• Informatization Policy
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• v.30 no.3
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• pp.29-48
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• 2023

This study aims to investigate empirically the structural relationships among the components of data governance and their impacts on data integration and data-based administration. Through literature review, various definitions, typologies, and case studies of data governance were examined, with the definition of data governance from a public policy perspective developed and applied. The study then analyzed the data from a survey conducted by the Korea Institute of Public Administration on the use of public data policies and confirmed that organizational factors play a mediating role between institutional and technical factors, and that institutional and technical factors have statistically significant positive relationships with data fusion and data-driven administration. Based on these results, interest and investment in the improvement and development of the legal system in data governance from the institutional, technical, and organizational perspective, clarification of means and purposes of data technology, interest in data organizations and human resources, and practical operation can be achieved. Policy implications such as the development of an effective mechanism were presented.

• Journal of Platform Technology
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• v.9 no.1
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• pp.23-31
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• 2021

In recent years, in the domestic industry, personal damage has occurred due to sensor malfunction and the emission of harmful gases. But there is a limit to the reliability verification of sensor data because the evaluation of environmental sensors is focused on durability and risk tests. This platform designed a sensor board that measures 10 major substances and a performance verification system for each sensor. In addition, the data collected by the sensor board was transferred to the server for data reliability evaluation and verification using LoRa communication, and a prototype of the sensor data platform was produced to monitor the transferred data. And the collected data is analyzed and predicted by using machine learning techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.154-160
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• 2018

This thesis aims to develop PBL (Problem-Based-Learning) problems. Its goal is for some groups of students to creative their own problems and to confirm the effectiveness of PBL as they apply it to AI (Artificial Intelligence) in engineering schools. Modern industrial society needs competent people who have abilities in cooperative learning, self-controlled learning, united knowledge application, and creative problem-solving. Universities need to offer their students the opportunity to improve their problem-solving and cooperative learning abilities in order to train the competent people that society demands. PBL activity is an appropriate learning method for the accomplishment of these goals. The study subjects are 37 sophomore students in H University who are studying 'AI'. Five PBL problems were submitted to the class over a period of 15 weeks. The students wrote and submitted a reflective journal after they finished each PBL activity. In addition, they filled out a class evaluation form to assess the performances of each member when the $5^{th}$ PBL problem activity was accomplished. The study shows that the students experienced the effectiveness of PBL in many fields, such as the comprehension of the studied contents (86.48%), comprehension of cooperative learning (94.59%), authentic experience (75.67%), problem-solving skills (89.18%), presentation skills (97.29%), creativity improvement (81.08%), knowledge acquisition ability (86.48%), communication ability (97.29%), united knowledge application (78.37%), self-directed study ability (86.48%) and confidence (97.29%). Through these methods, the students were able to realize that PBL learning activities play an important role in their learning. These methods prepare and enhance their ability to think creatively, work systematically and speak confidently as they learn to become competitive engineers equipped with the knowledge and skills that modern industrial society demands.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.79-92
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• 2018

The evaluation theory of reliability, availability, maintainability and safety (RAMS) as a mature theory of state evaluation in the railway engineering, can be well used to the evaluation, management, and maintenance of complicated structure like the long-span bridge structures on the high-speed railway. Taking a typical steel-truss arch bridge on the Beijing-Shanghai high-speed railway, the Nanjing Dashengguan Yangtze River Bridge, this paper developed a new method of state evaluation for the existing steel-truss arch high-speed railway bridge. The evaluation framework of serving state for the bridge structure is presented based on the RAMS theory. According to the failure-risk, safety/availability, maintenance of bridge members, the state evaluation method of each monitoring item is presented. The weights of the performance items and the monitoring items in all evaluation levels are obtained using the analytic hierarchy process. Finally, the comprehensive serving state of bridge structure is hierarchical evaluated.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.168-170
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• 2022

The government enacted and promulgated the 'Severe Accident Punishment Act' in January 2021, and is enforcing the law for workplaces with 50 or more full-time workers. However, the number of industrial accident accidents in 2021 increased by 10.7% compared to the same period of the previous year, and chemical gas Safety accidents due to leaks and explosions also occur frequently. Therefore, in high-risk industrial sites, comprehensive Safety measures are urgently needed. In this study, BLE Mesh networking in industrial sites with poor communication environment apply technology. The complex sensor AIoT device recognizes a dangerous situation as a gas sensing value, voice, and motion value, and transmits it to the server. The server monitors the risk situation in real time through information value analysis and judgment through artificial intelligence LSTM algorithm and CNN algorithm for AIoT transmission information. Through this study, through the development of AIoT devices capable of gas sensing, voice and motion recognition, and AI-applied safety management systems, It will contribute to the expansion of the social safety net by expanding its application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.341-348
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• 2020

The ability to extract useful information from an image, such as the human eye, is an interface technology essential for AI computer implementation. The building recognition technology has a lower recognition rate than other image recognition technologies due to the various building shapes, the ambient noise images according to the season, and the distortion by angle and distance. The computer vision based building recognition algorithms presented so far has limitations in discernment and expandability due to manual definition of building characteristics. This paper introduces the deep learning CNN (Convolutional Neural Network) model, and proposes new method to improve the recognition rate even by changes of building images caused by season, illumination, angle and perspective. This paper introduces the partial images that characterize the building, such as windows or wall images, and executes the training with whole building images. Experimental results show that the building recognition rate is improved by about 14% compared to the general CNN model.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.105-112
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• 2024

In contemporary society, the expression of emotions and self-reflection are considered pivotal factors with a positive impact on stress management and mental well-being, thereby highlighting the significance of journaling. However, traditional journaling methods have posed challenges for many individuals due to constraints in terms of time and space. Recent rapid advancements in chatbot and emotion analysis technologies have garnered significant attention as essential tools to address these issues. This paper introduces an artificial intelligence chatbot that integrates the GPT-3 model and emotion analysis technology, detailing the development process of a system that automatically generates journals based on users' chat data. Through this system, users can engage in journaling more conveniently and efficiently, fostering a deeper understanding of their emotions and promoting positive emotional experiences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.177-183
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• 2022

Recently, interesting of home training is getting bigger due to COVID-19. Accordingly, research on applying HAR(human activity recognition) technology to home training has been conducted. However, existing paper of HAR proposed static activity instead of dynamic activity. In this paper, the deep learning model where dynamic exercise posture can be analyzed and the accuracy of the user's exercise posture can be shown is proposed. Fitness images of AI-hub are analyzed by blaze pose. The experiment is compared with three types of deep learning model: RNN(recurrent neural network), LSTM(long short-term memory), CNN(convolution neural network). In simulation results, it was shown that the f1-score of RNN, LSTM and CNN is 0.49, 0.87 and 0.98, respectively. It was confirmed that CNN is more suitable for human activity recognition than other models from simulation results. More exercise postures can be analyzed using a variety learning data.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.24-29
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• 2019

AIS is one of the most critical systems in any organization. Data quality plays a critical role in a knowledge-based economy. The objective of this study is to identify the most important factors for accounting information quality and their impact on AIS data quality outcomes. This study includes an extensive literature review to identify a set of CSF for data quality. The study uses empirical data to test the research hypothesis and resluts show that the top three most important factors that affect AIS's data quality are toop management commitmentm the nature of the AIS and input controls. The study further uses regression analysis to test the effect of those factors on AIS data quality, finding that there is a significant positive relationship between the perceived performance of the three factors and AIS data quality putcomes. To be develop to AIS data quality further study for CSF's control methodology is necessary.