Recently, due to the increase in domestic and international online e-commerce platforms and the increase in container traffic at domestic ports, the operating ratio of large trucks has increased, and the number of truck fires is continuously increasing. In particular, spontaneous combustion is the most common cause of truck fires. Various academic approaches have been attempted to prevent truck fires, but due to the lack of research on the spontaneous tire ignition phenomenon that occurs during braking, this research directly designed and manufactured an experimental device to establish an environment similar to the braking system of a truck. A non-contact temperature sensor was installed on the brake device of the experimental device to collect temperature data generated from the brake device. Based on the data collected from the temperature sensor of the brake device and the temperature sensor on the tire surface, the ARIMA model among the time series prediction models was used to Appropriate parameters were selected to suit the temperature change trend, and as a result of comparing and analyzing the measured and predicted data, an accuracy of over 90% was obtained. Based on this, a plan was proposed to reduce the rate of fires in trucks by providing real-time warnings and support for truck drivers to respond to overheating phenomena occurring in the braking system.
In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.
In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.
The Construction Technology Information System collects, processes, and provides the public with free of charge 16 types of construction technology-related texts or index information, such as construction standards, construction practices, and construction reports, required at construction sites to enhance domestic construction technology. This study judged that user satisfaction could be increased if the budget was allocated first to the most effective construction technology information DB establishment. For each type of construction technology information, the level of satisfaction felt by the population for the quantity provided and the value of use according to the acquisition and utilization of information were investigated. Reliability between independent variables and dependent variables through a multiple regression analysis process to identify how the demographic characteristics of the population affect user satisfaction and the factors that affect the use value of information for each type of construction technology information. and the correlation was analyzed. The correlation between the demographic characteristics of respondents and users' satisfaction with the provided quantity was generally low, but construction experience, age, etc. were found to have an effect on satisfaction with the provided quantity. In particular, among the demographic measurement items of respondents, the more construction experience they had, the higher their satisfaction level with the quantity provided of low-level technical standards, and it was analyzed that older age had an impact on satisfaction with the quantity provided for small and medium-sized business support information.
The elderly in rural areas are faced with the dilemma of poor community environment, weak social communication ability and insufficient pension knowledge reserve. In addition, due to the serious shortage of social security facilities in rural areas and medical resources, the elderly are struggling, and the suicide rate is far higher than that in urban areas. In order to make the elderly have a comfortable pension environment and face the increasingly serious aging problem with a positive attitude, this paper takes the community environment of the Baima Tibetan elderly in Tielou Township, Gansu Province as the research object of aging transformation. First of all, literature data were used to carry out research on the aging transformation in rural areas. On the basis of sorting out previous research topics, ERG theory was determined as the guide. Secondly, the research methods of on-site investigation, interview and other research methods are adopted to investigate the number of left-behind elderly people in this area, and classify them according to the national standards. At the same time, the image of the current situation of the community environment of the elderly. Finally, combined with the ERG theory, the transformation design of the elderly living environment is implemented, mainly from the three aspects of survival, mutual relationship and growth.
Fourth Industrial Revolution and COVID-19 pandemic have boosted the use of Government 24 app for public service complaints in the era of non-face-to-face interactions. there has been a growing influx of complaints and improvement demands from users of public apps. Furthermore, systematic management of public apps is deemed necessary. The aim of this study is to analyze the grievances of Government 24 app users, understand the current dissatisfaction among citizens, and propose potential improvements. Data were collected from the Google Play Store from May 2, 2013, to June 30, 2023, comprising a total of 6,344 records. Among these, 1,199 records with a rating of 1 and at least one 'thumbs-up' were used for topic modeling analysis. The analysis revealed seven topics: 'Issues with certificate issuance,' 'Website functionality and UI problems,' 'User ID-related issues,' 'Update problems,' 'Government employee app management issues,' 'Budget wastage concerns ((It's not worth even a single star) or (It's a waste of taxpayers' money)),' and 'Password-related problems.' Furthermore, the overall trend of these topics showed an increase until 2021, a slight decrease in 2022, but a resurgence in 2023, underscoring the urgency of updates and management. We hope that the results of this study will contribute to the development and management of public apps that satisfy citizens in the future.
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.
The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.
Offshore wind power generation has significant advantages, including enhanced energy security and job creation. However, despite these benefits, South Korea has not fully utilized its potential in this sector. In contrast, offshore wind power industry development in Europe has been driven by government leadership. Drawing from this experience, South Korea also needs to relax regulations, strengthen necessary infrastructure, and enhance financial support systems to activate the offshore wind power industry. For this, sustained government leadership is absolutely essential. Without addressing the capacity issues in the power grid, we cannot expect offshore wind power generation to succeed. To address grid issues, we propose the enactment of a special law called the "Special Act on Grid Expansion." Considering KEPCO's financial situation, private investment should be encouraged for grid construction. The role of developers is crucial for the successful development and operation of offshore wind power. They manage risks throughout various stages, from site acquisition to construction and operation, which have a significant impact on the success or failure of projects. Since domestic developers currently lack experience in offshore wind power, a cooperative strategy that leverages the experience and technology of advanced countries is necessary. Energy issues should be recognized as important tasks beyond mere political ideologies, as they are crucial for the survival of the nation and its development. It is essential to form a public consensus and implement ways for residents to coexist with offshore wind power, along with the conservation of marine ecosystems and effective communication with stakeholders. Expansion of the offshore wind power industry requires support in various areas, including financial and tax incentives, technology research investment, and workforce development. In particular, achieving carbon neutrality by 2050 necessitates the activation of offshore wind power alongside efforts by major corporations to transition to renewable energy. South Korea, surrounded by the sea, holds significant offshore wind power potential, and it is our responsibility to harness it as a sustainable energy source for future generations. To activate the offshore wind power market, we need to provide financial and tax support, develop infrastructure and research, and foster a skilled workforce. As major corporations transition to renewable energy to achieve carbon neutrality by 2050, offshore wind power must play a significant role. It is our responsibility to fully utilize South Korea's potential and make offshore wind power a new driver of growth.
As the limitations of professional development programs and individual attempts to improve teaching expertise have been reported, mathematics teachers have operated various types of teacher learning communities as alternative teacher professional programs. A teacher learning community can be considered a Community of Practice(CoP) in that it satisfies three factors of Cop, which are common purpose, mutual participation, and shared repertoire, so the 'learning' of a teacher community can be interpreted based on the theory of CoP. The purpose of this study is to investigate the process of identity development of five mathematics teachers who have been continuously involved in teacher communities. For this, the researcher collected data on the entire process of community activities through participant observation and conducted individual follow-up interviews to explore mathematics teachers' narratives and personal experiences. Results indicated that mathematics teachers experienced the development of practical knowledge related to mathematics teaching and learning, improvement of teaching practice through continuous reflection and introspection, and recognization the shared value of togethering through community immersion. Based on these experiences, implications for the effective operation of learning communities such as national support of teacher learning communities and horizontal and cooperative teacher norms were discussed, and follow-up research was proposed.
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