• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.247-254
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• 2021

As COVID-19 spread, non-face-to-face activities were required, and the use of service robots is gradually increasing. This paper analyzed the relationship between the increasing trend of service robots before and after COVID-19 through keyword search containing the keyword 'service robot AND non-face-to-face' over the past three years (2018.10-20219) using BigKines, a news big data analysis system. As a result, there were 0 cases in the first period (2018.10~2019.9), 52 cases in the second period (2019.10~2020.9) and 112 cases in the third period (2020.10~2021.9), an increase of 115% compared to the second period. The keywords commonly mentioned in the analysis of related words in the second and third periods were COVID-19, AI, the Ministry of Trade, Industry, and Energy, and LG Electronics, and the weight of COVID-19 was the largest, confirming that the analysis keyword. Due to the spread of Corona 19, non-face-to-face is required, and with the development of information and communication technology, the field of application of service robots is rapidly increasing. Accordingly, for the commercialization of service robots that will lead the non-face-to-face economy, there is an urgent need to nurture human resources that require standardization and expertise in safety and performance fields.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.191-198
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• 2021

Since the size of training dataset become large and the model is getting deeper to achieve high accuracy in deep learning, the deep neural network training requires a lot of computation and it takes too much time with a single node. Therefore, distributed deep learning is proposed to reduce the training time by distributing computation across multiple nodes. In this study, we propose hybrid allreduce strategy that considers the characteristics of each layer and communication and computational overlapping technique for synchronization of distributed deep learning. Since the convolution layer has fewer parameters than the fully-connected layer as well as it is located at the upper, only short overlapping time is allowed. Thus, butterfly allreduce is used to synchronize the convolution layer. On the other hand, fully-connecter layer is synchronized using ring all-reduce. The empirical experiment results on PyTorch with our proposed scheme shows that the proposed method reduced the training time by up to 33% compared to the baseline PyTorch.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.109-126
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• 2018

With massive and dense production, the livestock industry is rapidly moving into a large-scale, capital-intensive industry especially in swine, poultry, and ducks. However, livestock epidemics can pose a serious threat to the livestock industry and the lives of the people. The government has established and operates the National Animal Protection and Prevention System (KAHIS) since 2013 in order to control the threat, in accordance with the five stages. The digitalized data and information are excellent in ease of management, but it is also pointed out that it is difficult to take countermeasures through linkage with the data in an emergency situation. Recently, the technology of the fourth industrial revolution such as Internet of Things (IoT), Big Data, Artificial intelligence (AI) has been rapidly implemented to the livestock industry, which makes smart livestock disease control system possible. Therefore, this study investigated the domestic and overseas cases which apply 4th Industrial Revolution technology in the industry, and derived 13 possible candidate tasks in the near future. In order to ascertain the priority of policy formulation, we surveyed the expert groups and examined the priority of each of the five stages of the prevention and the priority of each stage. The results of this study are expected to contribute to the establishment of policies for the advancement of smart livestock disease control research and livestock protection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.73-82
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• 2019

In order to build a smart factory, building a CPPS (Cyber Physical Product System) is an important system that must be accompanied. Through the CPPS, it is the reality of smart factories to move physical factories to a digital-based cyber world and to intelligently and autonomously monitor and control them. But The existing CPPS architectures present only an abstract modeling architecture, and the research that applied the OPC UA Framework (Open Platform Communication Unified Architecture), an international standard for data exchange in the smart factory, as the basic system of CPPS It was insufficient. Therefore, it is possible to implement CPPS that can include both cloud and IoT by collecting field data distributed by CPPS architecture applicable to actual factories and concentrating data processing in a centralized In this study, we implemented CPPS architecture through central OPC UA Server based on OPC UA conforming to central processing OPC UA Framework, and how CPPS logical process and data processing process are automatically generated through OPC UA modeling processing We have proposed the CPPS architecture including the model factory and implemented the model factory to study its performance and usability.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.41-51
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• 2018

