• Journal of Digital Convergence
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• v.20 no.1
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• pp.119-127
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• 2022

In order to analyze the effect of strengthening the disclosure of remuneration for high-paid workers among the measures to improve the governance structure of financial companies by the Financial Services Commission in 2018, this study demonstrated the compensation system, management performance, and improvement of governance for Korean financial companies from 2015 to 2020. Analysis was performed. As a result of the empirical analysis, it was found that financial companies after 2018 decreased the employee compensation disparity and the majority shareholding ratio, while the stock performance and foreign ownership ratio increased. This study has the greatest contribution in that it is the first domestic study to verify the effect of applying the so-called Say on Pay, which discloses management's remuneration and allows shareholders to check its appropriateness through voting.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.6
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• pp.403-412
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• 2016

As transaction and investment using technology are vitalized, the need for objective standards for the technology is increasing. Current technology value evaluation system is limited lacking reliability and objectivity. Besides the traditional evaluation methodology which are market approach, income approach and cost approach other diverse evaluation methodology such as real option method and royalty calculation method are being studied; however currently there are no dominant evaluation methodology in the market. Same value evaluation system cannot be applied between similar technologies because value of technology is relatively decided based on the target. Approaching through collective intelligence and crowd sourcing, in meaning of majority participant's decision can make objective and better result than handful of experts, suggest alternative to problems of such matter above. By grafting the four types of crowd sourcing model which are Wisdom, Voting, Funding and Creation, this paper will discuss the ways to enhance the objectivity of technology evaluation through direct evaluation utilizing expert group and the public's indirect evaluation.

• Korean Journal of Legislative Studies
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• v.15 no.1
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• pp.145-176
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• 2009

This paper introduces two concepts, specialization and representation, to evaluate activities of standing committees in Korean Assembly. Previous studies have criticized the committees based on frequent change of committee membership. After analyzing the empirical data of Finance and Economy Committee, it turns out that different from the previous arguments, the committee reaches the high level of activities in the perspective of specialization and representation. Changing of committee membership, after considering direction of the change and period of leaving the committee, is less happened than they are supposed. If voting by proxy or absentee voting were allowed, the frequency of the changing membership would be decreased. Study on Finance and Economy committee reveals that the extent of participation of the committee members is similar to the that of the floor meeting and that the number of participants and hours for each meeting display the characteristics of deliberative democracy of committee. Unanimous decisions outnumber majority ones. Therefore, this paper insists that standing committees in Korean Assembly works much better than what they have been criticized

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.77-84
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• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.

• Korean journal of communication and information
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• v.17
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• pp.37-60
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• 2001

This study attempted to demonstrate the interaction effects between attitudinal frames of nine daily newspapers and community structure in the 1994s Michigan gubernatorial election. It was theoretically guided by framing research and the self-presentation theory of social-cognition perspective and empirically tested with archival data. For the purpose of this study, content analysis of nine statewide daily newspapers was employed in order to provide data on news framing. Data on voting rates for John Engler, winner of the 1994 Michigan Gubernatorial election, in each county of Michigan were used for vote choice while Michigan census data were used for constructing an Index of community structural differentiation. The results indicated that majority compliance frames were slightly more related with vote choice in homogeneous com-unities rather than were majority compliance frames in heterogeneous communities while social identification frames tended to have an influence on vote choice in heterogeneous communities more than did social identification frames in homogeneous communities.

• Journal of Broadcast Engineering
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• v.14 no.3
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• pp.342-356
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• 2009

This paper presents a vote decision-based deinterlacing scheme for false directional error correction(VDD) to convert interlaced signal into non-interlaced signal using only one fields. The VDD using the vote decision goes through four steps process. The first step extracts regions having doubt of false edge using MM-ELA method. In these regions, the edge direction is decided by the majority vote using upper adjacent pixels's information through the second step. But, we still have undecided directions, which will be decided by the majority vote and the directional average decision at the third step. This step preserves the edge directions and minimizes visual degradation. Finally, the last step interpolates undecided pixels using DOI method which can consider the fine edge direction. Although the VDD with hierarchical structure has a high complexity, it can extract delicate edge compared to other pixel-by-pixel or window-by-window deinterlacing algorithms. Simulation results show that it has significantly improved both the subjective and objective qualities of the reconstructed images.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.659-669
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• 2012

This paper proposes the method to extract a binary audio fingerprint by combining several base binary fingerprints. Based on majority voting of base fingerprints, which are designed by mimicking the fingerprint used in Philips fingerprinting system, the proposed fingerprint is determined. In the matching part, the base fingerprints are extracted from the query, and distance is computed using the sum of them. In the experiments, the proposed fingerprint outperforms the base binary fingerprints. The method can be used for enhancing the existing binary fingerprint or for designing a new fingerprint.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.443-458
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• 2014

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.62-70
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• 2000

Intelligent vehicles are equipped with radar sensors for collision avoidance. We present a method of suppressing mutual interference among pulse-type radars, where all the radars are standardized. We developed a method of separating the true self-reflection from the false one by controlling the pulse emission of a radar in anorhogonal ON, OFF pattern. Interference signal identified in OFF-intervals is recorded to indicate the positions of the expected ghosts in ON-intervals. PFA and PM are derived for a radar system with I-Q demodulation scheme, where Gaussian noise alone is Rayleigh-distributed and Gaussian noise plus reflected radar pulse are Rician-distributed. The value of the threshold adaptively updated in order to prevent the deterioration of PM. In the experimental result, PFA decreases by an order of 10,000, when compared with the conventional M of N majority voting method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.