• Journal of Digital Convergence
• /
• v.19 no.4
• /
• pp.25-31
• /
• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Practical Engineering Education
• /
• v.11 no.1
• /
• pp.1-8
• /
• 2019

Virtual Training is an educational exercise in which the environment or the situation is virtually implemented for specific training and proceed like a real situation. In recent years, the virtual reality technology has developed rapidly, and the demand for experiencing situation that are not directly experienced in the real world is increasing more and more in virtual reality. Particularly, there is an increasing demand of contents for hands-on training and virtual training for equipment training that replaces high-risk and high-cost industry training. The virtual training contents have been developed and utilized for the purpose of technical training. However, it is known that virtual training is more effective when it is used as a supplementary training material or combined with e-learning contents rather than replacing one training course with virtual training contents because purpose and effect are different from general technical training course. In this study, we explored the development method for effective utilization of electrohydraulic servo control process, which is the virtual reality contents developed in 2017 in combination with e-learning contents. In addition, in order to establish a teaching and learning strategy, we actually develop and operate a case studies using virtual training contents. Surveys and case studies are conducted to investigate the effects of teaching and learning strategies applied in the classroom on students and their educational usefulness.

• Journal of Industrial Convergence
• /
• v.17 no.3
• /
• pp.39-49
• /
• 2019

This study, as a study on building platforms for smart campus ecosystem, took an approach that reflected the needs of various stakeholders of smart campus, and focused on functions to help them strengthen their competitiveness and advance into society by focusing on the learning of the most important university student users, college life, and social connection. First, we looked at the theories related to smart campus construction through prior research, and next, through domestic and international environmental analysis and trend analysis, we designed and presented a target model for e-portfolio focusing on core competency development and support system for Industry-Academic Cooperation, and proposed the main point for continuous smart campus development model.

• Information Systems Review
• /
• v.19 no.2
• /
• pp.1-19
• /
• 2017

Original scripts of e-learning lectures for the CEOs of corporation S were analyzed using topic analysis, which is a text mining method. Twenty-two topics were extracted based on the keywords chosen from five-year records that ranged from 2011 to 2015. Research analysis was then conducted on various issues. Promising topics were selected through evaluation and element analysis of the members of each topic. In management and economics, members demonstrated high satisfaction and interest toward topics in marketing strategy, human resource management, and communication. Philosophy, history of war, and history demonstrated high interest and satisfaction in the field of humanities, whereas mind health showed high interest and satisfaction in the field of in lifestyle. Studies were also conducted to identify topics on the proportion of content, but these studies failed to increase member satisfaction. In the field of IT, educational content responds sensitively to change of the times, but it may not increase the interest and satisfaction of members. The present study found that content production for CEOs should draw out deep implications for value innovation through technology application instead of simply ending the technical aspect of information delivery. Previous studies classified contents superficially based on the name of content program when analyzing the status of content operation. However, text mining can derive deep content and subject classification based on the contents of unstructured data script. This approach can examine current shortages and necessary fields if the service contents of the themes are displayed by year. This study was based on data obtained from influential e-learning companies in Korea. Obtaining practical results was difficult because data were not acquired from portal sites or social networking service. The content of e-learning trends of CEOs were analyzed. Data analysis was also conducted on the intellectual interests of CEOs in each field.

• Journal of The Korean Association For Science Education
• /
• v.19 no.1
• /
• pp.29-40
• /
• 1999

The purpose of the present study was to test hypothesis that, because it uses tri-dimensional sensory pathway which have been showed a higher rate of neural activities than uni- or bi-dimensional's, lab-activity-based instruction is more effective teaching strategy in learning science than verbal-based instruction. In the present study, manipulative teaching strategy that uses visual, somatosensory and auditory information pathway was regarded as a mode of tri-dimensional sensory inputs. In addition, verbal teaching strategy that uses mainly auditory and a little visual information pathway was used as a mode of bi-dimensional sensory inputs. Fifty-six students who failed to successfully solve two proportional reasoning tasks (i.e., pouring water tasks) were sampled for this research from a junior high school. The subjects were randomly divided into a manipulative or a verbal teaching group, and given manipulative or verbal tutoring on the use of proportional reasoning strategies and a test of proportional reasoning during instruction. The results showed that manipulative group's performance on the test of proportional reasoning during instruction showed significantly higher performance than verbal group's (t=2.45, p<0.02). The present study also discussed some educational implications of the results.

