• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.983-990
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The CD histogram is a technique which selves this problem by keeping four sub-histograms corresponding to the four points of rectangle. Although It provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors nay be occurred when it is applied to real applications. In this paper, we propose selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models : \circled1 probabilistic model which considers the query window area ratio, \circled2 probabilistic model which considers intersection area between a given grid and objects. Our method has the capability of eliminating an impact of the restriction on query window which the existing cumulative density histogram has. We experimented with real datasets to evaluate the proposed methods. Experimental results show that the proposed technique is superior to the existing selectivity estimation techniques. Furthermore, selectivity estimation technique based on probabilistic model considering the intersection area is very accurate(less than 5% errors) at 20% query window. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.138-141
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• 2010

In this paper, we propose a semi-automatic method for semantic video object extraction which extracts meaningful objects from an input sequence with one correctly segmented training image. Given one correctly segmented image acquired by the user's interaction in the first frame, the proposed method automatically segments and tracks the objects in the following frames. We formulate the semantic object extraction procedure as an energy minimization problem at the fragment level instead of pixel level. The proposed energy function consists of two terms: data term and smoothness term. The data term is computed by considering patch similarity, color, and motion information. Then, the smoothness term is introduced to enforce the spatial continuity. Finally, iterated conditional modes (ICM) optimization is used to minimize energy function in a globally optimal manner. The proposed semantic video object extraction method provides faithful results for various types of image sequences.

• Journal of the Microelectronics and Packaging Society
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• v.19 no.4
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• pp.13-18
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• 2012

By the need for high-speed data transmission in PCB, the studies on the optical PCB has been conducted with optical interconnection and its packaging technology. Particularly, the polymer-based optical interconnection has been extensively studied with the advantages such as cost-effective and ease of process. For high-efficiency and passive alignment, the studies were performed using the 45 degree mirrors, MT connector, and etc. In this work, integrated PLC device and fiber alignment array block was fabricated by using imprint technology to solve the alignment and array problem of optical device and the optical fiber. The fabricated integrated block for optical interconnection of PLC device has achieved higher precision of decreasing the dimensional error of the patterns by optimization of process and its insertion loss has an average value of 4.03dB, lower than criteria specified by international standard. In addition, a optical waveguide with built-in lens has been proposed for high-efficiency and passive alignment. By simulation, it was confirmed that the proposed structure has higher coupling efficiency than conventional no-lens structure and has the broad tolerance for the spatial offset of optical waveguide.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3543-3557
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• 2017

Compression is a very important technique for remotely sensed hyperspectral images. The lossless compression based on the recursive least square (RLS), which eliminates hyperspectral images' redundancy using both spatial and spectral correlations, is an extremely powerful tool for this purpose, but the relatively high computational complexity limits its application to time-critical scenarios. In order to improve the computational efficiency of the algorithm, we optimize its serial version and develop a new parallel implementation on graphics processing units (GPUs). Namely, an optimized recursive least square based on optimal number of prediction bands is introduced firstly. Then we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The proposed parallel method properly exploits the low-level architecture of GPUs and has been carried out using the compute unified device architecture (CUDA). The GPU parallel implementation is compared with the serial implementation on CPU. Experimental results indicate remarkable acceleration factors and real-time performance, while retaining exactly the same bit rate with regard to the serial version of the compressor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9A
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• pp.856-867
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• 2006

This paper proposes a new algorithm for opportunistic scheduling that take advantage of both multiuser diversity and power control. Motivated by the multicast RTS and priority-based CTS mechanism of OSMA protocol, we propose an opportunistic packet scheduling with power control scheme based on IEEE 802.11 MAC protocol. The scheduling scheme chooses the best candidate receiver for transmission by considering the SINR at the nodes. This mechanism ensures that the transmission would be successful. The power control algorithm on the other hand, helps reduce interference between links and could maximize spatial reuse of the bandwidth. We then formulate a convex optimization problem for minimizing power consumption and maximizing net utility of the system. We showed that if a transmission power vector satisfying the maximum transmission power and SINR constraints of all nodes exist, then there exists an optimal solution that minimizes overall transmission power and maximizes utility of the system.

• Korean Journal of Geomatics
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• v.1 no.1
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• pp.95-101
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• 2001

In a route plan, the route selection is a complicated problem to consider the spatial distribution and influence through overall related data and objective analysis on the social, economic and technical condition. The developed system in this study was compared and estimated by deciding a practical section for its validity and efficiency. Using Geographic Information System (GIS), the various information required for route selections in database was constructed, the characteristics of subject area by executing three-dimensional terrain analysis was grasped effectively, and the control point through buffering, overlay and location operation was extracted. An optimum route was selected by calculating the sum of alternatives to the sub-criteria weight, and from this result, there is a difference between real route and proposed route according to the prioritization of decision criteria based on the importance. This research could be constructed and applied geospatial information to the reasonable route plan and an optimum route selection efficiently using GIS. Therefore, the applications are presented by applying Analytic Hierarchy Process (AHP) to the decision-making of information needed in route selection.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1156-1170
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• 2019

In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5978-5999
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• 2018

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.51-62
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• 2022

Purpose: This study analyzes accessibility of outdoor evacuation places for earthquake and the accessibility improvement effects when expanding the evacuation places in accessibility-deficient areas. In order to consider real-world evacuees, the accessibility analysis is based on service population not on resident population. Method: Location-allocation model as a GIS-based spatial optimization mode is used to analyze accessibility and vulnerable areas to evacuation places. Of location-allocation problem types, 'Maximize Coverage' method is chosen to allocate as many potential evacuees as possible to evacuation places. And impedence cutoffs or evacuation distances (times) are applied to three classes: 500m (7.5 minutes), 1,000m (15 minutes), and 1,500m (22.5 minutes). Case study area is Jung-gu areas, Seoul as a high-density downtown area. Result: Results show that accessibility-deficient areas and population to evacuation places are much more in service population than in resident population. Accessibility is significantly improved when increases when expanding the evacuation places in accessibility-deficient areas. Yet, accessibility-deficient areas are still remained since available lands are insufficient in the high-density downtown area. Conclusion: The study suggests that temporary evacuation facilities like outdoor evacuation places for earthquake need to consider real potential evacuees based not only on resident population but also on service population. Also, policy measures to provide emergency shelters need to more utilize spatial optimization tools like location-allocation model.