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Currently, as the demand for stops in the urban increases resulting from an increase in the supply of express trains, the development of trains capable of operating both high-floor platforms in the urban and low-floor platforms in the suburbs is required. In this paper, we studied the design and fabrication of a controller for train doors that consists of low and high steps as a plug-in type door mechanism and thus can be used on low and high platforms. This DCU H/W was designed and implemented using 32 bit MCU to control 4 motors, 33 digital inputs and 16 digital outputs. In addition, based on the software life cycle of EN50128, 2 items of operation requirements and 12 items of control requirements were derived, and then they were designed and implemented as operation SW. The implemented SW was tested for each requirement. As a result, we performed tests on 13 items that could be tested in the mock-up out of 14 total requirement items and showed that the requirements were satisfied.

In this paper, we evaluate deep learning time series forecasting models. Recent studies show that those models perform better than the traditional prediction model such as ARIMA. Among them, recurrent neural networks to store previous information in the hidden layer are one of the prediction models. In order to solve the gradient vanishing problem in the network, LSTM is used with small memory inside the recurrent neural network along with BI-LSTM in which the hidden layer is added in the reverse direction of the data flow. In this paper, we compared the performance of Informer by comparing with other models (LSTM, BI-LSTM, and Transformer) for real Nitrogen dioxide (NO2) data. In order to evaluate the accuracy of each method, mean square root error and mean absolute error between the real value and the predicted value were obtained . Consequently, Informer has improved prediction accuracy compared with other methods.

In the process of collecting social reviews, a number of noise reviews irrelevant to a given search keyword can be included in the search results. To filter out such reviews, machine learning can be used. However, if the number of reviews is insufficient for a target place to be analyzed, filtering accuracy can be degraded due to the lack of training data. To resolve this issue, we propose a supervised learning method to improve accuracy of the noise review filtering for the places with insufficient reviews. In the proposed method, training is not performed by an individual place, but by a group including several places with similar characteristics. The classifier obtained through the training can be used for the noise review filtering of an arbitrary place belonging to the group, so the problem of insufficient training data can be resolved. To verify the proposed method, a noise review filtering model was implemented using LSTM and BERT, and filtering accuracy was checked through experiments using real data collected online. The experimental results show that the accuracy of the proposed method was 92.4% on the average, and it provided 87.5% accuracy when targeting places with less than 100 reviews.

Due to climate change, interest in crop production and distribution is increasing, and attempts are being made to use bigdata and AI to predict production volume and control shipments and distribution stages. Prediction of agricultural product imports not only affects prices, but also controls shipments of farms and distributions of distribution companies, so it is important information for establishing marketing strategies. In this paper, we create an artificial intelligence prediction model that predicts the future import volume based on the wholesale market melon import volume data disclosed by the agricultural statistics information system and evaluate its accuracy. We create prediction models using three models: the Neural Prophet technique, the Ensembled Neural Prophet model, and the GRU model. As a result of evaluating the performance of the model by comparing two major indicators, MAE and RMSE, the Ensembled Neural Prophet model predicted the most accurately, and the GRU model also showed similar performance to the ensemble model. The model developed in this study is published on the web and used in the field for 1 year and 6 months, and is used to predict melon production in the near future and to establish marketing and distribution strategies.

The purpose of this study is to design and develop the test standard to measure AI literacy abilities. First, we selected key areas of AI literacy through the related studies and expert FGI and designed detailed standard. The area of the test standard is divided into three categories: AI concept, practice, and impact. In order to confirm the validity of the test standard, we conducted twice expert validity tests and then modified and supplemented the test index. To confirm the validity of the test standard, we conducted an expert validity test twice and then modified and supplemented the test standard. The final AI literacy test standard consisted of a total of 30 questions. The AI literacy test standard developed in this study can be an important tool for developing self-checklists or AI competency test questions for measuring AI literacy ability.

This paper presents a method for predicting the threat index of combat systems using Gradient Boosting Regressors and Support Vector Regressors among machine learning models. Currently, combat systems are software that emphasizes safety and reliability, so the application of AI technology that is not guaranteed to be reliable is restricted by policy, and as a result, the electrified domestic combat systems are not equipped with AI technology. However, in order to respond to the policy direction of the Ministry of National Defense, which aims to electrify AI, we conducted a study to secure the basic technology required for the application of machine learning in combat systems. After collecting the data required for threat index evaluation, the study determined the prediction accuracy of the trained model by processing and refining the data, selecting the machine learning model, and selecting the optimal hyper-parameters. As a result, the model score for the test data was over 99 points, confirming the applicability of machine learning models to combat systems.

Various studies to enhance the accuracy of rice production forecasting are focused on improving the accuracy of the models. In contrast, there is a relative lack of research regarding the data itself, which the prediction models are applied to. When applying the same dependent variable and prediction model to two different sets of rice production data composed of distinct features, discrepancies in results can occur. It is challenging to determine which dataset yields superior results under such circumstances. To address this issue, by identifying potential influential features within the data before applying the prediction model and centering the modeling around these, it is possible to achieve stable prediction results regardless of the composition of the data. In this study, we propose a method to adjust the composition of the data's features in order to select optimal base variables, aiding in achieving stable and consistent predictions for rice production. This method makes use of the Korea Meteorological Administration's ASOS data. The findings of this study are expected to make a substantial contribution towards enhancing the utility of performance evaluations in future research endeavors.

