• International Journal of Advanced Culture Technology
• /
• v.10 no.1
• /
• pp.42-46
• /
• 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.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.11a
• /
• pp.307-309
• /
• 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.

• Proceedings of the Optical Society of Korea Conference
• /
• 1990.02a
• /
• pp.102-111
• /
• 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.

• Journal of the Korea Computer Industry Society
• /
• v.5 no.9
• /
• pp.987-992
• /
• 2004

There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the improved diamond search pattern using an motion vector prediction candidate search point by the predicted motion information from the same block of the previous frame. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improves as high as high as 14~24% in terms of average number of search point per motion vector estimation and improved about 0.02~0.37dB on an average except the full search(FS) algorithm.

• Proceedings of the KIPE Conference
• /
• 2004.07b
• /
• pp.620-624
• /
• 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.

• Proceedings of the KIPE Conference
• /
• 2004.07b
• /
• pp.625-629
• /
• 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
• /
• v.3 no.1
• /
• pp.100-104
• /
• 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
• /
• v.17 no.1
• /
• pp.124-135
• /
• 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)
• /
• v.14 no.4
• /
• pp.1562-1578
• /
• 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.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.16 no.2
• /
• pp.196-205
• /
• 1991

This paper is a study on the isolated word recognition of speaker independent which proposes to newly improved MSVQ(multisection vector quantization) recognition system which improve the classical MSVQ recognition system. It is a difference that test pattern has on more section than reference pattern in recognition system 146 DDD area names are selected as recognition vocabulary. 12th LPC cepstral coefficients is used as feature parameter. and when codebook is generated, MINSUM and MINMAX are used in finding the centroid. According to the experiment result. it is proved that this method is better than VQ(vector quantization) recognition methods, DTW(dynamic time warping) pattern matching methods and classical MSVQ methods for recognition rate and recognition time.