Recently, in the field of video surveillance, deep learning based learning method is applied to intelligent video surveillance system, and various events such as crime, fire, and abnormal phenomenon can be robustly detected. However, since occlusion occurs due to the loss of 3d information generated by projecting the 3d real-world in 2d image, it is need to consider the occlusion problem in order to accurately detect the object and to estimate the pose. Therefore, in this paper, we detect moving objects by solving the occlusion problem of object detection process by adding depth information to existing RGB information. Then, using the convolution neural network in the detected region, the positions of the 14 keypoints of the human joint region can be predicted. Finally, in order to solve the self-occlusion problem occurring in the pose estimation process, the method for 3d human pose estimation is described by extending the range of estimation to the 3d space using the predicted result of 2d keypoint and the deep neural network. In the future, the result of 2d and 3d pose estimation of this research can be used as easy data for future human behavior recognition and contribute to the development of industrial technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.603-613
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• 2021

As a core policy for achieving the goal of reducing greenhouse gas emissions in the building sector, Korea has enforced the mandatory certification of zero energy buildings for new public buildings from 2020. This study suggests energy-saving technologies and economic factors that building officials can refer to for decision-making on the implementation of zero energy buildings. For this study, the construction cost for the energy item of a building was analyzed by collecting the building energy efficiency level certification data and detailed construction cost statement data from public institutions for the last three years. Based on the building energy efficiency certification data, each energy item of the baseline building was derived, and the energy performance of the zero energy building was derived through repetitive simulations by gradually increasing the energy performance value of the baseline building. By applying the analyzed construction cost, the construction cost for each energy item of the baseline and zero energy buildings was derived. As a result, the lighting equipment contributed up to 10.5% energy savings, and the increase in construction cost of the cooling and heating system was at least 9.1%.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.185-210
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• 2020

The library faced an unexpected crisis of COVID-19, and as a countermeasure strategy, non-face-to-face online service has been reinforced. Therefore, this study attempted to present a plan to overcome the challenges arising from rapidly changing external environment and current crisis. To this end, data search, electronic library, library service, cultural event and open space management status of 288 public libraries serviced as an integrated site were investigated. Based on this, the meaning of online services in the post-COVID-19 era and the implication of it were examined. As a result, first, the increase in the use rate of online data search services with the spread of non-face-to-face culture, second, the expansion of the services of the electronic library, third, the diversification of non-face-to-face, online services, fourth, expansion of online cultural event services, fifth, the diversification of open space services were proposed, sixth, Introduced an artificial intelligence system for unattended loan return based on access and the Seventh, expansion of experiential cultural support services and educational contents through VR, AR and MR. It is deemed necessary for the research on the future direction of the library's non-face-to-face services to be conducted by investigating the current status of online services in various types of libraries and the types and case studies of library services in the era of COVID-19.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.268-278
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• 2021

Technologies that are leading the fourth industrial revolution, such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cyber-physical systems (CPS) are developing and generalizing. The demand to improve productivity, economy, safety, etc., is spreading in various industrial fields by applying these technologies. Digital twins are attracting attention as an important technology trend to meet demands and is one of the top 10 tasks of the Korean version of the New Deal. In this study, papers, magazines, reports, and other literature were searched using Google. In order to investigate the contribution or role of geospatial information in the digital twin application, the definition of a digital twin, we investigated technology trends of domestic and foreign companies; the components of digital twins required in manufacturing, plants, and smart cities; and the core techniques for driving a digital twin. In addition, the contributing contents of geospatial information were summarized by searching for a sentence or word linked between geospatial-related keywords (i.e., Geospatial Information, Geospatial data, Location, Map, and Geodata and Digital Twin). As a result of the survey, Geospatial information is not only providing a role as a medium connecting objects, things, people, processes, data, and products, but also providing reliable decision-making support, linkage fusion, location information provision, and frameworks. It was found that it can contribute to maximizing the value of utilization of digital twins.