• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.179-187
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• 2023

Consumer trust plays a crucial role in the development of the sharing economy. This study primarily focuses on the factors influencing consumer trust and examines the case of ofo, a former leader in China's bike-sharing industry. This paper analyzes the decline in consumer trust in ofo, which can be attributed to internal management issues and the near-bankruptcy situation. The "difficulty in refunds" issue faced by ofo since December 2018 has been growing continuously, and this study explores the factors influencing trust and distrust in this context. By considering product factors (quality), platform factors (payment security, privacy protection, reputation), and social factors (social norms, government regulation) as independent variables, the study analyzes the factors affecting consumer trust. The analysis results revealed that as consumers' distrust towards shared bikes increases, their switching intention also increases. The company's reputation and social norms were found to influence both trust and distrust, while government regulation was found to influence trust. The research findings provide insights relevant to sharing economy platforms and offer guidance for future studies.

• Journal of Service Research and Studies
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• v.11 no.3
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• pp.55-74
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• 2021

In the global era, the tourism industry is a major profitable business, and the state and local governments are making various efforts to provide quality tourism experiences. The purpose of this study in a variety of tourism contents is to study barrier-free subway services in line with the global era, and to expand from the existing rapid, safe, and ordered transportation to the provision of high-quality comfort and all-round service experiences. This study compared and analyzed the subway service systems of Korea, China and both countries through the service design method, and presented a barrier-free subway service system to improve the user's satisfaction with the subway service system by improving the user's service experience. As a result, research results showed that 20 attractive quality attributes in 17 fields, such as convenience facilities, language issues, security equipment, and riding environment, play an important role in improving the quality and experience of subway services. In addition, through the construction of a Service Spatial Scenario, an optimized subway service system can be visualized to help understand this so that it can be used as a reference for creating a strategic application.

• Korean Journal of Legislative Studies
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• v.27 no.3
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• pp.35-68
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• 2021

In the German federal election in 2021, the Social Democrats returned to power by a narrow margin and the Green Party emerged as the biggest winner. The two political parties took the lead by proposing policies that met the expectations of the people in the policies of climate and environment, pandemic response and health, and labor and social security. The Merkel effect did not play a significant role in the election, and it is highly likely that it will lead to government policy after the formation of a coalition. While the class cleavage in voting behavior has weakened, the generational cleavage has grown relatively large. Older people showed more support for the two major parties, while younger people showed higher support for the Green Party and the FDP. If the generational cleavage continues, it can be linked to the growth of the Green Party and the FDP, the continued weakening of the two major parties and the emergence of other new parties. In addition, the regional cleavage between the former East and West Germany still remain, which will affect the direction of the AfD and the Left and combine with other political cleavages. The 2021 German federal election can be said to be an election that heralds the realignment of the political party system.

• Journal of Information Technology Services
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• v.22 no.5
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• pp.1-16
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• 2023

This study aimed to provide fundamental insights into the digital customer experience by identifying its components and analyzing their importance and satisfaction levels among youths. To achieve this objective, the components of digital customer experience were identified through a review of prior research and consultation with experts. Subsequently, a survey was conducted with 200 youths in Seoul and Gyeonggi-do. The main findings of the study are as follows: First, The components of the digital customer experience consisted of 12 items grouped into three categories. Second, an analysis of the disparity between the importance and satisfaction levels of digital customer experience revealed statistically significant differences across all items. Third, By utilizing IPA (Importance-Performance Analysis), the digital customer experience was categorized into four quadrant, each with its own characteristics and recommendations for management: The first quadrant, the "current level maintenance area," encompassed items related to "entertainment" and "recommended service." This area is currently functioning well but necessitates continuous attention and management. The second quadrant, the "area to be supported first," included items such as "personalization," "security," "inducing participation," "privacy," and "individuality expression." Intensive management and improvements are imperative in this quadrant. The third quadrant, the "long-term improvement area," consisted of items like 'consistency,' 'information quality,' and 'convenience.' These items require focus on long-term enhancement efforts. The fourth quadrant, the "areas where efforts have already been invested," encompassed items like 'accessibility' and 'deliberation.' It appears that excessive investment has been made in these areas relative to their importance, calling for selective investments while considering the specific issues associated with each factor. These research findings serve as essential data for managing the digital customer experiences of youths.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.43-50
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• 2024

