• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.459-470
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• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In most DSMS(Data Stream Management System), the selection predicates of continuous queries are grouped or indexed to guarantee these constraints. This paper proposes a new scheme tailed an ASC(Attribute Selection Construct) that collectively evaluates selection predicates containing the same attribute in multiple continuous queries. An ASC contains valuable information, such as attribute usage status, partially pre calculated matching results and selectivity statistics for its multiple selection predicates. The processing order of those ASC's that are corresponding to the attributes of a base data stream can significantly influence the overall performance of multiple query evaluation. Consequently, a method of establishing an efficient evaluation order of multiple ASC's is also proposed. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.5
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• pp.13-20
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• 2013

In order to reduce seismic responses of a structure, additional dampers and vibration control devices are generally considered. Usually, control performance of additional devices are investigated for optimal design without variation of characteristics of a structure. In this study, multi-objective integrated optimization of structure-smart control device is conducted and possibility of reduction of structural resources of a building structure with smart top-story isolation system has been investigated. To this end, 20-story example building structure was selected and an MR damper and low damping elastomeric bearings were used to compose a smart base isolation system. Artificial earthquakes generated based on design spectrum of low-to-moderate seismicity regions are used for structural analyses. Based on numerical simulation results, it has been shown that a smart top-story isolation system can effectively reduce both structural responses and isolation story drifts of the building structure in low-to-moderate seismicity regions. The integrated optimal design method proposed in this study can provide various optimal designs that presents good control performance by appropriately reducing the amount of structural material and damping device.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.15-34
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• 2014

Overall, accuracy as a performance measure does not fully consider modular accuracy: the accuracy of classifying 1 (or true) as 1 is not same as classifying 0 (or false) as 0. A smarter classification algorithm would optimize the classification rules to match the modular accuracies' goals according to the nature of problem. Correspondingly, smarter algorithms must be both more generalized with respect to the nature of problems, and free from decretization, which may cause distortion of the real performance. Hence, in this paper, we propose a novel vertical boosting algorithm that improves modular accuracies. Rather than decretizing items, we use simple classifiers such as a regression model that accepts continuous data types. To improve the generalization, and to select a classification model that is well-suited to the nature of the problem domain, we developed a model selection algorithm with smartness. To show the soundness of the proposed method, we performed an experiment with a real-world application: predicting the intellectual properties of e-transaction technology, which had a 47,000+ record data set.

• Journal of KIISE:Information Networking
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• v.33 no.6
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• pp.420-427
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• 2006

An RFID system is an important technology that could replace the traditional bar code system changing the paradigm of manufacturing, distribution, and service industry. An RFID reader can recognize several hundred tags in one second. Tag identification is done by tags' random transmission of their IDs in a frame which is assigned by the reader at each round. To minimize tag identification time, optimal frame size should be selected according to the number of tags. This paper presents new query optimization methods in RFID systems. Query optimization consists of tag number estimation problem and frame length determination problem. We propose a simple yet efficient tag estimation method and calculate optimal frame lengths that minimize overall query time. We conducted rigorous performance studies. Performance results show that the new tag number estimation technique is more accurate than previous methods. We also observe that a simple greedy method is as efficient as the optimal method in minimizing the query time.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.748-753
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• 2012

In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1764-1770
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• 2007

The binding update of MIPv6 which basically makes a route optimization lets MN bring about high signaling traffic, packet loss and delay. HMIPv6, which introduces the MAP protocol, makes the signaling traffic low, thereby reducing the packet losses and delay. However, it still has the same problem in MIPv6 in the case of macro mobility. This paper proposes HMIPv6 with the address insurance policy. It makes MAP prepare LCoA and RCoA before the macro handover happens. When it happens, MN is able to use them after the registration is done in the foreign network. The perormance can be improved because MAP is composed to assure the address in advance. In addition the MAP sends the BU message during the handover, thereby making the proposed scheme better. The simulation shows that the proposed scheme is about 33% shorter than HMIPv6 in the handover delay and about 22% less than FMIPv6 in the packet loss.

• Smart Media Journal
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• v.8 no.2
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• pp.9-15
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• 2019

Since the computational complexity for hologram generation increases exponentially with respect to the size of the point cloud, parallel processing using CUDA and/or OpenCL library based on multiple GPUs has recently become popular. The CUDA kernel for parallelization needs to consist of threads, blocks, and grids properly in accordance with the number of cores and the memory size in the GPU. In addition, in case of multiple GPU environments, the distribution in grid-by-grid, in block-by-block, or in thread-by-thread is needed according to the number of GPUs. In order to evaluate the performance of CGH generation, we compared the computational speed in CPU, in single GPU, and in multi-GPU environments by gradually increasing the number of points in a point cloud from 10 to 1,000,000. We also present a memory structure design and a calculation method required in the CUDA-based parallel processing to accelerate the CGH (Computer Generated Hologram) generation operation in multiple GPU environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.3
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• pp.65-76
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• 2005

Speeding up scalar multiplication of an elliptic curve point has been a prime approach to efficient implementation of elliptic curve schemes such as EC-DSA and EC-ElGamal. Koblitz introduced a $base-{\phi}$ expansion method using the Frobenius map. Kobayashi et al. extended the $base-{\phi}$ scalar multiplication method to suit Optimal Extension Fields(OEF) by introducing the table reference method. In this paper we propose an efficient scalar multiplication algorithm on elliptic curve over OEF. The proposed $base-{\phi}$ scalar multiplication method uses an optimized batch technique after rearranging the computation sequence of $base-{\phi}$ expansion usually called Horner's rule. The simulation results show that the new method accelerates the scalar multiplication about $20\%{\sim}40\%$ over the Kobayashi et al. method and is about three times as fast as some conventional scalar multiplication methods.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.437-444
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• 2022

Recently, research on mobile edge services has been conducted to handle computationally intensive and latency-sensitive tasks occurring in wireless networks. However, MEC, which is fixed on the ground, cannot flexibly cope with situations where task processing requests increase sharply, such as commuting time. To solve this problem, a technology that provides edge services using UAVs (Unmanned Aerial Vehicles) has emerged. Unlike ground MEC servers, UAVs have limited battery capacity, so it is necessary to optimize energy efficiency through load balancing between UAV MEC servers. Therefore, in this paper, we propose a load balancing technique with consideration of the energy state of UAVs and the mobility of vehicles. The proposed technique is composed of task offloading scheme using genetic algorithm and task migration scheme using Q-learning. To evaluate the performance of the proposed technique, experiments were conducted with varying mobility speed and number of vehicles, and performance was analyzed in terms of load variance, energy consumption, communication overhead, and delay constraint satisfaction rate.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.69-74
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• 2022

Big data related analysis techniques, the prescriptive analytics methodology improves the performance of passive learning models by ensuring that active learning secures high-quality learning data. Prescriptive analytics is a performance maximizing process by enhancing the machine learning models and optimizing systems through active learning to secure high-quality learning data. It is the best subscription value analysis that constructs the expensive category data efficiently. To expand the value of data by collecting research field, research propensity, and research activity information, customized researcher through prescriptive analysis such as predicting the situation at the time of execution after data pre-processing, deriving viable alternatives, and examining the validity of alternatives according to changes in the situation Provides research information service.