• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.42-46
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• 2022

To cultivate talented people with creative and convergent thinking skills to live in the era of the 4th industrial revolution, the national curriculum of Korea is gradually emphasizing convergence education and software education. To meet the demands of the times, this paper suggests subject-convergent teaching-learning materials for educating core principles of Support Vector Machines, especially targeting elementary learners. Based on analysis of the national curriculum, achievement standards of three subjects are integrated. After printable worksheets for traditional face-to-face classes had developed, they were transformed to online interactive worksheets for non-face-to-face classes. The teaching-learning materials are expected to promote the growth of the learners' academic motivation and knowledge.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 가을 학술발표논문집 Vol.32 No.2 (1)
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• pp.307-309
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• 2005

본 논문에서는 distance-vector 기반의 라우팅 알고리즘에 경로 지속성에 대한 분석을 적용한 Durable Distance Vector Multicast(DDVM) 알고리즘을 제안한다. DDVM은 기존의 distance vector 알고리즘에 PATHS의 분석 내용을 기반으로 한 경로 지속성 정보를 포함하여 견고한 멀티캐스팅 경로를 구성한다. 또한 경로 정보에 목적지까지의 세부적인 경로의 지속성 정보 역시 포함하여 멀티캐스팅 경로 형성의 실패율을 줄이고 보다 지속성 있는 경로를 멀티캐스팅 경로에 포함시킨다. 이러한 경로들을 통해 멀티캐스팅을 수행함으로서 high mobility 환경에서 기존의 알고리즘보다 높은 전송율을 보이며, 실험 결과를 통해 이를 확인할 수 있다.

• 한국광학회:학술대회논문집
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• 한국광학회 1990년도 제5회 파동 및 레이저 학술발표회 5th Conference on Waves and lasers 논문집 - 한국광학회
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• pp.102-111
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• 1990

In this thesis, an optical bidirectional inner-product associative memory model using liquid crystal television is proposed and analyzed theoretically and realized experimentally. The LCTV is used as a SLM(spatial light modulator), which is more practical than conventional SLMs, to produce image vector in terms of computer and CCD camera. Memory and input vectors are recorded into each LCTV through the video input connectors of it by using the image board. Two multi-focus hololenses are constructed in order to perform optical inner-product process. In forward process, the analog values of inner-products are measured by photodetectors and are converted to digital values which are enable to control the weighting values of the stored vectors by changing the gray levels of the pixels of the LCTV. In backward process, changed stored vectors are used to produce output image vector which is used again for input vector after thresholding. After some iterations, one of the stored vectors is retrieved which is most similar to input vector in other words, has the nearest hamming distance. The experimental results show that the proposed inner-product associative memory model can be realized optically and coincide well with the computer simulation.

• 한국컴퓨터산업학회논문지
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• 제5권9호
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• pp.987-992
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• 2004

비디오 시퀀스의 현재 블록의 움직임 벡터와 이전 블록의 움직임 벡터는 시간적 상관성을 갖고있다. 본 논문에서는 현재 프레임 블록의 인접 블록으로부터 예측된 움직임 정보를 구하여, 이를탐색 원점으로 하여 수정된 다이아몬드 지역 레이더 패턴으로 블록 정합을 수행하는 블록 정합움직임 추정 방식을 제안한다. 실험 결과 제안된 방식은 전역탐색을 제외한 기존의 방식들에 비해 PSNR 값에 있어서 평균적으로 0.02~0.37[dB] 개선되고 움직임 벡터 예측의 속도에 있어서 약 14~24% 이상의 높은 성능 향상을 보였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2004년도 전력전자학술대회 논문집(2)
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• pp.620-624
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• 2004

