• International conference on construction engineering and project management
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• 2022.06a
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• pp.1247-1248
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• 2022

Due to rapid urbanization and population growth, it has become crucial to analyze the various volumes and characteristics of pedestrian pathways to understand the capacity and level of service (LOS) for pathways to promote a better walking environment. Different indicators have been developed to measure pedestrian volume. The pedestrian level of service (PLOS), tailored to analyze pedestrian pathways based on the concept of the LOS in transportation in the Highway Capacity Manual, has been widely used. PLOS is a measurement concept used to assess the quality of pedestrian facilities, from grade A (best condition) to grade F (worst condition), based on the flow rate, average speed, occupied space, and other parameters. Since the original PLOS approach has been criticized for producing idealistic results, several modified versions of PLOS have also been developed. One of these modified versions is perceived PLOS, which measures the LOS for pathways by considering pedestrians' awareness levels. However, this method relies on survey-based measurements, making it difficult to continuously deploy the technique to all the pathways. To measure PLOS more quantitatively and continuously, researchers have adopted computer vision technologies to automatically assess pedestrian flows and PLOS from CCTV videos. However, there are drawbacks even with this method because CCTVs cannot be installed everywhere, e.g., in alleyways. Recently, a technique to monitor bio-signals, such as electrodermal activity (EDA), through wearable sensors that can measure physiological responses to external stimuli (e.g., when another pedestrian passes), has gained popularity. It has the potential to continuously measure perceived PLOS. In their previous experiment, the authors of this study found that there were many significant EDA responses in crowded places when other pedestrians acting as external stimuli passed by. Therefore, we hypothesized that the EDA responses would be significantly higher in places where relatively more dynamic objects pass, i.e., in crowded areas with low PLOS levels (e.g., level F). To this end, the authors conducted an experiment to confirm the validity of EDA in inferring the perceived PLOS. The EDA of the subjects was measured and analyzed while watching both the real-world and virtually created videos with different pedestrian volumes in a laboratory environment. The results showed the possibility of inferring the amount of pedestrian volume on the pathways by measuring the physiological reactions of pedestrians. Through further validation, the research outcome is expected to be used for EDA-based continuous measurement of perceived PLOS at the alley level, which will facilitate modifying the existing walking environments, e.g., constructing pathways with appropriate effective width based on pedestrian volume. Future research will examine the validity of the integrated use of EDA and acceleration signals to increase the accuracy of inferring the perceived PLOS by capturing both physiological and behavioral reactions when walking in a crowded area.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.59-68
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• 2009

This paper aims to explore the factors of learner's flow and to develop and validate a scale to measure the flow in Massive Multiplayer Online Role Playing Game(MMORPG) for education. First of all, potential factors were drawn through literature review. The potential stage comprises 6 factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment) and 16 subfactors. With total 48 items developed. a survey was carried out among 293 elementary learners who had been participating in a commercial MMORPG for English skills to measure their flow in the MMORPG by utilizing the potential scale. Using the responses collected from 288 respondents, exploratory factor analysis, reliability analysis, and confirmatory factor analysis were performed. The expository factor analysis showed that items within each sub-factors could be bound into one factor. That is, the variables evaluating learner's flow were divided into six factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment). And these factors were interpreted consisting of 16 sub-ones. Reliability estimates indicated that the evaluation tool had good internal consistency. The confirmatory factor analysis did confirm the model suggested by the expository factor analysis. Over fit measures(CFI, NFI, NNFI) showed the good suitability of the model. Findings of this study confirmed the validity and reliability of the scale to measure learner's flow in MMORPG.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.134-139
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• 2023

Rehabilitators perform outpatient treatment and daily rehabilitation exercises to recover physical function with the aim of quickly returning to society after surgical treatment. Unlike performing exercises in a hospital with the help of a professional therapist, there are many difficulties in performing rehabilitation exercises by the patient on a daily basis. In this paper, we propose a CNN-LSTM-based upper limb rehabilitation real-time monitoring system so that patients can perform rehabilitation efficiently and with correct posture on a daily basis. The proposed system measures biological signals through shoulder-mounted hardware equipped with EMG and IMU, performs preprocessing and normalization for learning, and uses them as a learning dataset. The implemented model consists of three polling layers of three synthetic stacks for feature detection and two LSTM layers for classification, and we were able to confirm a learning result of 97.44% on the validation data. After that, we conducted a comparative evaluation with the Teachable machine, and as a result of the comparative evaluation, we confirmed that the model was implemented at 93.6% and the Teachable machine at 94.4%, and both models showed similar classification performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.389-399
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• 2023

Recently, there has been a growing focus on the Entrepreneurial Intention of Chinese College Students as a key driver of motivational behavior. However, previous research has provided limited analysis on the actual impact of Social Support on the Entrepreneurial Intention of Chinese College Students. The purpose of this study is to enhance the Entrepreneurial Intention of Chinese College Students and to ascertain the mediating effect of Career Adaptability in the relationship between Social Support and Entrepreneurial Intention. Zhejiang Province, the top-ranked province in private economy in China, possesses a strong economic development momentum and an innovative entrepreneurial atmosphere. Therefore, this study selected 194 third and fourth-year undergraduate students from universities in Zhejiang Province as participants and collected data through a survey utilizing measures of Social Support, Career Adaptability, and Entrepreneurial Intention. The collected data was analyzed for correlations between the measured variables using SPSS 26 and Stata 17 SEM Builder for quantification and validation. The results of the study revealed that, firstly, while Social Support did not have a direct impact on Entrepreneurial Intention, it was found to have an indirect influence on Entrepreneurial Intention through Career Adaptability and its various sub-variables. Secondly, Social Support among College Students was found to have a positive impact on Career Adaptability. Thirdly, Career Adaptability among College Students was found to have a positive impact on Entrepreneurial Intention. Based on these analytical findings, this study provides theoretical and practical implications as well as fundamental information for entrepreneurship education and Career Adaptability at the university level.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of The Korean Association For Science Education
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• v.27 no.3
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• pp.272-284
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• 2007