This manuscript proposes an artificial intelligence-based(AI) energy platform system that efficiently use existing energy than creating new energy than creating new energy sources. To this end, it collects public information data portal and statistics data portal and data emissions, including energy usage and greenhouse gas emissions, including energy consumption and greenhouse gas emissions.In addition, it provides strong security and personal information protection functions to overcome the limit of existing energy platform. Through the built energy platform, improving power supply and user convenience of users and users to contribute to global warming issues.In this paper, the contents to implement the contents of the system, and improvement direction from the future completion and improvement direction.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.103-108
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• 2024

In this paper, a smart elevator system was studied using real-time object detection technology based on YOLO(You only look once)v5. When an external elevator button is pressed, the YOLOv5 model analyzes the camera video to determine whether there are people waiting, and if it determines that there are no people waiting, the button is automatically canceled. The study introduces an effective method of implementing object detection and communication technology through YOLOv5 and MQTT (Message Queuing Telemetry Transport) used in the Internet of Things. And using this, we implemented a smart elevator system that determines in real time whether there are people waiting. The proposed system can play the role of CCTV (closed-circuit television) while reducing unnecessary power consumption. Therefore, the proposed smart elevator system is expected to contribute to safety and security issues.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.993-1001
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• 2024

The TANDEM project is a European initiative funded under the EURATOM program. The project started on September 2022 and has a duration of 36 months. TANDEM stands for Small Modular ReacTor for a European sAfe aNd Decarbonized Energy Mix. Small Modular Reactors (SMRs) can be hybridized with other energy sources, storage systems and energy conversion applications to provide electricity, heat and hydrogen. Hybrid energy systems have the potential to strongly contribute to the energy decarbonization targeting carbon-neutrality in Europe by 2050. However, the integration of nuclear reactors, particularly SMRs, in hybrid energy systems, is a new R&D topic to be investigated. In this context, the TANDEM project aims to develop assessments and tools to facilitate the safe and efficient integration of SMRs into low-carbon hybrid energy systems. An open-source "TANDEM" model library of hybrid system components will be developed in Modelica language which, by coupling, will extend the capabilities of existing tools implemented in the project. The project proposes to specifically address the safety issues of SMRs related to their integration into hybrid energy systems, involving specific interactions between SMRs and the rest of the hybrid systems; new initiating events may have to be considered in the safety approach. TANDEM will study two hybrid systems covering the main trends of the European energy policy and market evolution at 2035's horizon: a district heating network and power supply in a large urban area, and an energy hub serving energy conversion systems, including hydrogen production; the energy hub is inspired from a harbor-like infrastructure. TANDEM will provide assessments on SMR safety, hybrid system operationality and techno-economics. Societal considerations will also be encased by analyzing European citizen engagement in SMR technology safety.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Community Safety and Security by Environmental Design
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• v.9 no.2
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• pp.97-121
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• 2018

This study is to suggest planning of children's parks for the safe environment and the priority of planning by comparing the differences of perception among the subjects related to the establishment and use of children's parks in order to create a safe environment. The results of this study are as follows. First, there was the greatest concern about 'traffic safety' in the safety of children's parks. Second, it is necessary to establish safe environment for 'play space' and 'facilities' among the space factors that constitute the children's park. Third, the difference of perceptions between experts and users about planning factors for safe environments of a children's park was most conspicuous concerning the issues related to 'rest space' and 'crime safety facilities'. Fourth, to create safe environments of a children's park, the planning factors belonging to 'crime prevention facility planning factor' and 'toilet planning factor'.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.35-42
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• 2024

In this study, we propose a method for real-time recognition and analysis of dog behavior using a motion sensor and deep learning techonology. The existing home CCTV (Closed-Circuit Television) that recognizes dog behavior has privacy and security issues, so there is a need for new technologies to overcome them. In this paper, we propose a system that can analyze and care for a dog's behavior based on the data measured by the motion sensor. The study compares the MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Network) models to find the optimal model for dog behavior analysis, and the final model, which has an accuracy of about 82.19%, is selected. The model is lightened to confirm its potential for use in embedded environments.