• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.927-936
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• 2002

This paper presents the extraction of a feature by motion vector for efficient content-based retrieval for digital video. in this paper, divided by general size block for the current frame by video, using BMA(block matching algorithm) for an estimate by block move based on a time frame. but in case BMA appeared on a different pattern fact of motion in the vector obtain for the BMA. solve in this a problem to application for full search method this method is detected by of on many calculations. I propose an alternative plan in this paper Limit the search region to $\pm$15 and search is a limit integer pixel. a result, in this paper is make an estimate motion vector in more accurately using motion vector in adjoin in blocks. however, refer to the block vector because occurrence synchronism. Such addition information is get hold burden receive to transmit therefore, forecasted that motion feature each block and consider for problems for establish search region. in this paper Algorithm based to an examination Motion Estimation method by for motion Compensation is proposed.

• Journal of IKEEE
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• v.24 no.3
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• pp.791-796
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• 2020

In this paper, "Q-Bone", a 3^rd generation blockchain platform with enhanced security and flexibility, was developed. As a 3^rd generation blockchain platform, it exploits BP (block producer) to increase processing speed. It has many advantages as follows. It improves both security and speed by mixing RSA (Rivest-Shamir-Adleman) and AES (advanced encryption standard). It improves flexibility by exploiting gateway to convert between apps and blockchain with different programming language. It increases processing speed by combining whole transactions into one block and distribute it when too many transactions occur. It improves search speed by inserting sequence hash into transaction data. It was implemented and applied to pet communication service and academy-instructor-student matching service, and it was verified to work correctly and effectively. Its processing speed is 3,357 transactions/second, which shows excellent performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1234-1244
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• 2009

Nowadays, the traffic type and behavior are extremely diverse due to the appearance of various services and applications on Internet, which makes the need of application-level traffic classification important for the efficient management and control of network resources. Although lots of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in terms of accuracy and completeness. In this paper we propose an application traffic classification method using statistic signatures, defined as a directional sequence of packet size in a flow, which is unique for each application. The statistic signatures of each application are collected by our automatic grouping and extracting mechanism which is mainly described in this paper. By matching to the statistic signatures we can easily and quickly identify the application name of traffic flows with high accuracy, which is also shown by comprehensive excrement with our campus traffic data.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.44-51
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• 2007

For real-time recognizing of model objects in remote position a new Neural Networks algorithm is proposed. The proposed neural networks technique is the real time computation methods through the inter-node diffusion. In the networks, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of objects, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.7-13
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• 1996

MASF is a kind of temporal filter proposed for noise reduction and temporal band limitation. MASF uses motion vectors to extract temporal information in spatial domain. Therefore, inaccurate motion information causes some distortions in MASF operation. Currently, bilinear interpolation after BMA(Block Matching Algorithm) is used for the motion estimation sheme of MASF. But, this method results in unreliable estimation when the object in image sequence has larger movement than the maximum displacement assumed in BMA or the input images are severely corrupted with noise. In order to i:;olve this problem, we analyse the effect of inaccurate motion on MASF and propose a hierarchical motion estimation algorithm based on the analysis results. Experimental results show that the proposed method produces reliable output under large motion and noisy situations.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3
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• pp.178-185
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• 2005

This paper presents a new music identification method using probabilistic and dynamic characteristics of melody. The propo3ed method uses pitch and MFCC parameters as feature vectors for the characteristics of music notes and represents melody pattern by pitch histogram and temporal sequence of codeword indices. We also propose a new pattern matching method for the hybrid method. We have tested the proposed algorithm in small (drama OST) and broad (1.005 popular songs) search spaces. The experimental results on search areas of OST and 1,005 popular songs showed better performance of the proposed method over conventional methods. We achieved the performance improvement of average $9.9\%$ and $10.2\%$ in error reduction rate on each search area.

• Korean journal of food and cookery science
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• v.24 no.1
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• pp.11-15
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• 2008

In searching for yeast to be utilized as biocontrol agents, a single yeast strain was isolated from Camellia sinensis based on its morphological, cultural, physiological, and biochemical properties, as well as by molecular techniques. This single strain was pink to red in color and designated as strain JY-1. The effects of temperature, pH, NaCl concentration, and ethanol concentration on the growth of the JY-l strain were examined for the JY-1. Growth occurred at temperatures ranging from 20 to $35^{\circ}C$, and between pH 3.0 and 12.0, with optimal growth at $25-30^{\circ}C$ and pH 5.0. The yeast also grew in the presence of 0-2% (w/v) NaCl and 0-4% (v/v) EtOH. The isolate was further classified based on biochemical characteristics using the VITEK system. The biochemical data obtained using this system were similar to those of Rhodotorula glutinis/Rhodotorula mucilaginosa (exhibiting a 93% matching level). Molecular phylogenetic analysis based on l8S rDNA sequences indicated that the yeast represented a basidiomycetous species, and its highest degree of sequence similarity was with Rhodosporidium azoricum, strain JCM11251 (99%).

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.103-111
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• 2011

In this paper, we propose a Geometrical Centroid Contour Distance(GCCD) which is described by shape signature based on contour sequence. The proposed method uses geomertrical relation features instead of the absolute angle based features after it was normalized and aligned with dominant feature of the shape. Experimental result with MPEG-7 CE-Shape-1 Data Set reveals that our method has low time/spatial complexity and scale/rotation robustness than the other methods, showing that the precision of our method is more accurate than the conventional desctiptors. However, performance of the GCCD is limited with concave and complex shaped objects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2579-2585
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• 2010

This paper proposes a dynamic human pose tracking method using motion-based search strategy from an image sequence obtained from a monocular camera. The proposed method compares the image features between 3D human model projections and real input images. The method repeats the process until predefined criteria and then estimates 3D human pose that generates the best match. When searching for the best matching configuration with respect to the input image, the search region is determined from the estimated 2D image motion and then search is performed randomly for the body configuration conducted within that search region. As the 2D image motion is highly constrained, this significantly reduces the dimensionality of the feasible space. This strategy have two advantages: the motion estimation leads to an efficient allocation of the search space, and the pose estimation method is adaptive to various kinds of motion.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.655-660
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• 2009

The context prediction algorithms are not suitable to provide real-time personalized service for users in context-awareness environment. The algorithms have problems like time delay in training data processing and the difficulties of implementation in real-time environment. In this paper, we propose a prediction algorithm with user modeling to shorten of processing time and to improve the prediction accuracy in the context prediction algorithm. The algorithm uses moving path of user contexts for context prediction and generates user model by time-series analysis of user's moving path. And that predicts the user context with the user model by sequence matching method. We compared our algorithms with the prediction algorithms by processing time and prediction accuracy. As the result, the prediction accuracy of our algorithm is similar to the prediction algorithms, and processing time is reduced by 40% in real time service environment.