• Journal of Power Electronics
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• 제15권2호
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• pp.411-418
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• 2015

In this paper, a new method for the online estimation of equivalent series resistances (ESR) of the DC-link capacitors in induction machine (IM) drive systems with a front-end diode rectifier is proposed, where the ESR estimation is conducted during the regenerative operating mode of the induction machine. In the first place, a regulated AC current component is injected into the q-axis current component of the induction machine, which induces the current and voltage ripple components in the DC-link. By processing these AC signals through digital filters, the ESR can be estimated by a recursive least squares (RLS) algorithm. To acquire the AC voltage across the ESR, the DC-link voltage needs to be measured at a double sampling frequency. In addition, the ESR current is simply reconstructed from the stator currents and switching states of the inverter. Experimental results have shown that the estimation error of the ESR is about 1.2%, which is quite acceptable for condition monitoring of the capacitor.

• 상하수도학회지
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• 제20권6호
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• pp.823-828
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• 2006

For this study, an online particle counter was installed before and after the activated carbon filtration process of D water treatment plant where has advanced water treatment processes, produces average 900,000ton/day of drinking water and supply the produced drinking water to Busan citizens. We collected and analyzed particle count data for about 1 year. We inspected particle breakthrough in three out of sixteen filter processes operated at same conditions, i.e. 5th filter, 6th filter and 7th filter. According to the monitoring results, 6th and 7th filters showed similar results while 5th filter showed different results. When compared seasonal effect, the particle count for dry season was below 10 particles/ml while the particle count for August when monthly average rainfall is over 200mm was much higher than for dry season. In January and August, there was a difference in breakthrough particle size. In January, small particles in 2~3um were mainly detected while in August 10um particles were mainly detected and the size distribution was 40% of total count.

• The Journal of Asian Finance, Economics and Business
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• 제6권1호
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• pp.231-240
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• 2019

The paper aims to investigate the relationship between firm size and organizational actions on adopting social media for corporate reputation management. The sample group of 198 companies is selected with a simple random sample method from the New York Stock Exchange (NYSE) listings: Sixty nine companies were from the Fortune 500 listings, seventy one companies from the NYSE midsize capitalization and fifty eight companies from the NYSE small capitalization listings. This study employs cross tabulations and Chi-square analysis, and the Kruskal-Wallis that enables the comparison of three samples that are independent. The results of the study show that (1) large firms have more social media ownership than small firms, (2) large firms respond to social media posts at a greater frequency and quickly than small firms, and (3) firm size is less likely associated with response styles to social media for online reputation management. The results show that reply time and response styles of organizations to social media customers in the 2015 survey has no significant change compared to that of 2011. There appears to be a pervasive lack strategic framework as most firms in the study were found not to be adequately monitoring or leveraging social media communication for their reputation management.

• The Journal of Asian Finance, Economics and Business
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• 제6권1호
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• pp.249-259
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• 2019

Studies looking at many aspects of SNS addiction have dramatically increased in recent years. Most of the SNS addiction research has focused on individual behaviors. There is little academic research about SNS addiction in the workplace. This study, therefore, plans to examine the organizational behaviors related to SNS addiction in the workplace. We investigate whether negative behaviors in the workplace induce SNS addiction, and how SNS addiction influences the organizational or social attitudes of employees. We also explore the possible mediating effect of SNS addiction. We use an online survey and collected 285 responses from office workers in South Korea. The results tested by a structural equation modeling indicate, first, that both abusive supervision and workplace bullying have aroused SNS addiction among employees; second, employees' SNS addiction increases both from work-to-family-conflicts and family-to-work-conflicts; and third, SNS addiction fully mediates the relationship between abusive supervision and workplace bullying, as well as the relationship among abusive supervision, workplace bullying, and work-family conflicts. The study finds that abusive supervision and workplace bullying are important antecedents of SNS addiction, and that SNS addiction affects conflicts in both work-to-family and family-to-work situations. Therefore, companies should be cognizant of potential mediating influences in monitoring employees' SNS usage in order to improve their work environments.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 추계학술대회
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• pp.447-450
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• 2010

