• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.563-564
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• 2009

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.1
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• pp.9-16
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• 2008

This paper presents a frequency selection algorithm for minimization power consumption of processor in Mobile System. The proposed algorithm has processor designed low power processor using clock gating method. Clock gating method has improved the power dissipation by control main clock through the bus which is embedded clock block applying the method of clock gating. Proposed method has compared power consumption considered the dynamic power for processor, selected frequency has considered energy gain and energy consumption for designed processor. Or reduced power consumption with decreased processor speed using slack time. This technique has improved the life time of the mobile systems by clock gating method, considered energy and using slack time. As an results, the proposed algorithm reduce average power saving up to 4% comparing to not apply processor in mobile system.

• Advances in Energy Research
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• v.4 no.4
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• pp.255-274
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• 2016

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.88-95
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• 2014

Because the cost of performance testing using actual products is expensive, manufacturers use lower-cost computer-aided design simulations for this function. In this paper, we propose using hexahedral meshes, which are more accurate than tetrahedral meshes, for finite element analysis. We propose automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated using a voxel-based algorithm. In our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vectors in the fitting to preserve sharp features. However, this method could not represent concave sharp features precisely. In this proposal, we improve our previous Laplacian energy minimization by adding a term that depends on multi-normal vectors instead of using normal vectors. Furthermore, we accentuate a convex/concave surface subset to represent concave sharp features.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1843-1850
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• 2014

In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants' comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

• Journal of Power Electronics
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• v.8 no.4
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• pp.345-353
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• 2008

In this paper, a novel loss minimization of an induction generator in wind energy generation systems is presented. The proposed algorithm is based on the flux level reduction, for which the generator d-axis current reference is estimated using support vector regression (SVR). Wind speed is employed as an input of the SVR and the samples of the generator d-axis current reference are used as output to train the SVR algorithm off-line. Data samples for wind speed and d-axis current are collected for the training process, which plots a relation of input and output. The predicted off-line function and the instantaneous wind speed are then used to determine the d-axis current reference. It is shown that the effect of loss minimization is more significant at low wind speed and the loss reduction is about to 40% at 4[m/s] wind speed. The validity of the proposed scheme has been verified by experimental results.

• Journal of IKEEE
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• v.22 no.2
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• pp.455-459
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• 2018

In this paper, proposed a multi-channel charging control strategy for electric vehicle. This control strategy can adjust the charging power according to the calculated state-of-charge (SOC). Electric vehicle (EV) charging system using Particle Swarm Optimization (PSO) algorithm is proposed. A stochastic optimization algorithm technique such as PSO in the time-of-use (TOU) price used for the energy cost minimization. Simulation results show that the energy cost can be reduced using proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1304-1312
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• 2012

According to the successful development of hybrid vehicle, hybridization of construction equipments like excavator, wheel loader, and backhoe etc., is gaining increasing attention. However, hybridization of excavator and commercial vehicle is very different. Therefore a specialized energy management control algorithm for excavator should be developed. In this paper, hybridization of excavators is investigated and a new energy management control algorithm is proposed. Four control parameters, i.e., lower baseline, upper baseline, idling generation speed, and idling generation torque, are newly introduced and a new operating principle using those four control parameters is proposed. The use of Genetic Algorithm for the optimization of the four control parameters from the view point of minimization of fuel consumption for standard excavating operation is suggested. In order to verify the proposed algorithm, dedicated simulation program of hybrid excavator was developed. The proposed algorithm is applied to a specific hydraulic excavator and 20.7% improvement of fuel consumption is achieved.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.466-469
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• 1996

In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.