• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.258-264
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• 2018

This paper presents application of a differential search (DS) meta-heuristic optimization algorithm for optimal operation of a micro grid system. The DS algorithm simulates the Brownian-like random-walk movement used by an organism to migrate. The micro grid system consists of a wind turbine, a diesel generator, a fuel cell, and a photovoltaic system. The wind turbine generator is modeled by considering the characteristics of variable output. Optimization is aimed at minimizing the cost function of the system, including fuel costs and maximizing fuel efficiency to generate electric power. The simulation was applied to a micro grid system only. This study applies the DS algorithm with excellence and efficiency in terms of coding simplicity, fast convergence speed, and accuracy in the optimal operation of micro grids based on renewable energy resources, and we compared its optimum value to other algorithms to prove its superiority.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.613-616
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• 2008

그리드는 분산된 이기종 자원을 활용하는 컴퓨팅 패러다임으로 초기 그리드 자원을 활용하기 위한 표준으로 OGSA(Open Grid Service Architecture)가 제안되었다. 글로버스 툴킷3은 사용자가 OGSA에 표준에 맞게 그리드를 구성할 수 있는 기능을 제공했다. 하지만 그 이후 웹 서비스 표준의 발전에 따라 그에 맞는 새로운 표준인 WSRF(Web Service Resource Framework)가 정의 되었고, 현재 WSRF 표준에 따라서 GT4가 개발되어 발표된 상황이다. 이에 본 논문에서는 기존의 OGSA 표준에 맞게 구축된 그리드 정보검색 서비스들을 WSRF 표준에 맞도록 새로운 표준 인터페이스를 정립하고, OGSA-DAI(Data Access & Integration) 컴포넌트를 통한 데이터 관리 기능을 추가함으로써 새로운 그리드 정보검색 서비스 프로토타입을 정의하고자 한다.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• International Journal of Contents
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• v.6 no.1
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• pp.47-52
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• 2010

Skyline queries are an important new search capability for multi-dimensional databases. Most of the previous works have focused on processing skyline queries over static data set. However, most of the real applications deal with the dynamic data set. Since dynamic data set constantly changes as time passes, the continuous skyline computation over dynamic data set becomes ever more complicated. In this paper, we propose a multiple layer grids method for continuous skyline computation (MLGCS) that maintains multiple layer grids to manage the dynamic data set. The proposed method divides the work space into multiple layer grids and creates the skyline influence region in the grid of each layer. In the continuous environment, the continuous skyline queries are only handled when the updating data points are in the skyline influence region of each layer grid. Experiments based on various data distributions show that our proposed method outperforms the existing methods.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.123-130
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• 2009

Motion estimation (ME) plays an important role in digital video compression. But it limits the performance of image quality and encoding speed and is computational demanding part of the encoder. To reduce computational time and maintain the image quality, integer pel and fractional pel ME methods are proposed in this paper. The proposed method for integer pel ME uses a hierarchical search strategy. This strategy method consists of symmetrical cross-X pattern, multi square grid pattern, diamond patterns. These search patterns places search points symmetrically and evenly that can cover the overall search area not to fall into the local minimum and to reduce the computational time. The proposed method for fractional pel uses full search pattern, center biased fractional pel search pattern and the proposed search pattern. According to block sizes, the proposed method for fractional pel decides the search pattern adaptively. Experiment results show that the speedup improvement of the proposed method over Unsymmetrical cross Multi Hexagon grid Search (UMHexagonS) and Full Search (FS) can be up to around $1.2{\sim}5.2$ times faster. Compared to image quality of FS, the proposed method shows an average PSNR drop of 0.01 dB while showing an average PSNR gain of 0.02 dB in comparison to that of UMHexagonS.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.148-153
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• 2020

In order to obtain high accuracy with an convolutional neural network(CNN), it is necessary to set the optimal hyperparameters. However, the exact value of the hyperparameter that can make high performance is not known, and the optimal hyperparameter value is different based on the type of the dataset, therefore, it is necessary to find it through various experiments. In addition, since the range of hyperparameter values is wide and the number of combinations is large, it is necessary to find the optimal values of the hyperparameters after the experimental design in order to save time and computational costs. In this paper, we suggest an algorithm that use the design of experiments and grid search algorithm to determine the optimal hyperparameters for a classification problem. This algorithm determines the optima values of the hyperparameters that yields high performance using the factorial design of experiments. It is shown that the amount of computational time can be efficiently reduced and the accuracy can be improved by performing a grid search after reducing the search range of each hyperparameter through the experimental design. Moreover, Based on the experimental results, it was shown that the learning rate is the only hyperparameter that has the greatest effect on the performance of the model.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.178-184
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• 2024

The radar installed on an aircraft has various operating modes depending on tactical purposes, allowing for the configuration of search areas according to each mode's operational objectives. active electronically scanned array (AESA) radar emits search beams sequentially according to a predefined search beam grid within the designated search area specified by the pilot to detect targets within it. It is crucial that the radar can stably search the area designated by the pilot for target detection, even as the aircraft's attitude changes. This paper focuses on stabilizing the search pattern in the air-to-air operational mode of aircraft-mounted radar to ensure stable target detection during roll and pitch maneuvers of the aircraft. The paper demonstrates its performance by simulating aircraft maneuvers and targets in a system integration laboratory (SIL) test environment.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.669-680
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• 2012

Noise removal of document images is a necessary step during preprocessing to recognize characters effectively because it has influences greatly on processing speed and performance for character recognition. We have considered using the spatial filters such as traditional mean filters and Gaussian filters, and wavelet transformed based methods for noise deduction in natural images. However, these methods are not effective for the noise removal of document images. In this paper, we present noise removal of document images using support vector regression. The proposed approach consists of two steps which are SVR training step and SVR test step. We construct an optimal prediction model using grid search with cross-validation in SVR training step, and then apply it to noisy images to remove noises in test step. We evaluate our SVR based method both quantitatively and qualitatively for noise removal in Korean, English and Chinese character documents, and compare it to some existing methods. Experimental results indicate that the proposed method is more effective and can get satisfactory removal results.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.580-590
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• 2018

Active distribution system (ADS) considering distributed generation (DG) and electric vehicle (EV) is an effective way to cut carbon emission and improve system benefits. ADS is an evolving, complex and uncertain system, thus comprehensive model and effective optimization algorithms are needed. Battery swapping station (BSS) for EV service is an essential type of flexible load (FL). This paper establishes ADS planning model considering BSS firstly for the minimization of total cost including feeder investment, operation and maintenance, net loss and carbon tax. Meanwhile, immune binary firefly algorithm (IBFA) is proposed to optimize ADS planning. Firefly algorithm (FA) is a novel intelligent algorithm with simple structure and good convergence. By involving biological immune system into FA, IBFA adjusts antibody population scale to increase diversity and global search capability. To validate proposed algorithm, IBFA is compared with particle swarm optimization (PSO) algorithm on IEEE 39-bus system. The results prove that IBFA performs better than PSO in global search and convergence in ADS planning.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2542-2547
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• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.