• Journal of Industrial Convergence
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• v.20 no.8
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• pp.1-13
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• 2022

Although the number of venture start-ups has increased significantly, it is difficult to judge the success or failure based on short-term performance alone. The survival of a company cannot be guaranteed if it does not show sustainable growth prospects. As a growth factor for venture companies, the level of technology commercialization capability and competitive strategies are considered important. Recently, the emergence of innovative business models is creating new opportunities and driving the growth of numerous venture start-ups. This study tried to investigate the mediating effect of business model innovation in the relationship between technology commercialization capability, competitive strategy and the growth prospects of venture companies. For this, empirical analysis was conducted using the original data of the Research on the Precision Status of Venture Firms 2021. As a result, production, manufacturing, marketing capability, cost leadership and product differentiation had a positive(+) effect on growth prospects. The mediating effect of business model innovation between all factors except for manufacturing capacity and growth prospects was verified. This study expanded the scope of research by shedding new light on the factors influencing the long-term growth prospects of venture companies and revealing business model innovation as a new mediating variable. In future research, it is necessary to develop an objective measurement tool and to identify differences according to industrial characteristics.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.131-137
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• 2022

As the age increases, the oral cavity, that is, the teeth and periodontium, also begin to age, and accordingly, a preparation process is required. The preparation process is an important period for oral health management to start continuously with oral health education consisting of knowledge, attitude, and behavior from the 20s. Therefore, to design a clinical dental hygiene course for patients who visited a dental clinic in Gyeonggi-do and received continuous care in an oral health care room after treatment, we tried to analyze the data of the dental hygiene assessment. As a dental hygiene assessment tool, based on personal information and general medical history, dental visit experience, bleeding on probing(BOP), bad breath measurement, phase contrast microscopy, and O'Leary index were performed. The number of subjects who had dental visits was 75.4% and those without experience were 24.6%, and as a result of the periodontal examination, generally bleeding was found in 76.3%. In preventive oral care, the stage of dental hygiene assessment in the 20s is an important first step. From this point on, it is an important time to be systematically habituated so that you can take responsibility for your own oral condition. Therefore, in this study, the results of dental hygiene assessment through oral examinations of subjects in their 20s are derived and presented as basic data for the development of dental hygiene performance competency of dental hygienists during the clinical dental hygiene process in oral health education and oral health management.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.36-42
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• 2023

The development of autonomous vehicles are currently being actively carried out by various companies and research institutes. Expectations for commercialization in daily life as well as specific industries are also rising. Simulators for autonomous vehicles are an essential element in algorithm development and execution considering stability and cost. In this need, various simulators and platforms for simulators are emerging, but research on simulations that reflect various meteorological environmental factors in the real world is still insufficient. This paper proposes a traffic simulation for autonomous vehicles that can consider the weather environment. The weather environment that can be set is largely classified into four categories, and an improved collision prevention algorithm to apply them is presented. Simulation development was conducted through Carla's Python API, a development tool for autonomous driving, and the performance results were compared with existing collision algorithms. Through this, we tried to propose improvements for the development of advanced self-driving vehicle simulations that can reflect various weather environmental factors in real life.

• The Korean Journal of Coaching Psychology
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• v.7 no.1
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• pp.1-31
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• 2023

The purpose of this study is to verify the mediating effect of adaptive cognitive emotion regulation and the moderating effect of supervisor's emotional regulation in the relationship between the emotion recognition and negative feedback acceptance of employees. The data was collected from 273 non-managerial workers in various domestic companies. Confirmatory factor analysis was conducted with AMOS 22 to verify the reliability and validity of the measurement tool, and the mediating and moderating effects were examined using SPSS Process Macro to verify the hypothesis. The results of this study are summarized as follows. First, emotion recognition of employees indirectly affects the acceptance of negative feedback through adaptive cognitive emotional regulation. Second, the effect of emotion recognition on negative feedback acceptance is moderated by supervisor's emotion regulation. Specifically, it was confirmed that when the supervisor's emotional control is low, the relationship between emotional recognition and negative feedback acceptance becomes stronger. Based on the results of the study, it was confirmed that the level of awareness of oneself and others' emotions was psychological process of accepting performance-related feedback, and the importance of supervisor's emotional regulation in positively accepting negative feedback. Finally, based on the research results, the academic significance of this study, implications in coaching practice, limitations, and future research were discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.2
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• pp.104-108
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• 2023

Because of recent expansion of the electric car market. it is highly growing that should be supplemented its performance and safely issue. The EMI problem due to the interlocking of electrical components that causes various safety problems such as fire in electric vehicles is emerging every time. We strive to achieve optimal charging efficiency by combining various technologies and reduce radioactive noise among the EMI noise of a weirless charging control module, one of the important parts of an electric vehicle was designed and tested. In order to analyze the EMI problems occurring in the wireless charging control module, the optimized wireless charging control module by applying the optimization design technology by learning the accumulated test data for critical factors by utilizing the Python-based script function in the Ansys simulation tool. It showed an EMI noise improvement effect of 25 dBu V/m compared to the charge control module. These results not only contribute to the development of a more stable and reliable weirless charging function in electric vehicles, but also increase the usability and efficiency of electric vehicles. This allows electric vehicles to be more usable and efficient, making them an environmentally friendly alternative.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Journal of Christian Education in Korea
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• v.73
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• pp.117-137
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• 2023

