• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1173-1178
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• 2023

In modern society, there is a tendency to emphasize efficiency and lead to detailed planning of team projects and individual tasks within organizations. In a 24-hour routine, the ability to use time effectively is considered an indicator of conscientiousness, and people try to imitate planned and organized people and utilize various systems to manage their daily lives. The reason why you want to perform a given task efficiently is because it affects the success or failure of the project by completing the task within a set period of time. Neglecting the project schedule is considered a major risk that threatens a successful outcome. This applies not only to large-scale organizational projects but also to individual life, and utilizes a variety of schedule management tools that emphasize time-based efficient management. In project management, it is necessary to carefully understand the detailed work progress rather than simply based on Today. In this paper, we propose an Android application that can manage schedules by accessing the user's project in units of tasks rather than dates, and introduce its implementation. The application we implemented in this paper can manage the project's goals and schedule by registering the project to which the user belongs. In addition, it provides the ability to approach work efficiently by visualizing the progress of the entire project or individual goals. As a result, users can use the application we propose in this paper to focus on their projects and manage schedules by task, thereby improving the overall efficiency of the project.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.21-30
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• 2024

As the self-driving car market continues to grow, the need for charging infrastructure is growing. However, in the case of a wireless charging system, stability issues are being raised because it requires a large amount of power compared with conventional wired charging. SAE J2954 is a standard for building autonomous vehicle wireless charging infrastructure, and the standard defines a communication method between a vehicle and a power transmission system. SAE J2954 recommends using physical media such as Wi-Fi, Bluetooth, and UWB as a wireless charging communication method for autonomous vehicles to enable communication between the vehicle and the charging pad. In particular, UWB is a suitable solution for indoor and outdoor charging environments because it exhibits robust communication capabilities in indoor environments and is not sensitive to interference. In this standard, the process for building a wireless power transmission system is divided into several stages from the start to the completion of charging. In this study, UWB technology is used as a means of fine alignment, a process in the wireless power transmission system. To determine the applicability to an actual autonomous vehicle wireless power transmission system, experiments were conducted based on distance, and the distance information was collected from UWB. To improve the accuracy of the distance data obtained from UWB, we propose a Single Model and Multi Model that apply machine learning and deep learning techniques to the collected data through a three-step preprocessing process.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.185-200
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• 2024

This study explored the changes in the elementary science teaching expertise of teachers who participated in an in-school professional learning community for elementary science instructional research. Six elementary school teachers from grades 4, 5, and 6 at an 18-class S elementary school in a medium-sized city in Chungcheongbuk-do conducted collaborative instructional research on elementary science lessons as part of an in-school professional learning community, which was held 26 times over 7 months in 2020. During the professional learning community, video and audio recordings of the activities, research lessons, course materials, and professional learning community reflection activities were collected for analysis. The collected data were analyzed using qualitative research methods; data processing, reading, note-taking, description, classification, interpretation, reporting, and visualization; and the instructional professionalism elements were extracted based on the instructional professionalism framework. In the early professional learning community activity stages, the participating teachers first discussed their teaching perspectives, their experiences, and their goals for teaching science, which resulted in a selection of research questions. The teachers then collaboratively designed and implemented research lessons for each grade level, after which lesson reflections were conducted. The teachers' abilities to engage in qualitative reflection on the research questions improved after each reflection iteration. It was found that this professional learning community collaborative lesson study experience positively contributed to teaching expertise development. Based on the study findings, the implications for using professional learning communities to improve elementary teachers' science teaching expertise are given.

• Information Systems Review
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• v.24 no.4
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• pp.23-40
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• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.43-52
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• 2024

In this study, we evaluated the seismic performance of a masonry building that was not designed to be earthquake-resistant and attempted to improve the seismic performance by adopting a seismic reinforcement method on the exterior of the building. In addition, the building seismic design standards and commentary(KDS 41 17 00:2019) and existing facility(building) seismic performance evaluation methods were applied to evaluate seismic performance, and a pushover analysis was performed using non-linear static analysis. As the result of this study, it was determined that seismic reinforcement was urgent because the distribution rate of earthquake-resistant design of houses in Korea was low and masonry structures accounted for a large proportion of houses. When reinforcing the steel beam-column+brace frame in a masonry building, the story drift angle was 0.043% in the X direction and 0.047% in the Y direction, indicating that it satisfied the regulations. The gravity load resistance capacity by performance level was judged to be a safe building because it was habitable in both X and Y directions. In conclusion, it is believed that the livability and convenience of the house can be secured by reinforcing the exterior of the building and the seismic performance and behavior of the structure can be clearly predicted.

