• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.111-120
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• 2020

As an application of big data and artificial intelligence techniques, this study proposes an atypical language-based sentimental opinion poll methodology, unlike conventional opinion poll methodology. An alternative method for the sentimental classification model based on existing statistical analysis was to collect real-time Twitter data related to parliamentary elections and perform empirical analyses on the Polarity and Intensity of public opinion using attribute-based sensitivity analysis. In order to classify the polarity of words used on individual SNS, the polarity of the new Twitter data was estimated using the learned Lasso and Ridge regression models while extracting independent variables that greatly affect the polarity variables. A social network analysis of the relationships of people with friends on SNS suggested a way to identify peer group sensitivity. Based on what voters expressed on social media, political opinion sensitivity analysis was used to predict party approval rating and measure the accuracy of the predictive model polarity analysis, confirming the applicability of the sensitivity analysis methodology in the political field.

• Earthquakes and Structures
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• v.22 no.2
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• pp.185-201
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• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.276.1-276.1
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• 2016

본 연구에서는 차세대 유연 투명 히터 (Flexible and transparent heater) 제작을 위한 ATO 나노입자-은 네트워크 하이브리드 투명 전극의 특성을 연구하였다. 최적화된 은 네트워크 (Self-assembled Ag network) 투명 전극 상에 20-30 nm의 직경을 가지는 ATO (Sb-doped $SnO_2$) 나노입자를 스프레이 방식으로 상압, 상온에서 코팅하여 인쇄형 ATO-은 네트워크 하이브리드 투명 전극을 구현하였다. 스프레이로 코팅된 투명 ATO 나노 입자는 은 네트워크 전극의 빈 공간을 매워 줌으로써 은 네트워크 간의 연결성 및 표면 조도를 낮춰주어 유연 투명 히터 작동 시 전류의 집중 현상을 막아줄 수 있다. ATO-은 네트워크 하이브리드 투명 전극의 최적화를 위해 스프레이 횟수에 따른 하이브리드 투명 전극의 전기적, 광학적, 표면 특성을 분석하였으며, 최적의 조건에서 14 Ohm/square의 면저항과 66%의 투과도를 가지는 하이브리드 투명 전극을 구현하였다. 또한 FESEM 분석을 통해 ATO-은 네트워크 하이브리드 전극의 표면 및 계면 구조를 연구하고 ATO 코팅이 은 네트워크 전극의 특성에 미치는 영향을 규명하였다. 최적화된 ATO-은 네트워크 하이브리드 투명 전극을 이용하여 유연 투명 히터를 제작하고 전압에 따른 히터의 온도의 변화를 측정하여 차세대 유연 투명 히터용 투명 전극으로 인쇄기반 ATO-은 네트워크 하이브리드 투명 전극의 가능성을 확인하였다.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.294-297
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• 2017

SNS와 스마트기기의 발전으로 온라인을 통한 뉴스 배포가 용이해지면서 악의적으로 조작된 뉴스가 급속도로 생성되어 확산되고 있다. 뉴스 조작은 다양한 형태로 이루어지는데, 이 중에서 정상적인 기사 내에 광고나 낚시성 내용을 포함시켜 독자가 의도하지 않은 정보에 노출되게 하는 형태는 독자가 해당 내용을 진짜 뉴스로 받아들이기 쉽다. 본 논문에서는 뉴스 기사 내에 포함된 문단 중에서 부적합한 문단이 포함되었는지를 판정하기 위한 방법을 제안한다. 제안하는 방식에서는 자연어 처리에 유용한 Convolutional Neural Network(CNN)모델 중 Word2Vec과 tf-idf 알고리즘, 로지스틱 회귀를 함께 이용하여 뉴스 부적합 문단을 검출한다. 본 시스템에서는 로지스틱 회귀를 이용하여 문단의 카테고리를 분류하여 본문의 카테고리 분포도를 계산하고 Word2Vec을 이용하여 문단간의 유사도를 계산한 결과에 가중치를 부여하여 부적합 문단을 검출한다.

