• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.333-336
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• 2001

본 논문에서는 영상 시퀀스 상에서 물체의 가려짐을 고려하여 상대적인 깊이 순서에 의해 정렬되는 계층을 분리하기 위한 새로운 움직임 분할 방법을 제안한다. 블록을 기반으로 한 움직임 추정 및 클러스터링 과정을 통하여 각 계층에 대한 블록영역을 구하고, 이 블록영역에 대하여 윤곽선 추출을 이용하여 각 계층에 대한 정확한 객체를 분리할 수 있다. 이러한 움직임 분할방법을 통한 동영상의 계층적인 표현은 영상에서 원하지 않는 물체, 전경, 배경의 제거나 기존의 영상을 이용한 새로운 영상의 합성에 이용될 수 있으며, 분할을 통해 얻어진 객체는 영상 압축, 영상 합성 등을 위한 데이터베이스에 저장되어 응용될 수 있다.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.125-133
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• 2014

The column-oriented database storage is a very advanced model for large-volume data analysis systems because of its superior I/O performance. Traditional data storages exploit row-oriented storage where the attributes of a record are placed contiguously in hard disk for fast write operations. However, for search-mostly datawarehouse systems, column-oriented storage has become a more proper model because of its superior read performance. Recently, solid state drive using MLC flash memory is largely recognized as the preferred storage media for high-speed data analysis systems. The features of non-volatility, low power consumption, and fast access time for read operations are sufficient grounds to support flash memory as major storage components of modern database servers. However, we need to improve traditional transaction management scheme due to the relatively slow characteristics of column compression and flash operation as compared to RAM memory. In this research, we propose a new scheme called Column-aware Multi-Version Locking (CaMVL) scheme for efficient transaction processing. CaMVL improves transaction performance by using compression lock and multi version reads for efficiently handling slow flash write/erase operation in lock management process. We also propose a simulation model to show the performance of CaMVL. Based on the results of the performance evaluation, we conclude that CaMVL scheme outperforms the traditional scheme.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.331-341
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• 2015

The main purpose of this study is reducing the cost and effort for characterization of tensile strength of fiber reinforced concrete, in order to use in structural design. For this purpose, in this study, test for fiber reinforced concrete was carried out. Because fiber reinforced concrete is consisted of diverse material, it is hard to define the correlation between mix proportions and strength. Therefore, compressive strength test and tensile strength test were carried out for the range of smaller than 100 MPa of compressive strength and 0.25~1% of steel fiber volume fraction. as a results of test, two types of tensile strength were highly affected by compressive strength of concrete. However, increase rate of tensile strength was decreased with increase of compressive strength. Increase rate of tensile strength was decreased with increase of fiber volume fraction. Database was constructed using previous research data. Because estimation equations for tensile strength of fiber reinforced concrete should be multiple variable function, linear regression is hard to apply. Therefore, in this study, we decided to use the ANN(Artificial Neural Network). ANN was constructed using multiple layer perceptron architecture. Sigmoid function was used as transfer function and back propagation training method was used. As a results of prediction using artificial neural network, predicted values of test data and previous research which was randomly selected were well agreed with each other. And the main effective parameters are water-cement ratio and fiber volume fraction.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.519-526
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• 2013

ECG signal has the feature that is repeated in a cycle of P, Q, R, S, and T waves and is sampled at a high sampling frequency in general. By using the feature of periodic ECG signals, maximizing compression efficiency while minimizing the loss of important information for diagnosis is required. However, the periodic characteristics of such amplitude and period is not constant by measuring time and patients. Even though measured at the same time, the patient's characteristics display different periodic intervals. In this paper, an adaptive multi-level coding is provided by coding adaptively the dominant and non-dominant signal interval of the ECG signal. The proposed method can maximize the compression efficiency by using a multi-level code that applies different compression ratios considering information loss associated with the dominant signal intervals and non-dominant signal intervals. For the case of long time measurement, this method has a merit of maximizing compression ratio compared with existing compression methods that do not use the periodicity of the ECG signal and for the lossless compression coding of non-dominant signal intervals, the method has an advantage that can be stored without loss of information. The effectiveness of the ECG signal compression is proved throughout the experiment on ECG signal of MIT-BIH arrhythmia database.