• Smart Structures and Systems
• /
• v.22 no.4
• /
• pp.413-424
• /
• 2018

The factors affecting the shear strength of the angle shear connectors in the steel-concrete composite beams can play an important role to estimate the efficacy of a composite beam. Therefore, the current study has aimed to verify the output of shear capacity of angle shear connector according to the input provided by Support Vector Machine (SVM) coupled with Firefly Algorithm (FFA). SVM parameters have been optimized through the use of FFA, while genetic programming (GP) and artificial neural networks (ANN) have been applied to estimate and predict the SVM-FFA models' results. Following these results, GP and ANN have been applied to develop the prediction accuracy and generalization capability of SVM-FFA. Therefore, SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors. According to the results, the Firefly algorithm has produced a generalized performance and be learnt faster than the conventional learning algorithms.

• The Journal of Society for e-Business Studies
• /
• v.22 no.3
• /
• pp.57-74
• /
• 2017

The Electronic Shelf Label (ESL) is an alternative to the paper price label attached to merchandise shelves and is attracting attention as a retail IoT infrastructure that will lead the innovation of offline retail outlets. In general, when introducing a substitute product, the company tends to consider the financial factors such as the efficiency of the investment cost compared to the existing product or the reduction of the operating cost. However, considering only financial factors in the decision-making process, it may not properly reflect the various values associated with corporate strategy and the requirements of stakeholders. In this study, 8 evaluation items (Investment Cost, Operating Cost, Quality Level, Customer Management, Job Efficiency, Maintenance, Functional Expandability, and Store Image) based on BSC's 4 perspectives (Financial, Customer, Internal Business Process, Learning & Growth), and using AHP (Analytic Hierarchy Process) to measure the priorities of evaluation items for domestic small supermarket employees. As a result of the research, priority was given in order of Customer, Learning & Growth, Internal Business Process, and Financial aspects among the evaluation items for adopting the price label, and the electronic price label was supported with higher importance than the paper price label. In contrast to the priorities of the financial aspects of most prior studies, the items of Learning & growth and customer perspectives have relatively high priorities. In particular, respondents classified by job group, The priorities of the 8 evaluation items were different among the groups. These results are expected to provide implications for both companies (retail outlets) and ESL providers (manufacturers and service providers) who are considering the introduction of ESL.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.21 no.3
• /
• pp.426-439
• /
• 2015

Purpose: The purpose of this study was to identify the core competencies of nursing students and to improve competency-based nursing education. Methods: A triangulation method was used. A literature review and focus group interviews (FGI) were used to identify nursing core competencies. The present level of nursing competencies of students was identified through the nursing core performance questionnaire. Results: 1) Nursing core competencies, including 23 different competencies, were categorized into seven areas through a literature review and qualitative research. These competencies included: desirable personality, attitude & interpersonal skills, professionalism, nursing knowledge & basic nursing skills, ability to cope with nursing situations, basic ability in nursing research, coping ability with changes in the healthcare environment, and leadership. 2) Core nursing skills, nursing research, and nursing leadership were the three lowest ranking competencies. Some courses in the curriculum were to be newly established in an e-learning system, student's portfolio in non-curriculum. Conclusions: Further research is needed in order to show effects of the changes. Changes after applying the strategy of a nursing education program will be measured. Continuous research in competency-based nursing education is needed.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12
• /
• pp.269-279
• /
• 2021

In the current conditions of world socio-economic development, the strategic support of the process of managing the development of universities has become a particularly important area. Strategic management requires reliable information and analytical support in the form of sound descriptions of strategic directions of development, assumptions, and forecasts. The purpose of the study is to substantiate and elaborate the crucial causes in the strategic management of university's development and to suggest the coherent prospects for advancements. The data analysis was performed using descriptive methods to identify the most significant causes that affect the university's strategic development; the expert assessment was used to rank the factors, ultimately to assess each factor that affects to some extent the university's strategic development; the abstract-logical method was used to ground the positive impact of computer technologies and e-learning on the strategic development of a university and to formulate proposals for its further progress. The main results provided in the given paper showed that significant and most important strategic cause of university's development lies in the field of improving the quality of education, expanding access to educational services based on computer technology and its functionality. In turn, its widespread use at all stages of the educational process allows providing a number of advancements for universities in strategic prospects.