• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1098-1105
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• 1999

The caches in distributed shared memory systems enhance the performance by reducing memory access latency and communication overhead, but they must solve the cache coherence problem. This paper proposes a new directory protocol to solve the cache coherence problem and to improve the system performance in distributed shared memory systems. To maintain the cache coherence of shared data, processors within a limited distance reduce the communication overhead by using a bit-vector like the full directory scheme. Processors over a limited distance store pointers in a directory pool. Since the bit-vector and the directory pool remove the unnecessary cache invalidations, the proposed scheme reduces the communication traffic and improves the system performance. The dynamic limited directory scheme reduces the communication traffic up to 66 percents compared with the limited directory scheme and the number of directory access up to 27 percents compared with the dynamic pointer allocation scheme.

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.9-20
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• 2012

In computer vision, it is very important to obtain reliable corresponding feature points. However, we know it is not easy to find the corresponding feature points exactly considering by scaling, lighting, viewpoints, etc. Lots of SIFT methods applies the invariant to image scale and rotation and change in illumination, which is due to the feature vector extracted from corners or edges of object. However, SIFT could not find feature points, if edges do not exist in the area when we extract feature points along edges. In this paper, we present a new placement method of marker to improve the performance of SIFT feature detection and matching between different view of an object or scene. The shape of the markers used in the proposed method is formed in a semicircle to detect dominant direction vector by SIFT algorithm depending on direction placement of marker. We applied De Bruijn sequence for the markers direction placement to improve the matching performance. The experimental results show that the proposed method is more accurate and effective comparing to the current method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.103-112
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• 1999

Genetic algorithm, which uses principles of natural selection and population genetics, is an efficient method to find out an optimal solution. In conventional genetic algorithms, however, the size of gene pool needs to be increased to insure a convergency. Therefore, many memory spaces and much computation time were needed. Also, since child chromosomes were generated by chromosome crossover and gene mutation, the algorithms have a complex structure. Thus, in this paper, a compact stereo matching algorithm using a population-based incremental learning based on probability vector is proposed to reduce these problems. The PBIL method is modified for matching environment. Since th proposed algorithm uses a probability vector and eliminates gene pool, chromosome crossover, and gene mutation, the matching algorithm is simple and the computation load is considerably reduced. Even though the characteristics of images are changed, stable outputs are obtained without the modification of the matching algorithm.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1588-1596
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• 2004

Recently, many WMS(Web Mapping Services) and POI(Point of Interest) service on to be in service on the Internet using Web CIS(Geographic Information System) as information Technology and computer HW are evolved faster in its speed, network bandwidth and features. The Web GIS is, however, limited and constrained on the specification of its system configuration, the service class provided and the presentation methodology of a map. As the mobile Internet becomes popular in mobile service, Web GIS service on mobile environment is strongly required and to be provided by LBS(Location Based Service) on a mobile client such as PDA with location information of the user. In this paper, we made an effort to design and implement a GIS computing environment by thin client for mobile web mapping service. For implementing the thin client GIS computing environment, we were using NGII's(National Geographic Information Institute's) DXF map, representing the map by SVG(Scalable Vector Graphics) recommended by OGC(OpenGis Consortium), and adapting standard XML web service to provide the thin client GIS service on PDA by applying the location information of the user in realtime with GPS on mobile environment.

• The Journal of the Acoustical Society of Korea
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• v.35 no.6
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• pp.501-509
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• 2016

We propose a method to improve the robustness of speaker verification on short test utterances. The accuracy of the state-of-the-art i-vector/probabilistic linear discriminant analysis systems can be degraded when testing utterance durations are short. The proposed method compensates for utterance variations of short test feature vectors using deep neural networks. We design three different types of DNN (Deep Neural Network) structures which are trained with different target output vectors. Each DNN is trained to minimize the discrepancy between the feed-forwarded output of a given short utterance feature and its original long utterance feature. We use short 2-10 s condition of the NIST (National Institute of Standards Technology, U.S.) 2008 SRE (Speaker Recognition Evaluation) corpus to evaluate the method. The experimental results show that the proposed method reduces the minimum detection cost relative to the baseline system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.1
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• pp.44-54
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• 2001

In this paper, a novel test method is proposed to detect the hard and soft fault in analog circuits. The proposed test method makes use of the offset voltage, which is one of the op-amps characteristics. During the test mode, CUT is modified to unit gain op-amps with feedback loop. When the input of the op-amp is grounded, a good circuit has a small offset voltage, but a faulty circuit has a large offset voltage. Faults in the op-amp which cause the offset voltage exceeding predefined range of tolerance can be detected. In the proposed method, no test vector is required to be applied. Therefore the test vector generation problem is eliminated and the test time and cost is reduced. In this note, the validity of the proposed test method has been verified through the example of the dual slope A/D converter. The HSPICE simulations results affirm that the presented method assures a high fault coverage.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.63-70
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• 2009

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.