the binomial coefficients. Hence, our seven point kernel is better than the binomial coefficients when trend curves are needed to be generated. We also prove that if our Gaussian convolution is applied to sinusoidal functions, the amplitude of higher frequencies reduces faster than the lower frequencies and hence that it is a low pass filter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.8
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• pp.528-537
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• 2014

Wireless Sensor Network(WSN) is a network which consists of sensor nodes(SNs) in data collection area. It is hard for the SNs to replace battery. Thus energy and transfer efficiency is important because the energy is limited. In this paper, we propose energy-efficient data collection algorithm for wireless sensor networks by using mobile agent in distance-based cluster structure. For avoid collisions and guarantee low latency, make distance-based topology and build cluster by the topology. For performance comparison of the proposed algorithm, compare with existing mobile agent algorithm. When network constructed by 300 nodes, proposed algorithm has performance increase than existing algorithm(GCF, LCF, TBID) in network lifetime 194, 124.6, 1.46 times each and data merging energy efficiency 87.5%, 85%, 45% each.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.140-144
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• 2003

A sensor network consists of many low-cost, low-power, and multi-functional sensor nodes. One of most important issues in of sensor networks is to increase network lifetime, and there have been researches on the problem. In this paper, we propose a routing mechanism to prolong network lifetime, in which each node adjusts its transmission power to send data to its neighbors. We model the energy efficient routing with power control and present an algorithm to obtain the optimal flow solution for maximum network lifetime. Then, we derive an upper bound on the network lifetime for specific network topologies.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.30-43
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• 2013

Multicore and multiprocessor systems with dynamic voltage scaling architectures are being used as one of the solutions to satisfy the growing needs of high performance applications with low power constraints. An important aspect that has propelled this solution is effective task/application scheduling and mapping algorithms for multiprocessor systems. This work proposes an energy aware, offline, probability-based unified scheduling and mapping algorithm for multiprocessor systems, to minimize the number of processors used, maximize the utilization of the processors, and optimize the energy consumption of the multiprocessor system. The proposed algorithm is implemented, simulated and evaluated with synthetic task graphs, and compared with classical scheduling algorithms for the number of processors required, utilization of processors, and energy consumed by the processors for execution of the application task graphs.

• Journal of Magnetics
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• v.16 no.4
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• pp.379-385
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• 2011

This paper presents an optimal design of a direct-drive permanent magnet synchronous generator for a small-scale wind energy conversion system. An analytical model of a small-scale grid-connected wind energy conversion system is presented, and the effects of generator design parameters on the payback period of the system are investigated. An optimization procedure based on genetic algorithm method is then employed to optimize four design parameters of the generator for use in a region with relatively low wind-speed. The aim of optimization is minimizing the payback period of the initial investment on wind energy conversion systems for residential applications. This makes the use of these systems more economical and appealing. Finite element method is employed to evaluate the performance of the optimized generator. The results obtained from finite element analysis are close to those achieved by analytical model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2914-2935
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• 2016

With the increasing popularization of cloud computing, how to reduce physical energy consumption and increase resource utilization while maintaining system performance has become a research hotspot of virtual machine deployment in cloud platform. Although some related researches have been reported to solve this problem, most of them used the traditional heuristic algorithm based on greedy algorithm and only considered effect of single-dimensional resource (CPU or Memory) on energy consumption. With considerations to multi-dimensional resource utilization, this paper analyzed impact of multi-dimensional resources on energy consumption of cloud computation. A multi-dimensional resource constraint that could maintain normal system operation was proposed. Later, a novel virtual machine deployment method (NVMDM) based on improved particle swarm optimization (IPSO) and Euclidean distance was put forward. It deals with problems like how to generate the initial particle swarm through the improved first-fit algorithm based on resource constraint (IFFABRC), how to define measure standard of credibility of individual and global optimal solutions of particles by combining with Bayesian transform, and how to define fitness function of particle swarm according to the multi-dimensional resource constraint relationship. The proposed NVMDM was proved superior to existing heuristic algorithm in developing performances of physical machines. It could improve utilization of CPU, memory, disk and bandwidth effectively and control task execution time of users within the range of resource constraint.

• Journal of Sensor Science and Technology
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• v.22 no.4
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• pp.268-275
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• 2013

Efficient energy management becomes a critical design issue for complex WSN (Wireless Sensor Network). Most of complex WSN employ the sleep mode to reduce the energy dissipation. However, it should cause the reduction of sensing coverage. This paper presents new wake-up algorithm for reducing energy consumption in complex WSN. The proposed wake-up algorithm is devised using geometric probability. It determined which node will be waked-up among the nodes having overlapped sensing coverage. The only one sensor node will be waked-up and it is ready to sense the event occurred uniformly. The simulation results show that the lifetime is increased by 15% and the sensing coverage is increased by 20% compared to the other scheduling methods. Consequently, the proposed wake-up algorithm can eliminate the power dissipation in the overlapped sensing coverage. Thus, it can be applicable for the various WSN suffering from the limited power supply.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.185-194
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• 2020

Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimize the energy consumption of the Wireless Sensor Networks (WSNs). In this paper, a homogeneous and randomly deployed sensor nodes is considered. These sensors are energy constrained elements. The nominal selection of the Cluster Head (CH) which falls under the clustering part of the network protocol is studied and compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CHs in this proposed process is the function of total remaining energy of each node as well as total average energy of the whole arrangement. The algorithm considers initial energy, optimum value of cluster heads to elect the next group of cluster heads for the network as well as residual energy. Total remaining energy of each node is compared to total average energy of the system and if the result is positive, these nodes are eligible to become CH in the very next round. Analysis and numerical simulations quantify the efficiency and Average Energy Ratio (AER) of the proposed system.