• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.436-438
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• 2000

현재 각 국가별로 추진 중인 초고속 정보통신망은 지금까지 전화, 데이터 CATV 등 서비스별 특성에 따라 구축되어온 개별 망들을 대용량 전송, 광대역 교환, 고도지능형 관리 술을 바탕으로 통합하고, 이를 개별 망에서 제공하고 있는 서비스들은 물론 미래에 예상되는 멀티미디어 서비스들도 효과적으로 수용하는 것을 목표로 한다. 이를 가능하기 위하여 모든 초고속 정보통신망은 ATM(Asynchronous Transfer Mode)기술을 기반으로 하고 있다. 이에 따라 최근에는 ATM 기반 초고속 정보통신망 자원을 효율적으로 운용, 관리하는 방안이 통신망 사업자의 주요 관심사로 대두되고 있다. 본 논문에서는 ATM통신망에서의 자원 관리의 범주를 단순한 대역폭 할당 뿐 아니라 효율적인 자원관리를 위해 기본적인 트래픽 제어 등으로 폭넓게 고려하여 ATM 망자원 분배를 위한 다양한 접근 방법들을 분석하였다.

• Annual Conference of KIPS
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• 2020.05a
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• pp.617-620
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• 2020

최근, 국내 자동차 보유율은 매년 증가하고 있으며, 자동차 증가율에 따라 자동차로 인한 사건, 사고 발생률 또한 증가하고 있다. 국가에서도 지능형교통시스템(ITS) 중 차량 변호판을 인식하는 연구가 활발히 진행되고 있다. 차량 번호판 인식은 사건·사고 발생차량을 추적하거나 주차 무인시스템 등의 분야에 적용된다. 본 논문에서는 차량 번호판 영역을 추출하기 위한 여러 가지 방법들을 비교 분석하여 각 상황에 맞는 알고리즘을 적용하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.33-39
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• 2021

In this study, there are regional limitations for the fine dust data provided by public data to represent the wide area air quality. It is about a method that can provide detailed data in a specific area beyond location restrictions. The goal of this implementation is to mount the IoT-based fine dust sensor on a quadcopter that can fly autonomously beyond the positional limitations of the current measuring station, which is fixed, and quickly move the fire-generating area and the area of rapid fine dust to make detailed measurements. Through this, the system is designed and implemented to enable the fundamental analysis of the occurrence. We designed a QDMS system and proposed a method to measure detailed changes in fine dust at a specific location through manufacturing and experiments.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.211-227
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• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.75-83
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• 2007

IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.933-939
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• 2011

Recent heavy rains have caused natural disasters such as flooding and landslides nationwide. Because of flooding occurrence in most of the roads, traffic congestion and isolation caused many loss especially at rush hour. Constant monitoring and analysis of past disaster history data are needed to prevent disasters on areas prone to floods and disaster risk areas. If we managed to obtain traffic volume, speed, phase around intersection using disaster history data when disasters occurred, we can analyse traffic congestion, change of disaster scale and rainfall. In this study, We select a target district to develop by using a route from Dae-nam intersection in Busan Namgu Daeyoeon-dong, over Gwangan large bridge up until Haeundae Olympic intersection, We developed a system which searches disaster history information, traffic volume using disaster history data based on user selection of the road.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.436-436
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• 2023