• Journal of Bio-Environment Control
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• v.24 no.2
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• pp.51-56
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• 2015

The objective of this study was to evaluate the effect of silicate fertilizer on growth, physiology and abiotic stress tolerance of Chinese cabbage seedlings. Five silicate concentrations (8, 16, 32, 64, and 128mM) and control (non-treatment) were applied to Chinese cabbage seedlings twice a week. Three weeks after application of silicate treatment, seedlings were used for treating abiotic stresses and were assessed for growth and physiological characteristics. Growth parameters significantly increased in 8, 16, and 32mM treatments except 64 and 128mM. Total root surface area, total root length, and number of root tips increased in 8, 16 and 32mM treatments, but they decreased in treated seedlings with 64 and 128mM of silicate. The highest growth parameters and root morphology were observed in 8mM treatment. As for the effect on the seedling physiology, transpiration rates decreased while stomatal diffusive resistance increased to increasing silicate concentration. The application of silicate reduced the electrical conductivity, heating and chilling injury index at high and low temperatures. Silicate enhanced drought tolerance of Chinese seedlings by delaying the starting time of wilting point. The starting time of wilting point in the control was 3 days after discontinuation of irrigation, while in the 8, 64 and 128mM of silicate treatments were 4 days, and the 16 and 32mM treatments were 5 days. All plants were wilted after 5 days in control without irrigation whereas it showed in 8mM treatment after 6 days, in 16, 32, 64, 128mM treatments after 7 days.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.417-428
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• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.

• Journal of the Korean Chemical Society
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• v.60 no.3
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• pp.203-214
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• 2016

The purpose of this study was to investigate brain activation pattern and functional connectivity during convergence thinking based creative problem solving and chemistry problem solving to identify characteristic convergence thinking that is backbone of creative problem solving using functional magnetic resonance imaging(fMRI). A fMRI paradaigm inducing convergence thinking and chemistry problem solving was developed and adjusted on 17 highschool students, and brain activation image during task was analyzed. According to the results, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, medial frontal gyrus, cingulate gyrus, precuneus and caudate nucleus body in left hemisphere and cuneus and caudate nucleus body in right hemisphere were significantly activated during convergence thinking. The other hand, middle frontal gyrus, medial frontal gyrus and caudate nucleus in left hemisphere and middle frontal gyrus, lingual gyrus, caudate nucleus, thalamus and culmen of cerebellum in right hemisphere were significantly activated during chemistry problem solving. As results of analysis functional connectivity, all of areas activated during convergence thinking were functionaly connected, whereas scanty connectivity of chemistry problem solving between right middle frontal gyrus, bilateral nucleus caudate tail and culmen. The results show that logical thinking, working memory, planning, imaging, languge based thinking and learning motivation were induced during convergence thinking and these functions and regions were synchronized intimately. Whereas, logical thinking and inducing learning motivation functioning during chemistry problem solving were not synchronized. These results provide concrete information about convergence thinking.

• Journal of Christian Education in Korea
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• v.72
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• pp.245-271
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• 2022

The purpose of this study is to develop a nursing clinical practice education model that applies case-based learning and co-teaching to nursing students, and to secure the validity of the developed model. To verify the validity of the nursing clinical practice education model, it was applied to the subject of 'Health Response and Nursing VI (Perception/ Cognition) Practice' in the 2nd semester of 2021 at J University in Jeonju, and the instructor's response to the model was evaluated. Surveys and focus group interviews were conducted on confidence in clinical practice and teaching and learning models. After deriving the case-based learning stage and co-teaching elements through a review of precedent literature and case studies, an initial model was devised after expert review, and the devised model was reviewed for internal validity by nursing education experts, and then modified and supplemented. As a result of the learner response evaluation conducted after applying the model to the clinical practice subject for external validation verification, the confidence in clinical performance was 4.22 points and the satisfaction with the teaching-learning model was 4.68 points. Summarizing the results of the focus group interview, the importance of prior learning and the learning of selected cases based on actual cases, learning terminology and professional knowledge, eliminated fear of the practice field, felt familiar, and learned various cases. He said that he was able to think critically through the time to organize the knowledge learned in the practice field. In addition, through co-teaching, it was found that field leaders and advisors taught the theoretical and practical aspects at the same time through examples, thereby experiencing practical education closer to practice. It is expected that the nursing clinical practice education model developed through this study, applying case-based learning and co-teaching, will be an effective teaching and learning model that can reduce the gap between theory and practice and improve the clinical performance of nursing students.