In this paper, we analyze the problem of the user authentication scheme that provides dynamic ID in a multi-server environment proposed by Andola et al. and propose an improved authentication one to solve this problem. As a result of analyzing the authentication scheme of Andrea et al. in this paper, it is not safe for smart card loss attack, and this attack allows users to guess passwords, and eventually, the attacker was able to generate session key. This paper proposed an improved authentication scheme to solve these problems, and as a result of safety analysis, it was safe from various attacks such as smart card loss attack, password guess attack, and user impersonation attack. Also the improved authentication scheme not only provides a secure dynamic ID, but is also effective in terms of the computational complexity of the hash function. In addition, the improved authentication scheme does not significantly increase the amount of transmission, so it can be said to be an efficient authentication scheme in terms of transmission cost.

This research originated from the need to enhance the security of secure multiparty computation by ensuring that participants involved in multiparty computations provide truthful inputs that have not been manipulated. While malicious participants can be involved, which goes beyond the traditional security models, malicious behaviors through input manipulation often occur in real-world scenarios, leading to privacy infringements or situations where the accuracy of multiparty computation results cannot be guaranteed. Therefore, in this study, we propose a signature scheme applicable to secure multiparty technologies, combining it with secret sharing to strengthen the accuracy of inputs using authentication techniques. We also investigate methods to enhance the efficiency of authentication through the use of batch authentication techniques. To this end, a scheme capable of input certification was designed by applying a commitment scheme and zero-knowledge proof of knowledge to the CL signature scheme, which is a lightweight signature scheme, and batch verification was applied to improve efficiency during authentication.

Software birthmark refers to a unique feature inherent in software that can be extracted from program binaries even in the absence of the original source code of the program. Like human genetic information, the similarity between programs can be calculated numerically, so it can be used to determine whether software is stolen or copied. In this paper, we propose a new birthmark technique for Android applications developed using Unity. The source codes of Unity-based Android applications use C# language, and since the core logic of the program is included in the DLL module, it must be approached in a different way from normal Android applications. In this paper, a Unity birthmark extraction and comparison system was implemented, and reliability and resilience were evaluated. The use of the Unity birthmark technique proposed in this paper is expected to be effective in preventing illegal copy or code theft of the Unity-based Android applications.

The purpose of this study was to investigate the effect of 8 weeks of core training on dynamic posture control, lower extremity injury and proprioceptive joint position sensory in ski athletes. Twenty subjects participated in this study and were randomly divided into two groups : exercise group (Ex=10) and control group (Con=10). The core training program consisted of a bench, a sideways bench, a plank, a side bridge, and a supine bridge, and was conducted three times a week for 8 week. The dynamic posture control had a significant effect on the left and right postero-medial reach, and the lower extremity criterion test had a significant effect on the left and right composite scores. In addition, there was a significant decrease in the proprioceptive joint position sense at 15°of the left leg and 45°. In conclusion, 8 weeks a core training have been shown to improve skiers' dynamic posture control, lower extremity injury and proprioceptive joint position sensory.

This study was attempted to find out the effect of major satisfaction, maturation of career consciousness and clinical practice satisfaction of nursing college students on job-seeking stress. For this study, 157 3th, 4th grade students were participated from K University in K Province, using a self-reported questionnaire. Data analysis was performed using SPSS version 22.0. program. As a result of this study, it was revealed that there was a significant correlation between the job-seeking stress of nursing students on major satisfaction(r=-.270, p<.001), clinical practice satisfaction(r=-.283 p<.001), maturation of career consciousness(r=-.424, p<.001). Also, the factors affecting the job-seeking stress of nursing students were major satisfaction(β=-0.005, p<.05), clinical practice satisfaction(β=-0.126, p<.001), maturation of career consciousness(β=-0.369, p<.001) with a total explanatory power of 17.7%. Therefore consideration of educational programt be given to the s and support systems to reduced job seeking stress for nursing students.

The purpose of this study was the accuracy of cardiac compression, fatigue, and physiological changes of the rescuer for different mask type in cardiopulmonary resuscitation(CPR). Data collection was from 9 to 12 May 2022, the participants were a total of 24 paramedic students with a BLS provider at D University. The students participated in an experiment in which 12 students each wore a surgical mask (Dental mask) and a fine particle 94% blocking mask (KF94 mask) and performed CPR for 2 minutes over a total of 7 times. As a result of the study, in the analysis of the quality of the rescuer's chest compression according to the type of mask, there was a significant difference in the compression speed (F=24.91, p<.001) and bad compression hand position (F=14.54, p=.024) in the group wearing the KF94, Fatigue showed significant differences in both the Dental mask group (F=51.16, p<.001) and the KF94 mask group (F=63.49, p<.001). Among the physiological changes, heart rate showed a significant difference between the Dental mask group (F=34.79, p<.001) and the KF94 mask group (F=35.55, p<.001), and the respiratory rate showed a significant difference between the Dental mask group (F=25.02, p=.001) and the KF94 mask group(F=23.02, p=.002). Therefore, in order to improve the quality of efficient chest compression and reduce the fatigue and physiological changes of rescuers, it will be necessary for rescuers to wear suitable personal protective equipment.

