• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.738-750
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• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.55-65
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• 2023

The purpose of this study is to explore the teaching experience of first-time youth soccer leaders. To this end, four leaders registered in the U-12 team were selected from those with more than 10 years of player experience, less than 5 years of coaching experience, and a level C or higher of the Korea Football Association leader's license. Accordingly, the analysis categories and analysis units were categorized according to the research problem, and data analysis was conducted through an inductive method. As a result of the study, youth soccer leaders were starting their coaching with the mindset of "I shouldn't" and "I can do it" based on their past experiences. They who concerned their uncertainty about the future in the teaching field were struggling with how to communicate with student and were less professional in teaching and learning ability. but they were trying to gain expertise while feeling rewarded in teaching. Accordingly, it was discussed to improve the treatment of youth soccer leaders and improve the program that is the leader training system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.141-156
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• 2023

The advancement of Information and Communication Technology (ICT), along with the expansion of government and private investment in startup discovery and funding, has led to the emergence of startups seeking to generate outstanding results based on innovative ideas. As successful startups serve as role models, the number of aspiring entrepreneurs preparing to launch their own startups continues to increase. However, unlike entrepreneurs who challenge themselves with serial entrepreneurship after experiencing success, early-stage startups face various challenges such as team building, technology development, and fundraising. Accelerators play a dual role of mentor and investor by providing education, mentoring, consulting, network connection, and initial investment activities to help startups overcome various challenges they face and facilitate their growth. This study investigated whether there is a correlation between the characteristics of startups and their entrepreneurial performance, and analyzed whether accelerators mediate the relationship between startup characteristics and entrepreneurial performance. A total of 11 hypotheses were proposed, and a survey was conducted on 302 startup founders and employees located across the country, including the metropolitan area, for empirical research. SPSS 23.0 and Amos 23.0 were used for statistical analysis. Through this study, it was found that factors such as innovation, organizational culture, financial characteristics, and learning orientation among the characteristics of startups, rather than having a direct impact on entrepreneurial performance, are linked to entrepreneurial performance through the role of accelerators. By analyzing the impact factors of startup characteristics on entrepreneurial performance, this study presents research on the role of accelerators and provides institutional improvements. It is expected to contribute to the expansion of investment and differentiated acceleration programs, enabling startups to seize the market and grow stably in the market.

• Journal of Gifted/Talented Education
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• v.24 no.3
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• pp.421-444
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• 2014

In STEAM education, contents that has been extracted from a variety of areas, so it can work closely and systematically. Therefore STEAM education requires systems thinking that can be grasped effectively these different disciplines. The purposes of this study are to develop a STEAM program based on systems thinking, and apply the program to the secondary science gifted student in order to investigate the educational effect. A model of the Program developed from previous research and theoretical contents of systems thinking and STEAM. A draft of the STEAM program was developed on the theme of "rocket". A total of 113 students was participated in this study. 100 seventh and 13 eighth graders were enrolled at seigy. A single group pre-post test paired t-test was conducted on them in systems thinking skills. Result of applying the program to the students as follows. The systems thinking ability was improved after the application of the program. 'Mental Model', 'Personal Skill', 'Team Learning', and 'System Analysis', 'Shared Vision' emerged for both improved significantly. In conclusion, the STEAM program based on system thinking improves students' systems thinking skills. This program of results can be helpful in cultivate human resources with the problem solving ability based on system thinking and STEAM literacy by used in public education curriculum.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.639-652
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• 2021

In accordance with the rapidly changing social environment, it is becoming more important to cultivate creative and convergent practical talents with flexible thinking skills and problem-solving skills. Therefore, it is necessary for universities to provide educational experiences that enable students to cooperate and converge multidisciplinaryly to carry out on-the-job projects based on what they have learned at school. Therefore, this study designed, developed, and operated with the aim of cultivating creative talents with integrated problem-solving ability through a multidisciplinary capstone design curriculum based on industry-academia cooperation. To this end, the curriculum was developed together by recruiting participating companies and forming a convergence professor team, and it was operated for 15 weeks for students majoring in cosmetics engineering at D University. After the education was over, learning satisfaction and perceived academic achievement were surveyed, and as a result of the analysis, it was found to be above average with 3.77 points and 3.86 points, respectively. And as a result of the in-depth interview on the participation experience, five themes related to the positive experience and three themes related to the negative experience were derived. This study will be able to provide basic data when operating a multidisciplinary convergence capstone design curriculum based on industry-academia cooperation in the future.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.49-61
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• 2009

