• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.69-76
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• 2021

Recentlly neural network approach for solving a singular perturbed integro-differential boundary value problem have been researched. Especially the model of the feed-forward neural network to be trained by the back propagation algorithm with various learning algorithms were theoretically substantiated, and neural network models such as deep learning, transfer learning, federated learning are very rapidly evolving. The purpose of this paper is to study the approaching method for developing a neural network model with high accuracy and speed for solving singular perturbed problem along with asymptotic methods. In this paper, we propose a method that the simulation for the difference between result value of singular perturbed problem and unperturbed problem by using neural network approach equation. Also, we showed the efficiency of the neural network approach. As a result, the contribution of this paper is to show the possibility of simple neural network approach for singular perturbed problem solution efficiently.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.417-426
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• 2024

In general, the implementation of machine learning requires prior knowledge and experience with deep learning models, and substantial computational resources and time are necessary for data processing. As a result, machine learning encounters several limitations when deployed on embedded processors. To address these challenges, this paper introduces a novel approach where a genetic algorithm is applied to the convolution operation within the machine learning process, specifically for performing a selective convolution operation.In the selective convolution operation, the convolution is executed exclusively on pixels identified by a genetic algorithm. This method selects and computes pixels based on a ratio determined by the genetic algorithm, effectively reducing the computational workload by the specified ratio. The paper thoroughly explores the integration of genetic algorithms into machine learning computations, monitoring the fitness of each generation to ascertain if it reaches the target value. This approach is then compared with the computational requirements of existing methods.The learning process involves iteratively training generations to ensure that the fitness adequately converges.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.1-9
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• 2024

This paper proposes an integration of the ED-H scheduling algorithm, known for optimal real-time scheduling, with the LoRaEnergySim simulator. This integration facilitates the simulation of interactions between real-time scheduling algorithms for tasks with time constraints in Class-A LoRaWAN Class-A devices using a super-capacitor-based energy harvesting system. The time and energy characteristics of LoRaWAN status and state transitions are extracted in a log format, and the task model is structured to suit the time-slot-based ED-H scheduling algorithm. The algorithm is extended to perform tasks while satisfying time constraints based on CPU executions. To evaluate the proposed approach, the ED-H scheduling algorithm is executed on a set of tasks with varying time and energy characteristics and CPU occupancy rates ranging from 10% to 90%, under the same conditions as the LoRaEnergySim simulation results for packet transmission and reception. The experimental results confirmed the applicability of co-simulation by demonstrating that tasks are prioritized based on urgency without depleting the supercapacitor's energy to satisfy time constraints, depending on the scheduling algorithm.

• Journal of the Korea Society for Simulation
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• v.8 no.1
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• pp.77-88
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• 1999

There are two approaches to handle the Causality Error in parallel and distributed simulation. One approach is based on the conservative time synchronization and the other is the optimistic time synchronization. In this paper an efficient null message reduction method for the conservative time synchronization approach is suggested with the experimental results, which could improve performance of simulation and avoid deadlock situations.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.6
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• pp.469-477
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• 2016

This study develops an approach to assign numbers of facilities (rectangular shape) in a given plant and compares the test results by proposed approach with those by approaches in the literature. An improvement approach is proposed to minimize material handling cost given initial layout. Like popular heuristic approaches, the developed heuristic approach employs interchange routine to improve material handling cost in current layout. Horizontal interchange and vertical interchange procedures are applied to obtain better solution. Also, it is possible to rotate facility layout when the sizes of both facilities are same. However, the proposed approach generates good solutions without shape distortion. That means the shape of facilities remains rectangle in the final solution. In addition, the improve approach can find global optimal solution from local optimal solution by applying Tabu search technique. Based on 25 test problems in the literature, we obtained better solutions than other facility layout approaches in the literature when there are many facilities.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.827-837
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• 2007

Conventional color-based object tracking using Mean Shift algorithm does not provide appropriate result when initial color distribution disappears. In this paper we propose a tracking algorithm that updates the object color sample when the color is changing. Mean Shift analysis is first used to derive the object candidate with maximum increase in density direction from current position. The color information of object is updated iteratively. The proposed algorithm achieves accurate tracking of objects when initial color samples are changed and finally disappeared. The validity of the effective approach is illustrated by the experimental results.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.87-93
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• 2002

In this paper, an advanced load balancing algorithm in nth order Hypercube distributed system has been proposed. The new algorithm uses centralized load-balancing to avoid blocking phenomenon and processor thrashing, and shows the results which makes loads to approach average value of loads. The new algorithm is compared with several other algorithm and it shows a merit in cost function value.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.363-365
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• 2000

현재 사용중인 경로 선택 방법은 최단 경로 알고리즘을 이용하므로 망 자원을 효율적으로 이용하지 못하며 특정 경로로 트래픽이 집중될 경우 혼잡(congestion) 상황을 발생시킬 수 있다. 본 논문에서는 새롭게 요청되는 연결 설정 요구에 대해 요청된 대역폭을 충족시킬 수 있는 경로를 결정하는데 있어서 통계적 기법을 이용함으로써 망을 효율적으로 사용할 수 있도록 하는 통계적 경로 선택(Statistical Route Selection; SRS) 알고리즘을 제안한다. MPLS[4]의 등장으로 부하 균등화(load balancing)에 필요한 명시적인(explicit) LSP 설정을 할 수 있게 되었으며, MPLS의 LSP를 설정하기 위해 SRS 알고리즘을 이용할 수 있다. SRS 알고리즘은 경로 선택을 위해 링크들의 이용률을 구하고, 통계적인 기법을 사용하여 가중치를 결정하며, 그 가중치를 이용한 최단 경로를 구한다. 여기서 사용되는 통계적 기법은 링크 이용률의 평균과 분산을 이용하는 것으로, 이 정보를 기반으로 링크의 가중치에 대해 분산을 작게 하는 방향으로 경로를 결정함으로써 부하 균등화 효과를 얻게 되어 망 자원 이용률을 높인다. 실험을 통해 SP, WSP, SDP[3] 알고리즘에 비해 SRS 알고리즘이 망 자원을 효율적으로 이용하여 연결 설정 실패의 수와 혼잡 링크의 수를 줄이는 것을 보인다.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.63-68
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• 2010

In this paper, we propose a multiresolution stereo matching method based on genetic algorithm using edge information. The proposed approach considers the matching environment as an optimization problem and finds the solution by using a genetic algorithm. A cost function composes of certain constraints which are commonly used in stereo matching. We defines the structure of chromosomes using edge pixel information of reference image of stereo pair. To increase the efficiency of process, we apply image pyramid method to stereo matching and calculate the initial disparity map at the coarsest resolution. Then initial disparity map is propagated to the next finer resolution, interpolated and performed disparity refinement. We valid our approach not only reduce the search time for correspondence but alse ensure the validity of matching.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.212-216
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• 2011

Fault detection technique for photovoltaic power systems is significant to dramatically reduce economic damage in industrial fields. This paper presents a novel fault detection approach using Fourier neural networks and stochastic decision making strategy for photovoltaic systems. We achieve neural modeling to represent its nonlinear dynamic behaviors through a gradient descent based learning algorithm. Next, a general likelihood ratio test (GLRT) is derived for constructing a decision malling mechanism in stochastic fault detection. A testbed of photovoltaic power systems is established to conduct real-time experiments in which the DC power line communication (DPLC) technique is employed to transfer data sets measured from the photovoltaic panels to PC systems. We demonstrate our proposed fault detection methodology is reliable and practicable over this real-time experiment.