• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.245-259
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• 2022

Smart rural construction is an important direction for the Chinese government to promote the cause of "Rural Revitalization". In this paper, in the Xikou village project of the "future community" smart rural demonstration development project proposed by the local government of Zhejiang Province, China, the researcher participated in the design of the intellectualization of the smart rural life circle from the perspective of UX design and through qualitative and quantitative research methods. Through field investigation, understand the users' needs for intelligent design of smart countryside, and on this basis, design the maximum space of the region as a cognitive smart rural demonstration park, and quantitatively analyze the user experience feedback after the completion of the project. Before and after the actual design and application of the "Xikou village" in the demonstration area, chapters 3 and 4 are the symbols that can remember the rural era, the Rural Cultural Exchange Square. In the intelligent office space and living space, the user needs are composed of the design results. In order to reduce the anxiety of residents and tourists caused by the intelligent environment, the artificial manual service part is also designed. Now, as a case of intelligent rural space design, resident residence is developing continuously.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.52-62
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• 2016

Smart City is urban development for complex problem solving that provides convenience and safety for citizens, and it is a blueprint for future cities. In 2008, the Korean government defined the construction, management, and government support of U-Cities in the legislation, Act on the Construction, Etc. of Ubiquitous Cities (Ubiquitous City Act), which included definitions of terms used in the act. In addition, the Minister of Land, Infrastructure and Transport has established a "ubiquitous city master plan" considering this legislation. The concept of U-Cities is complex, due to the mix of informatization and urban planning. Because of this complexity, the foundation of relevant regulations is inadequate, which is impeding the establishment and implementation of practical plans. Smart City intelligent service facilities are not easy to define and classify, because technology is rapidly changing and includes various devices for gathering and expressing information. The purpose of this study is to complement the legal definition of the intelligent service facility, which is necessary for integrated management and operation. The related laws and regulations on U-City were analyzed using text-mining techniques to identify insufficient legal definitions of intelligent service facilities. Using data gathered from interviews with officials responsible for constructing U-Cities, this study identified problems generated by implementing intelligent service facilities at the field level. This strategy should contribute to improved efficiency management, the foundation for building integrated utilization between departments. Efficiencies include providing a clear concept for establishing five-year renewable plans for U-Cities.

• Convergence Security Journal
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• v.20 no.4
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• pp.113-123
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• 2020

The fourth industrial revolution can be called the hyper-connected-based intelligent revolution triggered by advanced information technology and intelligent technology, and the basis for implementing these technologies is 'data'. This study proposes a way to strategically use data in order to lead this intelligent revolution in the defense area. First of all, implications through analysis of domestic and international trends and prior research and current status of defense data management were analyzed, and four directions for development were presented. If the government composes conditions for building, releasing, sharing, distribution, and convergence of defense data considering the environment of national defense in the future, it is expected that it will serve as a foundation and a shortcut to be a digitalized strong military through smart defense innovation in the era of the fourth industrial revolution.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.11-19
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• 2020

A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager's decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods.

• Smart Media Journal
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• v.12 no.11
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• pp.185-197
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• 2023

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.519-531
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• 2020

Global warming is accelerating as greenhouse gas emissions increase owing to the increase in population and urbanization rates worldwide. As an alternative to this solution, smart cities are being promoted. The purpose of this paper is to suggest a plan for developing energy service modules for the Sejong 5-1 living area, which has been selected as a test-bed for smart cities in Korea. Based on the smart city plans announced by the government for this study, a survey questionnaire on 12 energy services was composed by collecting the opinions of experts. The survey was conducted with 1,000 citizens, the degree of necessity of energy service that citizens think of was identified. Principal Component Analysis and Association Rule Mining were conducted to describe 12 energy service items in a reduced manner and analyze the correlation and relationship of each energy service. Finally, three modules were suggested using the analyzed results so that 12 energy services could be implemented in an efficient platform. These results are expected to contribute to the realization of a smart city to make them easily accessible for those who want to promote platform services in the energy field and envision energy service items.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.569-572
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• 2012

식물공장의 다양한 건물형태에 따른 냉난방부하의 차이를 DesignBuilder를 이용하여 해석하였다. 엽채류 중 상추의 생육 적정온도인 $20^{\circ}C$를 실내온도로 설정하고 식물공장 외벽에 단일창호를 적용하여 온실형태에 따른 냉난방부하 변화를 계산하였다. 보광이 없는 건물자체의 냉난방부하를 계산한 결과 KNU 식물공장 단위유닛과 반원 온실형 식물공장이 냉방부하가 가장 적었다. 보광 적용 후에는 전반적으로 KNU 식물공장 단위유닛과 직사각형 식물공장이 양호한 냉난방 성능을 보여주었다. KNU 식물공장 창호의 면적이 와이드 스팬형보다 크기 때문에 건설비용의 증가가 예상되지만 회전형 스마트 조명 제어시스템을 적용하게 되면 재배면적을 넓히고 공간활용에 유리하다. 또한 LED 사용량을 줄일 수 있어 냉방부하와 보광에 드는 비용을 절감할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.710-712
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• 2019

오늘 날 산업현장에서는 현장에서의 생산성 향상과 안전을 강화하기 위한 방법으로 IT기술을 접목한 현장 안전관리 분야가 비약적으로 성장하고 있다. 특히 제조업, 건설업 분야의 산업현장에서 발생하는 안전사고에 대응하기 위해 현장 관리자를 보조하는 IT 기술은 지속적인 분석과 개선이 요구된다. 본 논문에서는 산업현장에 투입되는 근로자의 안전을 위하여 인물 Tracking과 광학문자인식 기법을 활용하여 스마트 현장 안전관리 시스템의 구조와 개선방안을 제시한다.