• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.118-121
• /
• 1998

In this paper, we propose another paradigm(QNBP) to be capable of overcoming Limitations of the traditional backpropagation(SDBP). QNBPis based on the method of Quasi -Newton(variable metric) with the nomalized direction vectors and computes step size through the linear search. Simulation results showed that QNBP was definitely superior to both the stochasitc SDBP and the deterministic SDBP in terms of accuracy and rate of convergence and might sumount the problem of local minima. and there was no different between DFP+SR1 and BFGS+SR1 combined algrothms in QNBP.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.19 no.2
• /
• pp.433-442
• /
• 1995

For the line search of a multi-variable optimization, an efficient algorithm is presented. The algorithm sequentially employs several polynomial approximations such as 2-point quadratic interpolation, 3-point cubic interpolation/extrapolation and 4-point cubic interpolation/extrapolation. The order of polynomial function is automatically increased for improving the accuracy of approximation. The method of approximation (interpolation or extrapolation) is automatically switched by checking the slope information of the sample points. Also, for selecting the initial step length along the descent vector, a new approach is presented. The performance of the proposed method is examined by solving typical test problems such as mathematical problems, mechanical design problems and dynamic response problems.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.9
• /
• pp.1420-1427
• /
• 1989

A new block matching algorithm that improved the existing block matching algorithm in terms of image quality is proposed in this paper. The subblock of image including the vertical edge of object is subdivided into new two subblocks, and the moving vector found. The result of computer simulation shows on real image that the image quality by the algorithm becomes higher than that of the three step search algorithm by 1.1dB.

• Journal of Electrical Engineering and Technology
• /
• v.4 no.4
• /
• pp.515-520
• /
• 2009

According to the operating characteristics of pump applications, they should exhibit high efficiency and energy saving capabilities throughout the whole operating process. A novel efficiency optimization control strategy is presented here to meet the high efficiency demand of a variable speed Permanent Magnet Synchronous Motor (PMSM). The core of this strategy is the excellent integration of mended maximum torque to the current control algorithm, based on the losses model during the dynamic and the grade search method with changed step by fuzzy logic during the steady. The performance experiments for the control system of a variable speed high efficiency PMSM have been completed. The test results verified that the system can reliably operate with a different control strategy during dynamic and steady operation, and the system exhibits better performance when using the efficiency-optimization control.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.8
• /
• pp.2271-2280
• /
• 1999

In this paper, a variable motion estimation scheme based on HBMA(Hierarchical Block Matching Algorithm) to improve the performance and to reduce heavy computational and transmission load, is presented. The proposed algorithm is composed of four steps. First, block activity for each block is defined using the edge information of differential image between two sequential images, and then average block activity of the present image is found by taking the mean of block activity. Secondly, camera pan compensation is carried out, according to the average activity of the image, in the hierarchical pyramid structure constructed by wavelet transform. Next, the LUT classifying each block into one among Moving, No Moving, Semi-Moving Block according to the block activity compensated camera pan is obtained. Finally, as varying the block size and adaptively selecting the initial search layer and the search range referring to LUT, the proposed variable HBMA can effectively carries out fast motion estimation in the hierarchical pyramid structure. The cost function needed above-mentioned each step is only the block activity defined by the edge information of the differential image in the sequential images.

• Nonlinear Functional Analysis and Applications
• /
• v.29 no.2
• /
• pp.307-332
• /
• 2024

Two inertial extragradient-type algorithms are introduced for solving convex pseudomonotone variational inequalities with fixed point problems, where the associated mapping for the fixed point is a 𝜌-demicontractive mapping. The algorithm employs variable step sizes that are updated at each iteration, based on certain previous iterates. One notable advantage of these algorithms is their ability to operate without prior knowledge of Lipschitz-type constants and without necessitating any line search procedures. The iterative sequence constructed demonstrates strong convergence to the common solution of the variational inequality and fixed point problem under standard assumptions. In-depth numerical applications are conducted to illustrate theoretical findings and to compare the proposed algorithms with existing approaches.

