• Journal of Power Electronics
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• v.18 no.3
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• pp.841-852
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• 2018

This paper presents a dual-algorithm search method (GWO-GSO) combining grey wolf optimization (GWO) and golden-section optimization (GSO) to realize maximum power point tracking (MPPT) for photovoltaic (PV) systems. First, a modified grey wolf optimization (MGWO) is activated for the global search. In conventional GWO, wolf leaders possess the same impact on decision-making. In this paper, the decision weights of wolf leaders are automatically adjusted with hunting progression, which is conducive to accelerating hunting. At the later stage, the algorithm is switched to GSO for the local search, which play a critical role in avoiding unnecessary search and reducing the tracking time. Additionally, a novel restart judgment based on the quasi-slope of the power-voltage curve is introduced to enhance the reliability of MPPT systems. Simulation and experiment results demonstrate that the proposed algorithm can track the global maximum power point (MPP) swiftly and reliably with higher accuracy under various conditions.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• Journal of KIISE
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• v.43 no.9
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• pp.1026-1033
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• 2016

Path-finding on a grid map is a problem generally addressed in the fields of robotics, intelligent agents, and computer games. As technology advances, virtual game worlds tend to be represented more accurately and more realistically, resulting in an excessive increase in the number of grid tiles and in path-search time. In this study, we propose a path-finding algorithm that allows a prompt response to real-time queries by constructing a reduced state space and by precomputing all possible paths in an offline preprocessing stage. In the preprocessing stage, we vertically decompose free space on the grid map, construct a connectivity graph where nodes are the decomposed regions, and store paths between all pairs of nodes in matrix form. In the real-time query stage, we first find the nodes containing the query points and then retrieve the corresponding stored path. The proposed method is simulated for a set of maps that has been used as a benchmark for grid-based path finding. The simulation results show that the state space and the search time decrease significantly.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.49-60
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• 2010

H.264/AVC offers a better encoding efficiency than conventional video standards by adopting many new encoding techniques. However, the advanced coding techniques also add to the overall complexity for H.264/AVC encoder. Accordingly, it is necessary to perform optimization to alleviate the level of complexity for the video encoder. The amount of computation for motion estimation is of particular importance. In this paper, we propose a diamond web-grid search algorithm combined with efficient stationary block skip method which employs full diamond and dodecagon search patterns, and the variable thresholds are used for performing an effective skip of stationary blocks. The experimental results indicate that the proposed technique reduces the computations of the unsymmetrical-cross multi-hexagon-grid search algorithm by up to 12% while maintaining a similar PSNR performance.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• YAKHAK HOEJI
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• v.38 no.6
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• pp.721-724
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• 1994

According to computer graphics/Grid search analysis, ${\beta}-keto$ carboxylic acid of nalidixic acid which has an antibacterial activity as DNA-gyrase inhibitor has been known to have got four different conformational energy values. In orders, the energy value of conformation A,B,C and D was -6.603, -4.114, -1.766 and 7.327 kcal/mol. The difference of energy value between conformation A and D was 13.9 kcal/mol. Usually conformation C was used in literature. However, it had a energy value of -1.766 kcal/mol as the result of the analysis which is about 5 kcal/mol higher than the most stable conformation A. Therefore, conformation A is expected to be more stable than conformation C.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.55-62
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• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• The KIPS Transactions:PartA
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• v.11A no.1
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• pp.75-80
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• 2004

To interact database data with GRID system, implementation and installation of data manipulation module which manipulates database data and its index is required. Developing a search system searching data on web-based database, and integrating it with grid system, it is possible that searching data on web and use it directly on GRID system without independent data module. So, we can build easy and effective grid system, and the system could have more flexible architecture adapting data change. In this paper, we propose a searching system which interacting web-based database with GRID systems. We integrated the searching system with a bio god system which runs virtual screening jobs. As a result, UB Grid (Universal Bio Grid) is constructed. Developer could reduce time and effort required to integrate web data to GRID system, and user could use UB Grid system easily and effectively.