• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.10
• /
• pp.1862-1878
• /
• 2011

With the advances in wireless communication technology and the advent of smartphones, research on location-based services (LBSs) is being actively carried out. In particular, several spatial index methods have been proposed to provide efficient LBSs. However, finding an optimal indexing method that balances query performance and index size remains a challenge in the case of wireless environments that have limited channel bandwidths and device resources (computational power, memory, and battery power). Thus, mechanisms that make existing spatial indexing techniques more efficient and highly applicable in resource-limited environments should be studied. Bitmap-based Spatial Indexing (BSI) has been designed to support LBSs, especially in wireless broadcast environments. However, the access latency in BSI is extremely large because of the large size of the bitmap, and this may lead to increases in the search time. In this paper, we introduce a Selective Bitmap-based Spatial Indexing (SBSI) technique. Then, we propose an Adaptive Bitmap-based Spatial Indexing (ABSI) to improve the tuning time in the proposed SBSI scheme. The ABSI is applied to the distribution of geographical objects in a grid by using the Hilbert curve (HC). With the information in the ABSI, grid cells that have no objects placed, (i.e., 0-bit information in the spatial bitmap index) are not tuned during a search. This leads to an improvement in the tuning time on the client side. We have carried out a performance evaluation and demonstrated that our SBSI and ABSI techniques outperform the existing bitmap-based DSI (B DSI) technique.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.11
• /
• pp.2002-2007
• /
• 2006

The path search method in the telematics system should consider traffic flow of the roads as well as the shortest time because the optimal path with minimized travel time could be continuously changed by the traffic flow. The existing path search methods are not able to cope efficiently with the change of the traffic flow. The search method to use traffic information also needs more computation time than the existing shortest path search. In this paper, a method for efficiency improvement of path search is implemented and its performance is evaluated. The method employs the fixed grid for adjustable heuristic to traffic flow. Moreover, in order to simplify the computation of estimation values, it only adds graded decimal values instead of multiplication operation of floating point numbers with due regard to the gradient between a departure and a destination. The results obtained from the experiments show that it achieves the high accuracy and short execution time as well.

• The Journal of Korea Robotics Society
• /
• v.18 no.1
• /
• pp.1-9
• /
• 2023

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.

• Journal of KIISE:Computer Systems and Theory
• /
• v.33 no.7
• /
• pp.370-379
• /
• 2006

Computational grid provides the environment which integrates v 따 ious computing resources. Grid environment is more complex and various than traditional computing environment, and consists of various resources where various software packages are installed in different platforms. For more efficient usage of computational grid, therefore, some kind of integration is required to manage grid resources more effectively. In this paper, a global scheduler is suggested, which integrates grid resources at meta level with applying various scheduling policies. The global scheduler consists of a mechanical part and three policies. The mechanical part mainly search user queues and resource queues to select appropriate job and computing resource. An algorithm for the mechanical part is defined and optimized. Three policies are user selecting policy, resource selecting policy, and executing policy. These can be defined newly and replaced with new one freely while operation of computational grid is temporarily holding. User selecting policy, for example, can be defined to select a certain user with higher priority than other users, resource selecting policy is for selecting the computing resource which is matched well with user's requirements, and executing policy is to overcome communication overheads on grid middleware. Finally, various algorithms for user selecting policy are defined only in terms of user fairness, and their performances are compared.

• Journal of Welding and Joining
• /
• v.26 no.1
• /
• pp.63-68
• /
• 2008

This paper has aimed to prevent excessive heat input by controlling arc distribution and heat input capacity with pulse GTAW in order to improve weld quality in 0.08mm pressure gauge diaphragm and flange welding parts. A design of experiment was designed using Box-Behnken method to optimize a welding process. The pulse GTAW parameters such as pulse current, base current, pulse duty, frequency and welding speed were set to input variables while hydraulic pressure that represents welding characteristics in diaphragm and flange joint were set to output variables. Based on the test result, a second regression equation was obtained between input and output variables and turned out significant. Besides, an influence of parameters has been confirmed through response surface analysis using the second-order regression equation and optimum welding condition was obtained through a grid-search method. The optimum welding condition was set to pulse current 84.4(A), base current 29.6(A), pulse duty 58.8(%), frequency 10(%), and welding speed 596(mm/min). Then, decent bead shape was acquired with no excessive heat input under the $2.3kgf/cm^2$ of hydrostatic pressure.

• Geomechanics and Engineering
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of KIISE:Computer Systems and Theory
• /
• v.30 no.10
• /
• pp.595-602
• /
• 2003

This Paper presents an efficient hybrid techniques for a standard cell placement. The prototype tool adopts a middle-down methodology in which an n${\times}$m grid is imposed over the layout area and cells are assigned to bins forming a global placement. The optimization technique applied in this phase is based on the Relaxation-Based Local Search (RBLS) framework [12］in which a combinatorial search mechanism is driven by an analytical engine. This enables a more global view of the problem and results in complex modifications of the placement in a single search“move.”Details of this approach including a novel placement legalization procedure are presented. When a global placement converges, a detailed placement is formed and further optimized by the optimal interleaving technique[13］. Experimental results on MCNC benchmarking circuits are presented and compared with the Feng Shui's results in［14］. Solution Qualifies are almost the same as the Feng Shui's results.

• The KIPS Transactions:PartC
• /
• v.17C no.3
• /
• pp.259-270
• /
• 2010

In this paper, a Location Information-based Gradient Routing (LIGR) algorithm is proposed for setting up routing path based on physical location information of sensor nodes in wireless ad-hoc networks. LIGR algorithm reduces the unnecessary data transmission time, route search time, and propagation delay time of packet by determining the transmission direction and search range through the gradient from the source node to sink node using the physical location information. In addition, the low battery nodes are supposed to have the second or third priority in case of forwarding node selection, which reduces the possibility of selecting the low battery nodes. As a result, the low battery node functions as host node rather than router in the wireless sensor networks. The LIGR protocol performed better than the Logical Grid Routing (LGR) protocol in the average receiving rate, delay time, the average residual energy, and the network processing ratio.

• Convergence Security Journal
• /
• v.22 no.2
• /
• pp.105-114
• /
• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• Journal of the Korea Society for Simulation
• /
• v.17 no.4
• /
• pp.229-239
• /
• 2008

A Distributed File System (DFS) provides a mechanism in which a file can be stored across several physical computer nodes ensuring replication transparency and failure transparency. Applications that process large volumes of data (such as, search engines, grid computing applications, data mining applications, etc.) require a backend infrastructure for storing data. And the distributed file system is the central component for such storing data infrastructure. There have been many projects focused on network computing that have designed and implemented distributed file systems with a variety of architectures and functionalities. In this paper, we describe a complete distributed file system which can be used in large-scale search-oriented systems.