산업혁명을 거치면서 높은 화석연료를 사용하는 제조업 중심의 산업구조와 많은 자원을 필요로 하는 도시의 집중 현상으로 지구 온난화에 따른 이상기후 발생이 증가하고 있다. 이러한 기후변화는 홍수, 태풍, 폭염 및 폭설 등의 자연재해 발생 빈도 및 규모를 증가시켜 피해가 커지고 있다. 특히 인구 및 시설들이 집중해 있어 도시의 집중 현상은 이러한 재해에 더욱 취약한 구조가 됨에 따라 피해의 규모를 가중 시키고 있는 실정이다. 전 세계적으로 기후변화 문제의 심각성을 인식하고 이를 해결하기 위해 선신국에 의무를 부여하는 교토의정서(1997년) 채택에 이어, 선진국과 개도국이 모두 참여하는 파리협정(2015년)을 채택하였고 2016년 협정이 발효되었다. 파리협정의 목표는 산업화 이전 대비 지구 평균온도 상승을 2℃보다 아래로 유지하고, 나아가 1.5℃로 억제하기 노력하는 것을 강제하는 것으로 2050년까지 탄소 순배출량을 '0'으로 만든다는 탄소중립사회로의 전환이 본격적으로 시작되었다. 본 연구에서는 기후변화로 인한 물부족 및 수실오염과 같은 도시의 수자원 문제 해결을 위해 IoT 기반 센서 및 네트워크 기반 수자원 플랫폼을 개발하였다. 도시 수자원 시설 데이터를 기반으로 대체 수자원 확보 및 수요 중심의 물 관리를 통해 효율적인 물 배분이 될 수 있도록 하였으며 이러한 스마트 물 관리에 따른 대체 수자원 확보 및 효율적 물 배분이 탄소 저감에 미치는 효과에 대해 분석하였다. 연구대상 지역은 세종 6-4구역으로 LID 특화지구로 조성되었으며 1,000 세대의 주민이 생활하는 공동주택이다. 물 순환(LID) 시설에서 확보된 물을 물 공급 시설과 연계하여 공동주택에서 활용함으로써 감소된 상수 사용량을 온실가스 배출량으로 환산하여 탄소 저감량을 계산하였다. 실제 주민들(1,000세대)이 사용하고 있는 상수량 데이터와 전력거래소 온실가스 배출계수를 활용하였으며 물순환(LID) 시설로 확보하여 대체할 수 있는 상수량은 10%로 가정하였다. 연구대상 지역(1,000세대)의 연간 상수공급량은 331,603m³이며, 연간 전력사용량은69,637kWh이다. 온실가스 배출량은 31.963tCO₂eq이며, 온실가스 저감량은 3.2tCO₂eq로 산정되었다. 추후 LID 시설에 대한 상수 대체량과 온실가스 저감효과 정량화가 필요하다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.344-344
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• 2020

기후변화 가속화와 국민의 높아진 서비스 요구 수준에 따라 농업용수의 관리방식을 현장인력의 경험적 물관리 방식에서 계측정보 기반의 과락적 물관리 방식으로 전환의 필요성이 대두되어, 2001년부터 농업기반시설 내 무인계측, 원격제어 기능이 탑재된 물관리자동화 시스템을 보급하는 농업용수관리자동화사업을 시행하였다. 농업용수관리자동화사업은 사업시행 초기 연구 결과, 농업기반시설 무인계측 및 원격제어 시스템 보급으로 인력에 의한 관행적 물관리 대비 수리시설의 관리 효율성이 크게 향상되어 유지관리 인력의 절감 및 용수수급의 적정성이 개선될 것으로 분석되었다. 하지만 영농환경의 변화에 따라, 당초 분석결과와 달리 자동화사업 추진과 한국농어촌공사의 유지관리 인력 규모 간 뚜렷한 상관성이 보이지 않는다는 정책기관의 지적이 발생하고 있다. 현재 4차산업기술이 산업 전 분야에 걸쳐 일어나고 있으며 농업분야에도 ICT, LOT, 빅데이터 기술이 도입되어 새로운 가치를 창출하고 있다. 농업용수관리 분야에 있어서는 데이터를 활요한 수요자 중심의 지능형 물관리 사업이 추진되고 있으며, 일정규모 이상 저수지 및 양수장 농업용수 공급량 측정 계측기의 설치가 추진중에 있다. 그러나 현재까지 이러한 설치된 계측장치들의 활용방안에 대해서는 뚜렷한 결과가 도축된 바 없으며, 현재 많은 예산과 인력이 투입되어 설치·운영되고 있는 계측장치들의 활용 방안에 대해서 연구가 필요한 실정이다. 2018년 2,228개 농업기반시설물에 자동화시스템을 설치 완료 하였으나, 각종 장비의 비표준화, 효과대비 고비용, 잦은 통신두절 등의 기술적 문제로 인해 현업부서의 수자원관리 업무에서 자동화시스템의 활용성이 저조한 것으로 관측됐다. 본연구에서는 국내 수자원 계측제어 기술 동향 및 운영환경 조사 결과를 기초로, 기술적 측면의 농업용수관리자동화사업의 개선사항과 4차 산업기술의 농업용수관리자동화사업의 적용방안을 제시 하여 농업용수관리자동화사업 중장기 계획 개정등 향후 정챡수립시 참고 자료로의 활용과 농업용수 효율적 활용과 관리를 위한 TM/TC 미래추진 방안을 제시로 정확하고 신뢰도 높은 농업용수 관리 체계를 구축 하고자 한다.