• Analyses & Alternatives
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• v.6 no.1
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• pp.223-258
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• 2022

The 2018 local elections completely upended the composition of Busan's city council, with the council membership changing from being solidly and consistently conservative to being over 80% liberal. What explains this anomalous outcome? While existing literature suggests the outcome of the 2018 city council elections was the consequence of a combination of structural and strategic factors, such as the decline of regional voting, we argue that the individual-level evaluation of President Moon Jae-in is one of the primary factors driving this result. Although coattails effects are commonly considered in concurrent national legislative elections, the Presidentialized and nationalized politics of Korea makes it possible for Presidential elections to affect local elections as well. We assess our explanation through an analysis of repeated cross-sectional survey data collected just before the 2018 local elections. The results of the analysis show that support for the Democratic Party is very strongly predicted by individual-level evaluation of President Moon. When considered in the context of the timing of presidential and local elections, the results suggest that Presidential coattail effects are capable of destabilizing established political patterns, such as regional voting, if perhaps only in a sporadic and idiosyncratic fashion, depending on whether or not local elections are held early on in a President's term.

• Korean Journal of Legislative Studies
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• v.25 no.2
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• pp.69-101
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• 2019

The purpose of this study is to verify that voting behavior of congressmen can vary depending on the different candidate selection methods of political parties. Specifically, this paper examines whether congressmen elected through a primary tend to deviate from the party line vote in the floor voting, compared to those who did not. As a result, it was founded that congressmen who went through competition of the primary are more likely to defect from the party line vote than who did not. This empirical evidence suggests that if the introduction of the open primary system is further expanded in the future, it may lead to weakening of party cohesion and reinforcing lawmakers' autonomy.

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.29-38
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• 2018

In this paper, we propose an automatic segmentation method of meniscus in knee MR images by automatic meniscus localization, multi-atlas-based locally-weighted voting, and patch-based edge feature classification. First, after segmenting the bone and knee articular cartilage, the volume of interest of the meniscus is automatically localized. Second, the meniscus is segmented by multi-atlas-based locally-weighted voting taking into account the weights of shape and intensity distribution in the volume of interest of the meniscus. Finally, to remove leakage to the collateral ligaments with similar intensity, meniscus is refined using patch-based edge feature classification considering shape and distance weights. Dice similarity coefficient between proposed method and manual segmentation were 80.13% of medial meniscus and 80.81 % for lateral meniscus, and showed better results of 7.25% for medial meniscus and 1.31% for lateral meniscus compared to the multi-atlas-based locally-weighted voting.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1111-1120
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• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Design Convergence Study
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• v.14 no.4
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• pp.133-150
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• 2015

In this study, we proposed Interactive Visualization methods that can be analyzed relations between nations in various viewpoints such as period, issue using total 5211 of the UN General Assembly voting data.For this research, we devised a similarity matrix between nations and developed two visualization method based similarity matrix. The first one is Network Graph Visualization that can be showed relations between nations which participated in the vote of the UN General Assembly like Social Network Graph by year. and the second one is Proximity based Circular Visualization that can be analyzed relations between nations focus on one nation or Changes in voting patterns between nations according to time. This study have a great signification. that's because we proposed Proximity based Circular Visualization methods which merged Line and Circle Graph for network analysis that never tried from other cases of studies that utilize conventional voting data and made it. We also derived co-operatives of each visualization through conducting a comparative experiment for the two visualization. As a research result, we found that Proximity based Circular Visualization can be better analysis each node and Network Graph Visualization can be better analysis patterns for the nations.

• Korean Journal of Legislative Studies
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• v.27 no.2
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• pp.71-109
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• 2021

The purpose of this study is to find out what factors influence lawmakers' voting decisions in the plenary session. In particular, the study examines causes and characteristics of waffling and strategic waffling, which express opposition or abstention in a discharge petition or a vote at the plenary session despite participating in a bill sponsorship. The study on waffling is meaningful because it has not been covered in previous literatures. To this end, the Gadeokdo New Airport Special Act, which the National Assembly passed in February 2021, was set as an analysis target. The study examined the factors that affected legislators' voting decisions and participation in bill sponsorship and who were related with waffling. Statistical results showed that the age variable influenced the motion of the bill while seniority and party affiliation had an effect on the participation of the bill. Likewise, the study found that party affiliation and district had an influence on the approval of the bill. In addition, the analysis of waffling showed that lawmakers with higher seniority tended not to participate in the vote. It could be interpreted that lawmakers with more legislative experience judged that they would benefit from strategic waffling. There is a limit to understanding lawmakers' decision-making and waffling through a limited analysis of the Gadeokdo New Airport bill. However, given that lawmakers tend to choose avoidance strategies in ambiguous situations, and given the high intelligence of lawmakers who know better than anyone about the future impact of a new bill, the decision-making shown by lawmakers in each stage of the situation is a prudent judgment gained from their experience. It indicates that it is necessary to research the legislative activities of lawmakers in various ways.

• Magazine of the Korean Society of Agricultural Engineers
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• v.66 no.2
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• pp.24-29
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• 2024

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.449-451
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• 1999

지금까지 많은 필기 숫자 인식 방법들이 제안되었지만 고도의 신뢰도가 요구되는 은행 수납 장표상의 숫자 인식에 적합한 방법은 아직 발표된 것이 미미한 실정이다. 본 연구에서는 세 개의 분류기의 결과를 융합하여 100%에 가까운 신뢰도를 낼 수 있는 필기숫자 인식 시스템을 제안하였다. Karhunen-Loeve Transform(KLT)를 통하여 특징을 추출하였으며 오류 역전파 신경망(BP), LVQ를 적용한 SOFM(SOFM-LVQ)과 Weignted Several Nearest Neighbor(WSNN)을 분류기로 사용하였다. 융합을 위해서는 다수결(Majority Voting)이 아닌 만장일치제(Unanimous Voting)을 적용하여 신뢰도를 높혔다. ETL-6 DB를 사용하여 실험하였으며 실험 결과 99.95%의 높은 신뢰도를 기록하였다.

• Journal of Computing Science and Engineering
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• v.8 no.3
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• pp.149-156
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• 2014

Skeletonization is a morphological operation that transforms an original object into a subset, which is called a 'skeleton'. Skeletonization has been intensively studied for decades and is a challenging issue especially for special target objects. This paper proposes a novel approach to the skeletonization of text images based on stroke width detection. First, the preliminary skeleton is detected by using a Canny edge detector with a Tensor Voting framework. Second, the preliminary skeleton is smoothed, and junction points are connected by interpolation compensation. Experimental results show the validity of the proposed approach.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.75-81
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• 2020

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.