• Proceedings of the Korean Information Science Society Conference
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• 2008.06b
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• pp.337-341
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• 2008

갈수록 복잡해지는 내장형 시스템을 개발함에 있어서 소프트웨어 개발의 중요성은 날로 커지고 있다. 기존 연구에서 소프트웨어 개발 효율을 높이기 위해 소프트웨어의 재사용 가능성을 높이고 병렬성 명세를 용이하게 하고자 중간단계코드(CIC)를 정의하였다. 이 중간단계 코드는 각 태스크의 순수 알고리즘을 기술하는 C형태의 태스크 코드와 그 외의 정보를 포함하는 XML형태의 아키텍쳐 정보 파일로 구성된다. 이 CIC는 사용자가 직접 기술할 수 있고 각종 모델로부터 자동 생성할 수도 있다. 이 논문에서는 후자에 초점을 두고 데이터 플로우 모델에 사용된 블록들을 클러스터링하여 태스크 코드를 생성하는 기법을 제안하였다. 이것을 위해 블록 클러스터링 알고리즘은 주어진 클러스터의 크기로 블록이 묶일 때까지 블록의 수행시간 정보를 고려하여 함수 병렬성을 최대한 보존하며 블록들을 묶어나간다. H.263 코덱 예제를 이용한 실험을 통해 제안하는 방법이 다양한 클러스터의 크기 조건에 대해서 다양한 클러스터링 결과를 제공함을 보였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.656-658
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• 2006

This study deals with the part-machine grouping (PMG) that considers realistic manufacturing factors, such as the machine duplication, operation sequences with multiple visits to the same machine, and production volumes of parts. Basically, this study is an extension of Won(2006) that has adopted fuzzy ART neural network to group parts and machines. The proposed fuzzy ART neural network algorithm is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Computational experiments applied to large-size PMG data sets with a psuedo-replicated clustering procedure show effectiveness of the proposed approach.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.257-260
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• 2011

본 논문에서는 비디오 데이터로부터 물체의 초기 움직임 영역을 자동으로 검출하는 방법을 소개한다. 제안하는 시스템은 먼저 입력 영상을 받아들인 후 인접된 영상으로부터 일정 크기의 정방향의 블록 단위로 움직임을 나타내는 모션 벡터를 추출한다. 그리고 추출된 모션벡터를 아웃라이어를 제거하는 강건 예측 알고리즘에 적용하여 배경에 해당하는 모션벡터와 잡음 및 움직이는 물체에 해당하는 모션벡터를 구분한다. 그런 다음, 군집화 알고리즘을 적용하여 이동하는 물체를 나타내는 모션벡터를 군집화하고, 군집화된 모션벡터에 해당하는 영역의 크기가 일정 수치 값 이상일 때 움직이는 물체가 감지되었다고 판단한다. 본 논문의 실험에서는 제안된 물체의 움직임 감지 방법이 기존의 방법에 비해 성능이 보다 우수함을 보인다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.579-588
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• 2013

This paper proposes an effective watermarking approach for copyright protection of fire video images. The proposed watermarking approach efficiently utilizes the inherent characteristics of fire data with respect to color and texture by using a gray level co-occurrence matrix (GLCM) and fuzzy c-means (FCM) clustering. GLCM is used to generate a texture feature dataset by computing energy and homogeneity properties for each candidate fire image block. FCM is used to segment color of the fire image and to select fire texture blocks for embedding watermarks. Each selected block is then decomposed into a one-level wavelet structure with four subbands [LL, LH, HL, HH] using a discrete wavelet transform (DWT), and LH subband coefficients with a gain factor are selected for embedding watermark, where the visibility of the image does not affect. Experimental results show that the proposed watermarking approach achieves about 48 dB of high peak-signal-to-noise ratio (PSNR) and 1.6 to 2.0 of low M-singular value decomposition (M-SVD) values. In addition, the proposed approach outperforms conventional image watermarking approach in terms of normalized correlation (NC) values against several image processing attacks including noise addition, filtering, cropping, and JPEG compression.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1121-1130
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• 2004