• Journal of Korean Society for Quality Management
• /
• v.50 no.1
• /
• pp.77-89
• /
• 2022

Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.1
• /
• pp.22-28
• /
• 2017

The Convolutional Neural Network(CNN) can be divided into two stages: feature extraction and classification. The hyperparameters such as kernel size, number of channels, and stride in the feature extraction step affect the overall performance of CNN as well as determining the structure of CNN. In this paper, we propose a method to optimize the hyperparameter in CNN feature extraction stage using Parameter-Setting-Free Harmony Search (PSF-HS) algorithm. After setting the overall structure of CNN, hyperparameter was set as a variable and the hyperparameter was optimized by applying PSF-HS algorithm. The simulation was conducted using MATLAB, and CNN learned and tested using mnist data. We update the parameters for a total of 500 times, and it is confirmed that the structure with the highest accuracy among the CNN structures obtained by the proposed method classifies the mnist data with an accuracy of 99.28%.

• Ceramist
• /
• v.21 no.4
• /
• pp.427-432
• /
• 2018

In this study, the aspherical lens for optical communications produced not with an one-step pneumatic type of external pressurization system (existed GMP process) but a constant weight of self-loaded mold put up to upper core. So the lens is molding with self-loaded weight molding and it calls Weight Molding process. In self-loaded molding process, we measured changes of center thickness molding lenses with each variable molding temperatures and time to find the effect of center of lens thickness to search key factors. As experimental results, the center thickness reach to targeted lenses step time value was changed drastically and it depends by molding temperature. If the molding temperature gets higher, the targeted lens that is reaching to the center thickness step time value was decreased. To find the effect of life improvement on mold core by imposing the self-loaded molding process we molded with GMP(Glass molding press) method and self-loaded molding method for 9,000 times and measured the lenses shape accuracy and surface roughness to evaluate the core life. As a result the self-loaded molding method core has 2,000 times longer that GMP (Glass molding press) method. If we adopt self-loaded molding method of the optical aspherical lens molding in the future, we expect that it would reduce the expense of changing the molds by molding core life improvements.

• The Bulletin of The Korean Astronomical Society
• /
• v.38 no.2
• /
• pp.80-80
• /
• 2013

대규모 광도곡선 자료에서 다양한 주기변광성들의 정확한 주기를 효율적으로 검출하는 실험을 시도하였다. 실험을 위해 OGLE-III 맥동 변광성(RR Lyrae, Delta Scuti, Cepheid) 목록 중, I 필터로 관측된 총 31,324개의 광도 곡선을 사용하였다. 이 실험에 사용한 주기분석 알고리즘 MS_Period(Multi-Step period searching method)는 주기를 놓치지 않기 위해 두 가지 다른 방법(Multi Polynomial function, Phase Dispersion)으로 후보 주기를 구하고 정밀주기를 도출하기 위해 후보 주기 주변부를 Spline fitting을 통해 재탐색하는 방법이다. 기존의 MS_Period 방식은 주기 탐색 간격(dP/P)이 일정하였으나, 우리는 탐색 주기 구간을 나누고 짧은 주기에서는 작은 간격으로, 긴 주기에서는 보다 넓은 간격으로 주기를 탐색하는 과정을 추가하였다. 그 결과 98% 이상의 별에서 OGLE-III와 거의 일치하는 주기를 얻었으며, 긴 주기에서의 불필요한 정밀 탐색을 회피함으로써 분석시간도 단축되었다. 주기 결정이 어려운 경우들은 주로 1) periodogram에서 실제 주기가 아닌 1일 근처에서 noise보다 큰 peak가 보이는 경우, 2) 하나의 별에 대해 여러 주기가 비슷한 Phase diagram을 보이고, periodogram에서도 비슷한 peak를 갖는 경우, 3) OGLE-III의 주기와 전혀 다른 주기만 찾은 경우, 4) OGLE-III에서 제시하지 않은 혼합된 주기의 존재가 의심되는 경우인 것을 확인하였고, 각 사례들의 특징을 살펴보았다.