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.379-389
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• 2006

Map databases for CNS (Car Navigation System) can be accessed quickly and compressed efficiently due to that these are usually recorded as in a PSF (Physical Storage Format). However, it is difficult to create and manage data storage based on a file-system. To solve these problems, DBMS needs to be combined with spatial data management. Therefore, we developed an embedded DBMS with which to store data and conduct quick searches in CNS. Spatial data could be easily managed and accessed using the compression method, Multi-Link, spatial index, and spatial division. In the result, the proposed embedded DBMS searched quickly and stably supported data management. If synchronization is applied in DBMS, it is expected to utilize the advantages of an embedded DBMS.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.4
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• pp.50-58
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• 2001

The image information, used most frequently in multimedia, is visual and spatial information. It has several characters including the diversity of storage and output methods, large capacity, spatial relationship expression, and irregularity. Therefore, the various researches for methods of storing efficiently, managing, searching such image data are going on. And recently, it has arisen the movement of international standardization, MPEG-7 for searching contents base in multimedia environment. Especially, the research for implementation of more effective image database searching system important subject, because the practical image search system which can storage a lot of image information as database and query, search them has not generalized. Now the image search system based on text has researched to high degree, but it has many shortages so that nowadays the researches for searching system based on contents are going on. This research has used the wavelet conversion largely using in image processing instead of DCT method largely using in existent system, and so it had met similar and precise results than prior methods by image compression and extraction of specific vector.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.927-934
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• 2003

Database query optimates the selectivety of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show adventages of the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.496-512
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• 2007

In this paper, we propose a novel JPEG-2000 compressed image retrieval system using feature vector extracted through partial entropy decoding. Main idea of the proposed method is to utilize the context information that is generated during entropy encoding/decoding. In the framework of JPEG-2000, the context of a current coefficient is determined depending on the pattern of the significance and/or the sign of its neighbors in three bit-plane coding passes and four coding modes. The contexts provide a model for estimating the probability of each symbol to be coded. And they can efficiently describe texture images which have different pattern because they represent the local property of images. In addition, our system can directly search the images in the JPEG-2000 compressed domain without full decompression. Therefore, our proposed scheme can accelerate the work of retrieving images. We create various distortion and similarity image databases using MIT VisTex texture images for simulation. we evaluate the proposed algorithm comparing with the previous ones. Through simulations, we demonstrate that our method achieves good performance in terms of the retrieval accuracy as well as the computational complexity.

• Spatial Information Research
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• v.19 no.1
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• pp.73-82
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• 2011

Recently, demand for the use of cadastral map is increasing in both public and private area. To use cadastral map in web or mobile environment, construction of the multi-representation database(MRDB) that is the compressed into multiple scale from the original map data is recommended. In this study, the map generalization methodology for the cadastral map by applying overlay with topographic map and polygon generalization technique is suggested. This process is composed of three steps, re-constructing the network data of topographic map, polygon merging of parcel lines according to network degree, and applying line simplification techniques. Proposed methodologies are applied to the cadastral map in Suwon area. The result map was generalized into 1:5,000, 1:20,000, 1:100,000 scale, and data compression ratio was shown in 15% 8% 1% level respectively.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.577-590
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• 2003

The shape-based retrieval is defined as the operation that searches for the (sub) sequences whose shapes are similar to that of a query sequence regardless of their actual element values. In this paper, we propose a similarity model suitable for shape-based retrieval and present an indexing method for supporting the similarity model. The proposed similarity model enables to retrieve similar shapes accurately by providing the combination of various shape-preserving transformations such as normalization, moving average, and time warping. Our indexing method stores every distinct subsequence concisely into the disk-based suffix tree for efficient and adaptive query processing. We allow the user to dynamically choose a similarity model suitable for a given application. More specifically, we allow the user to determine the parameter p of the distance function $L_p$ when submitting a query. The result of extensive experiments revealed that our approach not only successfully finds the subsequences whose shapes are similar to a query shape but also significantly outperforms the sequence search.