Th is paper presents a systematical approach to study carrier based PWM techniques (CPWM) in diode-clamped and cascade multilevel inverters by using the proposed multi-modulating pattern method. This method is based on the vector correlation between CPWM and space vector PWM (SVPWM) and applicable to both multilevel inverter topologies. The CPWM technique can be described in a general mathematical equation, and obtain the same outputs similarly as of corresponding SVPWM. Control of the fundamental voltage, vector redundancies and phase redundancies in multilevel inverter can be formulated separately in the CPWM equation. The deduced CPWM can obtain a full vector redundancy control, and fully utilize phase redundancy in a cascade inverter. In the paper, CPWM equations and corresponding algorithm for generating multi-modulating signals will be performed, in which SVPWM attributes will be presented by corresponding controllable factors.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2004년도 전력전자학술대회 논문집(2)
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• pp.625-629
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• 2004

This paper presents a systematical approach to study carrier based PWM techniques (CPWM) in diode-clamped and cascade multilevel inverters by using a proposed named multi-modulating pattern method. This method is based on the vector correlation between CPWM and the space vector PWM (SVPWM) and applicable to both multilevel inverter topologies. A CPWM technique can be described in a general mathematical equation, and obtain the same outputs similarly as of the corresponding SVPWM. Control of the fundamental voltage, vector redundancies and phase redundancies in multilevel inverter can be formulated separately in the CPWM equation. The deduced CPWM can obtain the full vector redundancy control, and fully utilize phase redundancy in a cascade inverter In this continued part, it will be deduced correlation between CPWM equations in multi-carrier system and single carrier system, present the mathematical model of voltage source inverter related to the common mode voltage and propose a general algorithm for multi-modulating modulator. The obtained theory will be demonstrated by simulation results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• Journal of Information Processing Systems
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• 제17권1호
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• pp.124-135
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• 2021

Object detection is a fundamental yet challenging task in computer vision that plays an important role in object recognition, tracking, scene analysis and understanding. This paper aims to propose a multiproperty fusion algorithm for moving object detection. First, we build a scale-invariant feature transform (SIFT) vector field and analyze vectors in the SIFT vector field to divide vectors in the SIFT vector field into different classes. Second, the distance of each class is calculated by dispersion analysis. Next, the target and contour can be extracted, and then we segment the different images, reversal process and carry on morphological processing, the moving objects can be detected. The experimental results have good stability, accuracy and efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1562-1578
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• 2020

Vector data compression algorithm can meet requirements of different levels and scales by reducing the data amount of vector graphics, so as to reduce the transmission, processing time and storage overhead of data. In view of the fact that large threshold leading to comparatively large error in Douglas-Peucker vector data compression algorithm, which has difficulty in maintaining the uncertainty of shape features and threshold selection, a segmented Douglas-Peucker algorithm based on node importance is proposed. Firstly, the algorithm uses the vertical chord ratio as the main feature to detect and extract the critical points with large contribution to the shape of the curve, so as to ensure its basic shape. Then, combined with the radial distance constraint, it selects the maximum point as the critical point, and introduces the threshold related to the scale to merge and adjust the critical points, so as to realize local feature extraction between two critical points to meet the requirements in accuracy. Finally, through a large number of different vector data sets, the improved algorithm is analyzed and evaluated from qualitative and quantitative aspects. Experimental results indicate that the improved vector data compression algorithm is better than Douglas-Peucker algorithm in shape retention, compression error, results simplification and time efficiency.

• 한국통신학회논문지
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• 제16권2호
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• pp.196-205
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• 1991

본 논문은 화자 독립의 단독이 언직에 관한 연구로 기존의 MSVQ(multisection vector quantization) 일질시스템을 개선한 새로운 MSVQ 시스템을 제안한다. 제안된 내용은 기존의 시스템과는 달리 인식시 시험패턴의 구간 수를 표준패턴의 구간 수보다 한 구간 더 늘리는 것이다. 이 방법에 의한 실험시 인식 대상으로는 146개의 DDD 지역망을 선택했으며, 특징 파라베타로는 12사 LPC 스트럼(cepstrum) 계수를 사용했고 코드북 지정석 중심점 구하는 방법으로 MINSUM과 MINIMAX기법을 사용하였다. 실험 결과에 의하면 DTW(dynamic time warping) 패턴 매칭 방법, VQ(vector quantization)에 의한 방법은 물론 기존의 MSVQ 방법보다 계산량이 감소함과 동시에 더 높은 인식율을 얻을 수 있었다. 수 있었다.