This study analyzed 247 data surveyed on 119 ambulance workers 268 in a G-Do. The variance of the multiple regression model on the effect of independent variables on life satisfaction factors was statistically significant. And transformational leadership is an independent variable that significantly affects life satisfaction factors. However, there is a slight difference in the perception of transactional satisfaction factors (p=.051) was not statistically significant, so the second research question raised in this study was rejected. Based on the results of this study, transformational leadership should be centered on improving 119 paramedics' life satisfaction in the relationship between the direct effects of leadership variables related to 119 paramedics' life satisfaction. By doing so, 119 paramedics' life satisfaction will increase and the ultimate improvement in the quality of first aid at the emergency site will be achieved by providing a foothold to demonstrate the potential capabilities of 119 paramedics. In addition, 119 paramedics need to recognize and promote the importance of life satisfaction recognized in their daily lives and workplaces, creating an environment that can demonstrate transformative leadership by carefully caring for 119 center heads, team leaders, and field seniors.

In the future knowledge society, the importance of business data is expected to increase, and it is recognized as a raw material for companies to manufacture product or develop service. As the importance of data increases, methods to calculate the economic value of database assets is being studied. There are many studies to evaluate the value of database assets, but the characteristics of database assets are not fully reflected. In this study, we classified database assets into revenue-type, non-revenue-type, and public-type database assets by considering the characteristics of database assets. In addition, focusing on the fact that revenue-type database assets can be valued similarly to existing technology valuation, we developed a method for calculating the life of database assets that includes risk-adjusted discount rate.

The aim of this study is to examine the relationship between self-regulated learning, NLR(non-learning-related) behavior, interaction and flow on satisfaction in database classes. To achieve this purpose, this study proposed a research model consisting of self-regulated learning, NLR behavior, interaction, flow and satisfaction. A survey was conducted to test the research hypotheses, and a total of 122 online questionnaires were obtained and used for the final statistical analysis. The main findings of the analysis are as follows: First, flow was consistently identified as a key determinant of satisfaction. Second, self-regulated learning was found to have a significant effect on flow. Third, NLR behavior and interaction were found to mediate the relationship between self-regulated learning and flow. This study provides insights into the role of NLR behavior and interaction in promoting flow and offers implications for understanding how to promote flow.

In this paper, we examine the influence of Environmental, Social, and Governance (ESG)-related content on the success of online crowdfunding proposals. Along with the increasing significance of ESG standards in business, investment proposals incorporating ESG concepts are now commonplace. Due to the ESG trend, conventional wisdom holds that the majority of proposals with ESG concepts will have a higher rate of success. We investigate by analyzing over 9000 online business presentations found in a Kickstarter dataset to determine which characteristics of these proposals led to increased investment. We first utilized lexicon-based measurement and Feature Engineering to determine the relationship between environment and society scores and financial indicators. Next, Logistic Regression is utilized to determine the effect of including environmental and social terms in a project's description on its ability to obtain funding. Contrary to popular belief, our research found that microentrepreneurs were less likely to succeed with proposals that focused on ESG issues. Our research will generate new opportunities for research in the disciplines of information science and crowdfunding by shedding new light on the environment of online micro-entrepreneurship.

In this paper analyzed the impact of innovation of CEOs of small and medium-sized companies, which are rapidly shifting to a digital economy, on corporate performance and how data literacy performs mediating functions. It was confirmed that innovation has a positive effect on corporate performance and that data literacy partially mediates the relationship between innovation and corporate performance. Transformational leadership shows a moderating effect in the relationship between innovation and corporate performance, and transactional leadership showed no moderating effect. Laissez-faire leadership has a moderating effect in the relationship between innovation and data literacy. These results show that innovation is an effective means of improving the organization's management performance, and are expected to awaken the importance of laissez-faire leadership and contribute to the establishment of management strategies.

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.

The phenomenon of climate change is deteriorating which increased heatwaves, typhoons and heavy snowfalls in recent years. Followed by the 25th United nations framework convention on climate change(COP25), the world countries have achieved a consensus on achieving carbon neutrality. City plays a crucial role in achieving carbon mitigation as well as economic development. Considering economic and environmental factors, we selected 63 cities in South Korea to analyze carbon emission situation by Elbow method and K-means clustering algorithm. The results reflected that cities in South Korea can be categorized into 6 clusters, which are technology-intensive cities, light-manufacturing intensive cities, central-innovation intensive cities, heavy-manufacturing intensive cities, service-intensive cities, rural and household-intensive cities. Specific suggestions are provided to improve city-level carbon mitigation development.