• Journal of the Korea society of information convergence
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• v.7 no.2
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• pp.49-57
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• 2014

Recently, opinion mining is introduced to extract useful information from SNS data and to evaluate the true intention of users. Opinion mining are required several efficient techniques to collect and analyze a large amount of SNS data and extract meaningful data from them. Therefore in this paper, we propose a parallel HDFS(Hadoop Distributed File System) and emotion functions based on Mapreduce to extract some emotional information of users from various unstructured big data on social networks. The experiment results have verified that the proposed system and functions perform faster than O(n) for data gathering time and loading time, and maintain stable load balancing for memory and CPU resources.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.311-317
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• 2007

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.4
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• pp.393-399
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• 2008

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.294-297
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• 2017

SNS와 스마트기기의 발전으로 온라인을 통한 뉴스 배포가 용이해지면서 악의적으로 조작된 뉴스가 급속도로 생성되어 확산되고 있다. 뉴스 조작은 다양한 형태로 이루어지는데, 이 중에서 정상적인 기사 내에 광고나 낚시성 내용을 포함시켜 독자가 의도하지 않은 정보에 노출되게 하는 형태는 독자가 해당 내용을 진짜 뉴스로 받아들이기 쉽다. 본 논문에서는 뉴스 기사 내에 포함된 문단 중에서 부적합한 문단이 포함 되었는지를 판정하기 위한 방법을 제안한다. 제안하는 방식에서는 자연어 처리에 유용한 Convolutional Neural Network(CNN)모델 중 Word2Vec과 tf-idf 알고리즘, 로지스틱 회귀를 함께 이용하여 뉴스 부적합 문단을 검출한다. 본 시스템에서는 로지스틱 회귀를 이용하여 문단의 카테고리를 분류하여 본문의 카테고리 분포도를 계산하고 Word2Vec을 이용하여 문단간의 유사도를 계산한 결과에 가중치를 부여하여 부적합 문단을 검출한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.197-207
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• 2015

A new communication revolution such as social networking service (SNS) has been transforming the way of interaction between people in both of individual level and organizational level. Recently many people have switched to closed type SNS such as Naver Band and Kakao group due to the several reasons. The purpose of this study is to investigate the usage motivation and satisfaction of closed type SNS based on use and gratifications perspectives. Based on prior studies on use and gratifications of Internet-related media and SNS, information motivation, relationship motivation, pleasure/entertainment motivation, self-expression motivation, work motivation were identified as usage motivation of closed type SNS. According to the results, relationship motivation and work motivation were found to be significantly related to the satisfaction. But other motivation factors(information motivation, pleasure/entertainment motivation, self-expression motivation) are not significantly related to the satisfaction. Then satisfaction was found to be significantly related to the intention to use. This study contributes to give companies providing closed type SNS and using it as a marketing tool with the base of activation strategies and practical implications.

• The Research Journal of the Costume Culture
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• v.28 no.3
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• pp.313-329
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• 2020

Social Network Services (SNS) have become a vital means of shopping, significantly influencing consumers' purchases of fashion products. The aim of this study was to identify bandwagon consumption among fashion consumers and to analyze the effects of bandwagon consumption on negative emotions, purchase discontinuation, and switching intention. A survey questionnaire was developed, and data were obtained from 285 female consumers in Korea aged in their 20s and 30s who had experienced guilt, regret, or disappointment after purchasing fashion products using SNS during the previous six months. The survey results indicated four different types of band- wagon consumption: intentional, impulsive, unintentional, and planned. The presence of negative emotions such as guilt, disappointment, and regret were affected by different types of bandwagon consumption. Intentional bandwagon consumption only affected guilt, while unintentional bandwagon consumption affected both guilt and disappointment. Impulsive bandwagon consumption affected guilt and regret; however, planned bandwagon consumption only affected regret. Furthermore, negative emotions affected purchase discontinuation and switching intention. Planned bandwagon con- sumption had an effect on both purchase discontinuation and switching intention, while both impulsive and unintentional bandwagon consumption influenced switching intention only. Intentional bandwagon consumption had no effect on either purchase discontinuation or switching intention. The results of this study indicate that SNS consumers' bandwagon consumption causes different negative emotions, purchase discontinuation, and switching intention.