오늘날 상대적으로 미비한 소규모저수지 및 간척담수호 농업용수의 수질정보를 온라인으로 수집 및 분석하고, 분석된 정보를 실시간으로 전달 및 데이터베이스화함으로써 농업용저수지와 담수호의 수질관리를 체계적으로 할 수 있다. 본 논문에서는 원격에서 자동으로 수질을 측정하고 사용자에게 측정된 정보를 제공하기 위해 각 센서로부터 수집된 정보를 통합 처리 이후 무선 네트워크를 통해서 실시간으로 통합관리 함으로써 관측지점에 대한 수질정보를 실시간으로 모니터링 할 수 있으며 이동 통신망의 이용도 가능한 시스템을 제안하고자 한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.118-118
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• 2019

현재 홍수예보는 정형데이터인 유량 및 수위 등을 활용하여 이뤄지고 있다. 하지만 실제 사람들이 체감하는 홍수에 대한 위험도는 홍수예보 발령과는 달라 홍수예보가 이뤄지지 않은 지역에서 인명사고가 발생하기도 한다. 이는 수위 측정이 이뤄지지 않는 소규모 하천이나 사람들의 유동성이 큰 도심지역에서 빈번하게 발생한다. 이를 보완하기 위해서는 사람들의 체감 정도 및 인구의 유동성을 고려한 비정형데이터를 활용해야 한다. 특히 소셜 네트워크 서비스(Social Network Commuinty, SNS)를 사용하는 사람들이 많아지면서 기존에 사용되어 왔던 정형데이터 센서 이외의 데이터를 제공한다. 또한 개개인이 작성하는 글은 실시간으로 활용이 가능하여 인구의 유동성 및 시 공간적 데이터를 얻기에 유용하여 활용성이 매우 높은 비정형데이터이다. 따라서 본 연구에서는 SNS 데이터를 추출하고 이를 분석하여 2018년에 발생했던 강우사상과의 패턴을 비교하여 홍수예보에서의 활용성을 분석하였다. 홍수와 관련한 키워드를 중심으로 시 공간적 정보 및 추출이 가능한 웹 크롤러(Web Crawler) 프로그램을 작성하였으며 이를 토대로 데이터를 수집하였다. 수집한 데이터와 실제 홍수사상을 비교 분석을 한 결과 강우량 및 수위와 해당 지역에 대한 데이터의 양이 유사한 패턴을 보인 것으로 확인되었다. 실시간으로 데이터를 수집하고 이를 분석하여 리드타임을 충분히 확보한다면 홍수예측에 활용 가능할 것이라 생각된다. 본 연구는 한국건설기술연구원 19주요-대4-시드사업인 '커뮤니티 빅데이터 패턴 해석을 통한 수난(水難) 발생 및 규모 예측 기술 개발(20190126-001) '로 수행되었습니다.

• Structural Engineering and Mechanics
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• 제19권3호
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• pp.261-279
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• 2005

The hot spot stress approach is usually adopted in the fatigue design and analysis of tubular welded joints. To apply the hot spot stress approach for fatigue evaluation of long span suspension bridges, the FEM is used to determine the hot spot stress of critical fatigue location. Using the local finite element models of the Tsing Ma Bridge, typical joints are developed and the stress concentration factors are determined. As a case for study, the calculated stress concentration factor is combined with the nominal representative stress block cycle to obtain the representative hot spot stress range cycle block under traffic loading from online health monitoring system. A comparison is made between the nominal stress approach and the hot spot stress approach for fatigue life evaluation of the Tsing Ma Bridge. The comparison result shows that the nominal stress approach cannot consider the most critical stress of the fatigue damage location and the hot spot stress approach is more appropriate for fatigue evaluation.

• 한국태양에너지학회 논문집
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• 제35권3호
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• pp.1-8
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• 2015

The MERRA reanalysis data provided online by NASA was applied to predict the monthly energy productions of Donghai Bridge Offshore wind farms in China. WindPRO and WindSim that are commercial software for wind farm design and energy prediction were used. For topography and roughness map, the contour line data from SRTM combined with roughness information were made and used. Predictions were made for 11 months from July, 2010 to May, 2011, and the results were compared with the actual electricity energy production presented in the CDM(Clean Development Mechanism)monitoring report of the wind farm. The results from the prediction programs were close to the actual electricity energy productions and the errors were within 4%.

• Journal of Multimedia Information System
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• 제8권3호
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.

• ETRI Journal
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• 제41권4호
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.