The purpose of this study was to describe the scales of attitudes toward science and the validation of instruments for students and their parents. These instruments include three scales: cognition about value of science, affection toward science & science learning, and conative participation in scientific activities. A sample of middle school students (N=198) and their parents (N=153) was selected. Data analysis indicated that the instruments developed in this study had proper validity and reliability measures (${\alpha}=0.93$ for student questionnaire, ${\alpha}=0.88$ for parent questionnaire). The results reveal that both students and parents were well aware of the academic/vocational and social value of science, but they had low awareness of the individual value. In spite of that, students have positive feelings regarding enjoyment toward science and science learning, their self-concept and self-efficacy were low. And parents' responses were observed to support their kids in general field but not in science. Especially, female students had low participation in scientific activities and also their parents had low support for scientific activities (p<.0.1). Finally, there were positive correlations between students' attitudes toward science and their parents' affection toward science & science learning and conative participation in science activities.

• Journal of The Korean Association For Science Education
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• v.29 no.1
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• pp.43-53
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• 2009

The teacher's role in education is important. Science education majors must be able to solve problems effectively and pertinently when facing new ones in various situations and complicated human relations in order to become successful science teacher. The purpose of this research is to develop a test that measures the Practical Ability of pre-service science teachers and to apply this to them. The Practical Efficacy Scale for Science Education Majors was also developed in order to be used for validation. In this research, Practical Ability of Science Education Majors consisted of four sub-domains: subject education, business administration, relations and self-development. The result of the correlations between the scores of four sub-domains and the composite score of Practical Ability Test for Preservice Science Teacher(PATPST) is relevant. Subject education and administration business is the highest correlation with PATPSP score specially, and correlation between two areas appeared high. The result of applying PATPSP scores differed according to the grade of science education majors, but not according to their majors. This study's limitation is that the subjects consisted only of female students. However, PATPSP could be a new method that will help science education majors be successful science teachers.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.79-90
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• 2024

This study focused on user trust in ChatGPT, a generative AI technology, and explored the factors that affect usage status and intention to continue using, and whether the influence of trust varies depending on the purpose. For this purpose, the survey was conducted targeting people in their 20s and 30s who use ChatGPT the most. The statistical analysis deploying IBM SPSS 27 and SmartPLS 4.0. A structural equation model was formulated on the foundation of Bhattacherjee's Expectation-Confirmation Model (ECM), employing path analysis and Multi-Group Analysis (MGA) for hypothesis validation. The main findings are as follows: Firstly, ChatGPT is mainly used for specific needs or objectives rather than as a daily tool. The majority of users are cognizant of its hallucination effects; however, this did not hinder its use. Secondly, the hypothesis testing indicated that independent variables such as expectation- confirmation, perceived usefulness, and user satisfaction all exert a positive influence on the dependent variable, the intention for continuance intention. Thirdly, the influence of trust varied depending on the user's purpose in utilizing ChatGPT. trust was significant when ChatGPT is used for information retrieval but not for creative purposes. This study will be used to solve reliability problems in the process of introducing generative AI in society and companies in the future and to establish policies and derive improvement measures for successful employment.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.569-582
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• 2023

In recent years, the service life of major SOC facilities in south korea has exceeded 30 years, and rapid aging is expected within the next 10 years. This has led to a growing recognition of the need for proactive maintenance of these facilities. Consequently, there have been numerous research efforts to introduce smart inspection technologies into maintenance. However, the current system relies primarily on manpower for safety inspections and diagnostics, and on-site surveys rely on visual inspections. Manpower inspections can be time-consuming, and subjective errors may occur during result analysis. In the case of tunnels, there are disadvantages, such as the loss of social overhead capital due to partial closures during inspections. Therefore, institutionalizing smart safety inspections is essential, considering specific measures like using advanced equipment and updating qualifications for experts. Furthermore, it is necessary to verify and validate safety inspection results using advanced equipment before instituting changes. This could be achieved through national-level official research programs and the operation of verification and validation institutions. If smart inspection technology is introduced into maintenance, routine inspections of SOC facilities, such as tunnels, will become feasible. As a result, maintenance technology capable of early detection and proactive response to safety incidents caused by changes in facility conditions is anticipated.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.15-21
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• 2024

The purpose of this study is to propose a framework for the introduction and evaluation of artificial intelligence (AI) services not only in general applications but also in public policies. To achieve this, the study explores AI service management and governance toolkits, providing insights into how to introduce AI services in public policies. Firstly, it offers guidelines on the direction of AI service development and what aspects to avoid. Secondly, in the development phase, it recommends using the AI governance toolkit to review content through checklists at each stage of design, development, and deployment. Thirdly, when operating AI services, it emphasizes the importance of adhering to principles related to 1) planning and design, 2) the lifecycle, 3) model construction and validation, 4) deployment and monitoring, and 5) accountability. The governance perspective of AI services is crucial for mitigating risks associated with service provision, and research in risk management aspects should be conducted. While embracing the advantages of AI, proactive measures should be taken to address limitations and risks. Efforts should be made to efficiently formulate policies using AI technology to create high value and provide meaningful societal impacts.