• Archives of Plastic Surgery
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• v.43 no.2
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• pp.204-209
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• 2016

Temporal hollowing occurs to varying degrees after pterional craniotomy. The most common cause of temporal hollowing is a bony defect of the pterional and temporal regions due to the resection of the sphenoid ridge and temporal squama for adequate exposure without overhang. The augmentation of such bony defects is important in preventing craniofacial deformities and postoperative hollowness. Temporal cranioplasty has been performed using a range of materials, such as acrylics, porous polyethylene, bone cement, titanium, muscle flaps, and prosthetic dermis. These methods are limited by the risk of damage to adjacent tissue and infection, a prolonged preparation phase, the possibility of reabsorption, and cost inefficiency. We have developed a method of temporal augmentation using a calvarial onlay graft as a single-stage neurosurgical reconstructive operation in patients requiring craniotomy. In this report, we describe the surgical details and review our institutional outcomes. The patients were divided into pterional craniotomy and onlay graft groups. Clinical temporal hollowing was assessed using a visual analog scale (VAS). Temporal soft tissue thickness was measured on preoperative and postoperative computed tomography (CT) studies. Both the VAS and CT-based assessments were compared between the groups. Our review indicated that the use of an onlay graft was associated with a lower VAS score and left-right discrepancy in the temporal contour than were observed in patients undergoing pterional craniotomy without an onlay graft.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.25-33
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• 1997

A DEDS is a system whose states change in response to the occurence of events from a predefined event set. In this paper, we consider the optimal control and reasoning problem for Discrete Event Systems(DES) in the Temporal Logic Framework(TEL) which have been recnetly defined. The TLE is enhanced with objective functions(event cost indices) and a measurement space is alos deined. A sequence of event which drive the system form a give initial state to a given final state is generated by minimizing a cost functioin index. Our research goal is the reasoning of optimal trajectory and the design of the optimal controller for DESs. This procedure could be guided by the heuristic search methods. For the heuristic search, we suggested the Stochastic Ruler algorithm, instead of the A algorithm with difficulties as following ; the uniqueness of solutions, the computational complexity and how to select a heuristic function. This SR algorithm is used for solving the optimal problem. An example is shown to illustrate our results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.321-330
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• 2015

Atmospheric correction is an essential part in time-series analysis on biophysical parameters of surface features. In this study, we tried to examine possible problems in atmospheric correction of multitemporal High Spatial Resolution (HSR) images obtained from two different sensor systems. Three KOMPSAT-2 and two IKONOS-2 multispectral images were used. Three atmospheric correction methods were applied to derive surface reflectance: (1) Radiative Transfer (RT) - based absolute atmospheric correction method, (2) the Dark Object Subtraction (DOS) method, and (3) the Cosine Of the Uun zeniTh angle (COST) method. Atmospheric correction results were evaluated by comparing spectral reflectance values extracted from invariant targets and vegetation cover types. In overall, multi-temporal reflectance from five images obtained from January to December did not show consistent pattern in invariant targets and did not follow a typical profile of vegetation growth in forests and rice field. The multi-temporal reflectance values were different by sensor type and atmospheric correction methods. The inconsistent atmospheric correction results from these multi-temporal HSR images may be explained by several factors including unstable radiometric calibration coefficients for each sensor and wide range of sun and sensor geometry with the off-nadir viewing HSR images.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.117-129
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• 2008

The cost-based scheduling work has been done in both the Operations Research (OR) and Artificial Intelligence (AI) literature. To deal with more realistic problems, AI-based heuristic scheduling approach with non-regular performance measures has been studied. However, there has been little research effort to develop a full inter-job cost propagation algorithm (CPA) for different jobs having multiple downstream and upstream activities. Without such a CPA, decision-making in scheduling heuristics relies upon local, incomplete cost information, resulting in poor schedule performance from the overall cost minimizing objective. For such a purpose, we need two types of CPAs : intra-job CPA and inter-job CPA. Whenever there is a change in cost information of an activity in a job in the process of scheduling, the intra-job CPA updates cost curves of other activities connected through temporal constraints within the same job. The inter-job CPA extends cost propagation into other jobs connected through precedence relationships. By utilizing the cost information provided by CPAs, we propose cost-based scheduling heuristics that attempt to minimize the total schedule cost. This paper develops inter-job CPAs that create and update cost curves of each activity in each search state, and propagate cost information throughout a whole network of temporal constraints. Also we propose various cost-based scheduling heuristics that attempt to minimize the total schedule cost by utilizing the cost propagation algorithm.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.515-517
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• 2004

Although sustainable development of a city should in theory be based on updated spatial information like land cover/use changes, in practice there are no effective tools to get such information. However the development of satellite and sensor technologies has increased the supply of high resolution satellite data, allowing cost-effective, multi-temporal monitoring. Especially KOMPSAT-1(KOrea Multi-Purpose SATellite) acquired a large number of images of the whole Korean peninsula and covering some large cities a number of times. In this study land-use patterns and trends of Daejeon from the year 2000 to the year 2003 will be considered using land use maps which are generated by manual interpretation of multi-temporal KOMPSAT EOC imagery and to show the possibility of using high resolution satellite remote sensing data for urban analysis.

• ETRI Journal
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• v.25 no.5
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• pp.275-287
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• 2003

We present a high performance cache structure with a hardware prefetching mechanism that enhances exploitation of spatial and temporal locality. Temporal locality is exploited by selectively moving small blocks into the direct-mapped cache after monitoring their activity in the spatial buffer. Spatial locality is enhanced by intelligently prefetching a neighboring block when a spatial buffer hit occurs. We show that the prefetch operation is highly accurate: over 90% of all prefetches generated are for blocks that are subsequently accessed. Our results show that the system enables the cache size to be reduced by a factor of four to eight relative to a conventional direct-mapped cache while maintaining similar performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.287-288
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• 2009

Multi-query optimization (MQO) is a critical research issue in the real-time data stream management system (DSMS). We propose to address this problem in the ubiquitous GIS (u-GIS) environment, focusing on grouping 'similar' spatio-temporal queries incrementally into N clusters so that they can be processed virtually as N queries. By minimizing N, the overlaps in the data requirements of the raw queries can be avoided, which implies the reducing of the total disk I/O cost. In this paper, we define the spatio-temporal query clustering problem and give a data cubing approach (Q-cube), which is expected to be implemented in the cloud computing paradigm.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.857-860
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• 2000

In this letter, The proposed algorithm has processed the motion estimation and compensation only in the region which has the motion. By considering the temporal redundancies between the successive frames, the bit cost for representing the motion are reduced efficiently. The performance of them proposed technique show the superior result to the existing methods' in terms of both PSNR and coding cost.