This paper proposes a method for line and word segmentation for machine-printed text blocks. To separate a text region into the unit of lines, it analyses the horizontal projection profile and performs a recursive projection profile cut method. In the word segmentation, between-word gaps are identified by a hierarchical clustering method after finding gaps in the text line by using a connected component analysis. In addition, a special symbol detection technique is applied to find two types of special symbols tying between words using their morphologic features. An experiment with 84 text regions from English and Korean documents shows that the proposed method achieves 99.92% accuracy of word segmentation, while a commercial OCR software named Armi 6.0 Pro$^{TM}$ has 97.58% accuracy.y.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.657-668
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• 2008

Traditional off-the-shelf file server has several potential drawbacks to store data blocks. A first drawback is a lack of practical de-duplication consideration for storing data blocks, which leads to worse storage capacity waste. Second drawback is the requirement for high performance computer system for processing large data blocks. To address these problems, this paper proposes a clustering backup system that exploits file fingerprinting mechanism for block-level de-duplication. Our approach differs from the traditional file server systems in two ways. First, we avoid the data redundancy by multi-level file fingerprints technology which enables us to use storage capacity efficiently. Second, we applied a cluster technology to I/O subsystem, which effectively reduces data I/O time and network bandwidth usage. Experimental results show that the requirement for storage capacity and the I/O performance is noticeably improved.

• Journal of KIISE:Databases
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• v.29 no.4
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• pp.262-273
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• 2002

MOLAP is a technology that accelerates multidimensional data analysis by storing data in a multidimensional array and accessing them using their position information. Depending on a mapping scheme of a multidimensional array onto disk, the sliced of MOLAP operations such as slice and dice varies significantly. [1] proposed a MOLAP cube storage scheme that divides a cube into small chunks with equal side length, compresses sparse chunks, and stores the chunks in row-major order of their chunk indexes. This type of cube storage scheme gives a fair chance to all dimensions of the input data. Here, we developed a variant of their cube storage scheme by placing chunks in a different order. Our scheme accelerates slice and dice operations by aligning chunks to physical disk block boundaries and clustering neighboring chunks. Z-indexing is used for chunk clustering. The efficiency of the proposed scheme is evaluated through experiments. We showed that the proposed scheme is efficient for 3~5 dimensional cubes that are frequently used to analyze business data.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.145-167
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• 2013

This paper proposes a method to compare planned processes with actual processes of bock assembly operations in shipbuilding industry. Process models can be discovered using the process mining techniques both for planned and actual log data. The comparison between planned and actual process is focused in this paper. The analysis procedure consists of five steps : 1) data pre-processing, 2) definition of analysis level, 3) clustering of assembly bocks, 4) discovery of process model per cluster, and 5) comparison between planned and actual processes per cluster. In step 5, it is proposed to compare those processes by the several perspectives such as process model, task, process instance and fitness. For each perspective, we also defined comparison factors. Especially, in the fitness perspective, cross fitness is proposed and analyzed by the quantity of fitness between the discovered process model by own data and the other data(for example, the fitness of planned model to actual data, and the fitness of actual model to planned data). The effectiveness of the proposed methods was verified in a case study using planned data of block assembly planning system (BAPS) and actual data generated from block assembly monitoring system (BAMS) of a top ranked shipbuilding company in Korea.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.81-90
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• 2020

The purpose of this study is to study how to apply block-chain technology to prevent data forgery and alteration in the defense sector of AI(Artificial intelligence). AI is a technology for predicting big data by clustering or classifying it by applying various machine learning methodologies, and military powers including the U.S. have reached the completion stage of technology. If data-based AI's data forgery and modulation occurs, the processing process of the data, even if it is perfect, could be the biggest enemy risk factor, and the falsification and modification of the data can be too easy in the form of hacking. Unexpected attacks could occur if data used by weaponized AI is hacked and manipulated by North Korea. Therefore, a technology that prevents data from being falsified and altered is essential for the use of AI. It is expected that data forgery prevention will solve the problem by applying block-chain, a technology that does not damage data, unless more than half of the connected computers agree, even if a single computer is hacked by a distributed storage of encrypted data as a function of seawater.