This article provides training exercises for executives into interpreting subroutine maps of executives' thinking in processing business and industrial marketing problems and opportunities. This study builds on premises that Schank proposes about learning and teaching including (1) learning occurs by experiencing and the best instruction offers learners opportunities to distill their knowledge and skills from interactive stories in the form of goal.based scenarios, team projects, and understanding stories from experts. Also, (2) telling does not lead to learning because learning requires action-training environments should emphasize active engagement with stories, cases, and projects. Each training case study includes executive exposure to decision system analysis (DSA). The training case requires the executive to write a "Briefing Report" of a DSA map. Instructions to the executive trainee in writing the briefing report include coverage in the briefing report of (1) details of the essence of the DSA map and (2) a statement of warnings and opportunities that the executive map reader interprets within the DSA map. The length maximum for a briefing report is 500 words-an arbitrary rule that works well in executive training programs. Following this introduction, section two of the article briefly summarizes relevant literature on how humans think within contexts in response to problems and opportunities. Section three illustrates the creation and interpreting of DSA maps using a training exercise in pricing a chemical product to different OEM (original equipment manufacturer) customers. Section four presents a training exercise in pricing decisions by a petroleum manufacturing firm. Section five presents a training exercise in marketing strategies by an office furniture distributer along with buying strategies by business customers. Each of the three training exercises is based on research into information processing and decision making of executives operating in marketing contexts. Section six concludes the article with suggestions for use of this training case and for developing additional training cases for honing executives' decision-making skills. Todd and Gigerenzer propose that humans use simple heuristics because they enable adaptive behavior by exploiting the structure of information in natural decision environments. "Simplicity is a virtue, rather than a curse". Bounded rationality theorists emphasize the centrality of Simon's proposition, "Human rational behavior is shaped by a scissors whose blades are the structure of the task environments and the computational capabilities of the actor". Gigerenzer's view is relevant to Simon's environmental blade and to the environmental structures in the three cases in this article, "The term environment, here, does not refer to a description of the total physical and biological environment, but only to that part important to an organism, given its needs and goals." The present article directs attention to research that combines reports on the structure of task environments with the use of adaptive toolbox heuristics of actors. The DSA mapping approach here concerns the match between strategy and an environment-the development and understanding of ecological rationality theory. Aspiration adaptation theory is central to this approach. Aspiration adaptation theory models decision making as a multi-goal problem without aggregation of the goals into a complete preference order over all decision alternatives. The three case studies in this article permit the learner to apply propositions in aspiration level rules in reaching a decision. Aspiration adaptation takes the form of a sequence of adjustment steps. An adjustment step shifts the current aspiration level to a neighboring point on an aspiration grid by a change in only one goal variable. An upward adjustment step is an increase and a downward adjustment step is a decrease of a goal variable. Creating and using aspiration adaptation levels is integral to bounded rationality theory. The present article increases understanding and expertise of both aspiration adaptation and bounded rationality theories by providing learner experiences and practice in using propositions in both theories. Practice in ranking CTSs and writing TOP gists from DSA maps serves to clarify and deepen Selten's view, "Clearly, aspiration adaptation must enter the picture as an integrated part of the search for a solution." The body of "direct research" by Mintzberg, Gladwin's ethnographic decision tree modeling, and Huff's work on mapping strategic thought are suggestions on where to look for research that considers both the structure of the environment and the computational capabilities of the actors making decisions in these environments. Such research on bounded rationality permits both further development of theory in how and why decisions are made in real life and the development of learning exercises in the use of heuristics occurring in natural environments. The exercises in the present article encourage learning skills and principles of using fast and frugal heuristics in contexts of their intended use. The exercises respond to Schank's wisdom, "In a deep sense, education isn't about knowledge or getting students to know what has happened. It is about getting them to feel what has happened. This is not easy to do. Education, as it is in schools today, is emotionless. This is a huge problem." The three cases and accompanying set of exercise questions adhere to Schank's view, "Processes are best taught by actually engaging in them, which can often mean, for mental processing, active discussion."

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.