This is a case study of an extra-curricular program that designed and implemented maker education based on design thinking to foster a Christian worldview. The program was designed at K university in the course of 10 sessions as following stages; tinkering, providing a special lecture for motivation, finding issues, empathizing, making, sharing and reflecting. A total of 15 students in 5 teams participated in the program, progressed through each stage in the process of solving the problems they found around them so that their neighbors and the creative world could become better. As a result of operating this program, the participants became concerned about their neighbors and community and reflected on the change of perspective of the world from a Christian worldview. As a suggestion for follow-up research and projects, to develop a model of maker education based on design thinking for cultivating a Christian view of the world is proposed in order to support to easy design and management of the program even if there is a lack of professional related knowledge and experience. In addition, it is needed to develop a manual and guide book including a facilitator's role and an assessment tool like a rubric that can give feedback on the performance of the program and make improvement.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.85-92
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• 2023

Recently, interest in artificial intelligence has increased due to the development of artificial intelligence fields such as ChatGPT and self-driving cars. However, there are still many unknown elements in training process of artificial intelligence, so that optimizing the model requires more time and effort than it needs. Therefore, there is a need for a tool or methodology that can analyze the weight changes during the training process of artificial intelligence and help out understatnding those changes. In this research, I propose a visualization system which helps people to understand the accumulated weight changes. The system calculates the weights for each training period to accumulates weight changes and stores accumulated weight changes to plot them in 3D space. This research will allow us to explore different aspect of artificial intelligence learning process, such as understanding how the model get trained and providing us an indicator on which hyperparameters should be changed for better performance. These attempts are expected to explore better in artificial intelligence learning process that is still considered as unknown and contribute to the development and application of artificial intelligence models.

• Steel and Composite Structures
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• v.48 no.2
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• pp.207-233
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• 2023

Steel-concrete composite box girder bridges are widely used in the construction of highway and railway bridges both domestically and abroad due to their advantages of being light weight and having a large spanning ability and very large torsional rigidity. Composite box girder bridges exhibit the effects of shear lag, restrained torsion, distortion and interface bidirectional slip under various loads during operation. As one of the most commonly used calculation tools in bridge engineering analysis, one-dimensional models offer the advantages of high calculation efficiency and strong stability. Currently, research on the one-dimensional model of composite beams mainly focuses on simulating interface longitudinal slip and the shear lag effect. There are relatively few studies on the one-dimensional model which can consider the effects of restrained torsion, distortion and interface transverse slip. Additionally, there are few studies on vehicle-bridge integrated systems where a one-dimensional model is used as a tool that only considers the calculations of natural frequency, mode and moving load conditions to study the dynamic response of composite beams. Some scholars have established a dynamic analysis model of a coupled composite beam bridge-train system, but where the composite beam is only simulated using a Euler beam or Timoshenko beam. As a result, it is impossible to comprehensively consider multiple complex force effects, such as shear lag, restrained torsion, distortion and interface bidirectional slip of composite beams. In this paper, a 27 DOF vehicle rigid body model is used to simulate train operation. A two-node 26 DOF finite beam element with composed box beams considering the effects of shear lag, restrained torsion, distortion and interface bidirectional slip is proposed. The dynamic analysis model of the coupled composite box girder bridge-train system is constructed based on the wheel-rail contact relationship of vertical close-fitting and lateral linear creeping slip. Furthermore, the accuracy of the dynamic analysis model is verified via the measured dynamic response data of a practical composite box girder bridge. Finally, the dynamic analysis model is applied in order to study the influence of various mechanical effects on the dynamic performance of the vehicle-bridge system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.273-275
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• 2022

The use of drugs by pregnant women poses a potential risk to the fetus. Therefore, it is essential to classify drugs that pregnant women should prohibit. However, the fetal toxicity of most drugs has not been identified. This takes a lot of time and cost. In silico approaches, such as virtual screening, can identify compounds that may present a high risk to the fetus for a wide range of compounds at the low cost and time. We collected class information of each drug from the hazard classification lists for prescribing drugs in pregnancy by the government of Korea and Australia. Using the structural and chemical features of each drug, various machine learning models were constructed to predict fetal toxicity of drugs. For all models, the quantitative performance evaluation was performed. Based on the attention algorithm, important molecular substructures of compounds were identified in the process of predicting the fetal toxicity of the drug by the proposed model. From the results, we confirmed that drugs with a high risk of fetal toxicity can be predicted for a wide range of compounds by machine learning. This study can be used as a pre-screening tool for fetal toxicity predictions, as it provides key molecular substructures associated with the fetal toxicity of compounds.