• 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.

• Journal of Astronomy and Space Sciences
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• v.35 no.1
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• pp.39-46
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• 2018

In this study, the performance of ranging techniques for the Korea Pathfinder Lunar Orbiter (KPLO) space communication system is investigated. KPLO is the first lunar mission of Korea, and pseudo-noise (PN) ranging will be used to support the mission along with sequential ranging. We compared the performance of both ranging techniques using the criteria of accuracy, acquisition probability, and measurement time. First, we investigated the end-to-end accuracy error of a ranging technique incorporating all sources of errors such as from ground stations and the spacecraft communication system. This study demonstrates that increasing the clock frequency of the ranging system is not required when the dominant factor of accuracy error is independent of the thermal noise of the ranging technique being used in the system. Based on the understanding of ranging accuracy, the measurement time of PN and sequential ranging are further investigated and compared, while both techniques satisfied the accuracy and acquisition requirements. We demonstrated that PN ranging performed better than sequential ranging in the signal-to-noise ratio (SNR) regime where KPLO will be operating, and we found that the T2B (weighted-voting balanced Tausworthe, voting v = 2) code is the best choice among the PN codes available for the KPLO mission.

• Smart Structures and Systems
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• v.5 no.1
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• pp.43-54
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• 2009

This study introduces a novel approach for locating damage in a structure using wireless sensor system with local level computational capability to alleviate data traffic load on the centralized computation. Smart wireless sensor systems, capable of iterative damage-searching, mimic an optimization process in a decentralized way. The proposed algorithm tries to detect damage in a structure by monitoring abnormal increases in strain measurements from a group of wireless sensors. Initially, this clustering technique provides a reasonably effective sensor placement within a structure. Sensor clustering also assigns a certain number of master sensors in each cluster so that they can constantly monitor the structural health of a structure. By adopting a voting system, a group of wireless sensors iteratively forages for a damage location as they can be activated as needed. Since all of the damage searching process occurs within a small group of wireless sensors, no global control or data traffic to a central system is required. Numerical simulation demonstrates that the newly developed searching algorithm implemented on wireless sensors successfully localizes stiffness damage in a plate through the local level reconfigurable function of smart sensors.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2429-2432
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• 2000

Adaptive filter algorithm has been used for plant identifier and noise cancellation. This algorithm has been researched for performance enhancement of filtering. The design and development of a reliable system has been becoming a key issue in industry field because the reliability of a system is considered as an important factor to perform the system's function successfully. And the computing with reliability and fault tolerance is a important factor in the case of aviation and nuclear plant. This paper presents design of reliable adaptive filter with fault tolerance. Generally, redundancy is used for reliability. In this case it needs computing or circuit for voting mechanism or computing for fault detection or switching part. But this presented Filter is not in need of computing for voting mechanism, or fault detection. Therefore it has simple computing, and practicality for application. And in this paper, reliability of adaptive filter is analyzed. The effectiveness of the proposed adaptive filter is demonstrated to the case studies of plant identifier and noise cancellation by using DSP.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.131-134
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• 2000

본 논문에서는 각 지역의 수중 음향 센서로부터 중앙의 정보 융합 센터로 전송되어진 동일한 또는 상이한 표적의 Identity 정보들을 종합해 최종적으로 표적의 Identity를 결정하는 Decision Fusion 기법을 다룬다. 기존의 연구는 표적의 속성 정보로부터 정보 융합을 통해 표적의 Identity를 선택하는 기법을 주로 다루고 있다. 그러나 본 논문에서는 기존의 연구보다 한 단계 나아가 선택된 표적의 Identity들로부터 운용자가 가장 합리적인 결정을 내릴 수 있도록 하는 표적의 Identity 결정을 위한 Decision Fusion 기법을 제안한다. 이러한 수중 음향 표적 식별 시스템에서의 Identity Decision Fusion 기법으로 Voting 기법, 센서 정보의 신뢰도를 고려한 Weighted Voting 기법, 그리고 다 기준 의사 결정 기법인 Analytic Hierarchy Process (AHP) 기법을 제안하고 그 성능을 평가한다

• KDI Journal of Economic Policy
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• v.44 no.1
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• pp.1-47
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• 2022

This paper introduces a politico-economic model with a welfare state and immigration. In this model, policies on taxes and immigration are determined through a plurality voting system. While many studies of fiscal implications of immigration argue that relaxing immigration policies can substitute for tax reforms in an aging economy, I show that the democratic voting procedure can dampen the effect of relaxing immigration policies as desired policy reforms are not always implemented by the winner of an election. This political economy results in three types of social welfare losses. First, the skill composition is not balanced at a socially efficient level because workers are motivated to maximize their wages. Second, older retirees implement excessive taxes to maximize the size of the welfare state. Third, the volume of immigration is lower than the optimal level given the incentive by young workers to regain political power in the future.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.141-153
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• 2016

The purpose of this study is to predict future of wage-peak system by using text mining, futures wheel and polarity voting (+, -) techniques after reviewing a variety of documents. For this study, we collected articles, news articles, SNS(Twitter, Blog), research report documents. Above all, we extracted keywords for main subject words by utilizing text mining techniques. Next, we drew a final conclusion about future of wage-peak system by using futures wheel and polarity voting techniques. The result showed that future of wage peak system is positive. Two of five main topics were negatively predicted (favor/oppose of wage-peak system, solving task of wage-peak system), however, three of five main topics were positively predicted (background of wage-peak system, purpose/reason of wage-peak system, alternative wage-peak system). Therefore, because three of the five main topics were positively predicted, the future for wage-peak system is positive.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.137-140
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• 2001

This paper has been studied for alphabet recognition by using MPEG-7 descriptors. As an application of MPEG-7, the algorithms in this paper have employed Homogeneous Texture descriptor and Edge Histogram descriptor. For confident matching a voting system is used with a tree structure,

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.604-615
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• 2019

Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.