• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.161-169
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• 2011

Recent intelligent video surveillance system asks for development of more advanced technology for analysis and recognition of video data. Especially, machine learning algorithm such as Support Vector Machine (SVM) is used in order to recognize objects in video. Because SVM training demands massive amount of computation, parallel processing technique is necessary to reduce the execution time effectively. In this paper, we propose a parallel processing method of SVM training with a multi-core processor. The results of parallel SVM on a 4-core processor show that our proposed method can reduce the execution time of the sequential training by a factor of 2.5.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.709-711
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• 2004

최근 단백질간의 상호작용의 중요성의 이해와 함께 축적되어 가는 단백질 정보들 간의 상호작용을 예측하기 위하여 통계학적 모델인 Support Vector Machine(SVM)을 사용한 예측 실험이 활발하다. 하지만 이는 거대한 생물 데이터를 처리하기 위해 많은 연산시간을 필요로 한다. 즉, 방대하게 존재하는 데이터를 처리하기 위해 SVM을 통한 실험은 정확한 결과뿐만 아니라 빠른 처리속도를 요구하게 되었다. 따라서 본 논문에서는 SVM의 개선을 통해 빠른 처리속도로 데이터를 처리하는 incremental SVM과 이를 병렬화 하여 더욱 빠른 처리시간을 가지는 Parallel SVM(PSVM)을 소개하고 실험해 본다. 즉, 단백질 상호작용에 사용되어지는 데이터를 PSVM을 사용한 실험을 통하여 정확성과 처리속도를 측정, 비교함으로써 단백질 상호작용 예측에 적합한지를 검증해본다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.229-231
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• 2005

본 논문에서는. 단백질은 도메인으로 모듈화 되어 있으며, 단백질 간 상호작용이 도메인간 상호작용의 결과라는데 착안, SVM을 이용하여 도메인 기반 단백질 상호작용을 예측하는 시스템을 구성하였다. 하지만, SVM 을 통한 실험은 정확한 예측 결과뿐 아니라 빠른 처리속도를 요구하게 되었다. 따라서 SVM을 병렬화하여 빠른 처리시간을 가지는 Parallel SVM을 적용, 도메인을 기반으로 한 단백질 상호작용을 실험하였으며, 이를 통해 정확성과 처리 속도를 측정, 비교함으로써 도메인 기반 병렬 단백질 상호작용 예측을 검증해 본다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.115-116
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• 2019

SVM (Support Vector Machine)은 견고성으로 인해 다양한 분류 문제에 적용 할 수 있는 효율적인 기계 학습 기술이다. 그러나 훈련 데이터의 수가 증가함에 따라 시간 복잡도가 급격히 증가하므로 대규모 데이터 세트의 경우 SVM이 비실용적이다. 본 논문에서는 SVM을 사용하여 중복 된 학습 데이터를 효율적으로 제거하는 새로운 병렬 평면(Parallel Hyperplane) 기법을 소개한다. 제안 기법에서 PH는 재귀 적으로 형성되는 반면 PH의 외부에 있는 데이터 포인트의 클러스터는 매 반복마다 제거된다. 시뮬레이션 결과 제안 기법은 기존의 클러스터링 기반 감축 기법과 SMO 기법에 비해 학습 시간을 크게 단축시키면서 데이터 축소 없이 분류의 정확성을 높일 수 있음을 확인 하였다.

• Journal of IKEEE
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• v.20 no.3
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• pp.209-219
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• 2016

SVM is a machine learning method used for image processing. It is well known for its high classification performance. We have to perform multiple MAC operations in order to use SVM for image classification. However, if the resolution of the target image or the number of classification cases increases, the execution time of SVM also increases, which makes it difficult to be performed in real-time applications. In this paper, we propose an hardware architecture which enables real-time applications using SVM classification. We used parallel architecture to simultaneously calculate MAC operations, and also designed the system for several feature extractors for compatibility. RBF kernel was used for hardware implemenation, and the exponent calculation formular included in the kernel was modified to enable fixed point modelling. Experimental results for the system, when implemented in Xilinx ZC-706 evaluation board, show that it can process 60.46 fps for $1360{\times}800$ resolution at 100MHz clock frequency.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.239-246
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• 2015

Video based object detection is basic technology of implementing smart CCTV system. Various features and algorithms are developed to detect object, however computations of them increase with the performance. In this paper, performances of object detection algorithms with GPU and CPU are compared. Adaboost and SVM algorithm which are widely used to detect pedestrian detection are implemented with CPU and GPU, and speeds of detection processing are compared for the same video. As results of frame rate comparison of Adaboost and SVM algorithm, it is shown that the frame rate with GPU is faster than CPU.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.578-584
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• 2004

The CSVM(Current Support Vector Machine) that is a digital architecture performing all phases of recognition process including kernel computing, learning, and recall of SVM(Support Vector Machine) on a chip is proposed. Concurrent operation by parallel architecture of elements generates high speed and throughput. The classification problems of bio data having high dimension are solved fast and easily using the CSVM. Quadratic programming in original SVM learning algorithm is not suitable for hardware implementation, due to its complexity and large memory consumption. Hardware-friendly SVM learning algorithms, kernel adatron and kernel perceptron, are embedded on a chip. Experiments on fixed-point algorithm having quantization error are performed and their results are compared with floating-point algorithm. CSVM implemented on FPGA chip generates fast and accurate results on high dimensional cancer data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.141-148
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• 2011

Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.