• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.

• 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 Contemporary Eastern Asia
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• v.12 no.1
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• pp.21-34
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• 2013

How do mixed legislative systems shape voter behavior and public perceptions? Through an analysis of the electoral systems in Japan, South Korea, and Taiwan, this paper evaluates the extent to which the public in these three countries understand their mixed systems and whether claims of voter ignorance translate into irrational voting behavior based on the institutional effects of mixed systems. Through a multi-method approach including data from outside of East Asia, this analysis seeks to determine whether these three cases exhibit patterns consistent with other mixed systems. Empirical analysis affirms levels of strategic voting consistent with comprehension of electoral rules. Furthermore, this analysis suggests a disconnect between practical knowledge and electoral expectations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.271-272
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• 2015

센서 네트워크에서 센서는 제한적인 자원 때문에 다양한 공격으로부터 취약하다. 이러한 공격 중 하나인 허위 보고서 삽입 공격은 불필요한 에너지 소모와 허위 알람을 유발한다. 이 공격의 피해를 줄이기 위한 확률적 투표 여과 기법은 검증 노드를 통해 보고서의 맥들을 검증한다. 그러나 허위 보고서가 검증 노드까지 도달하는 데 불필요한 에너지가 소비된다. 본 논문에서, 우리의 제안 기법은 소스의 다음 노드에 키를 배포하여 허위 보고서 삽입 공격을 효율적으로 감지한다. 따라서 제안 기법은 기존 기법보다 에너지 효율성 향상을 기대할 수 있다.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.528-537
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• 2015

Text in images is one of the most important cues for understanding a scene. In this paper, we propose a novel approach based on interest points to localize text in natural scene images. The main ideas of this approach are as follows: first we used interest point detection techniques, which extract the corner points of characters and center points of edge connected components, to select candidate regions. Second, these candidate regions were verified by using tensor voting, which is capable of extracting perceptual structures from noisy data. Finally, area, orientation, and aspect ratio were used to filter out non-text regions. The proposed method was tested on the ICDAR 2003 dataset and images of wine labels. The experiment results show the validity of this approach.

• 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.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.404-404
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• 2012

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.54-60
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• 2010

The local appearance-based method is one of the face recognition methods that divides face image into small areas and extracts features from each area of face image using statistical analysis. It collects classification results of each area and decides identity of a face image using a voting scheme by integrating classification results of each area of a face image. The conventional local appearance-based method divides face images into small pieces and uses all the pieces in recognition process. In this paper, we propose a local appearance-based method that makes use of only the relatively important facial components. The proposed method detects the facial components such as eyes, nose and mouth that differs much from person to person. In doing so, the proposed method detects exact locations of facial components using support vector machines (SVM). Based on the detected facial components, a number of small images that contain the facial parts are constructed. Then it extracts features from each facial component image using principal components analysis (PCA). We compared the performance of the proposed method to those of the conventional methods. The results show that the proposed method outperforms the conventional local appearance-based method while preserving the advantages of the conventional local appearance-based method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2086-2094
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• 2008

Finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this paper, we propose a method using a plane detection cell (PDC) and iterative randomized Hough transform (IRHT) for finding the planar region from a 3D range image. First, the local planar region is detected by a PDC from the target area of the range image. Each plane is then segmented by analyzing the accumulated peaks from voting the local direction and position information of the local PDC in Hough space to reduce effect of noises and outliers and improve the efficiency of the HT. When segmenting each plane region, the IRHT repeatedly decreases the size of the planar region used for voting in the Hough parameter space in order to reduce the effect of noise and solve the local maxima problem in the parameter space. In general, range images have many planes of different normal directions. Hence, we first detected the largest plane region and then the remained region is again processed. Through this procedure, we can segment all planar regions of interest in the range image.