• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Journal of Trauma and Injury
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• v.36 no.1
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• pp.3-7
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• 2023

Purpose: As resuscitative endovascular balloon occlusion of the aorta (REBOA) is performed in an extremely emergent situation, achieving competent clinical practice is mandatory. Although there are several educational courses that teach the REBOA procedure, there have been no reports evaluating the impact of training on clinical practice. Therefore, this study is aimed to evaluate the effects of the course on procedural performance during resuscitation and on clinical outcomes. Methods: Patients who were managed at a regional trauma center in Dankook University Hospital from August 2016 to February 2018 were included and were grouped as precourse (August 2016-August 2017, n=9) and postcourse (September 2017- February 2018, n=9). Variables regarding injury, parameters regarding REBOA procedure, morbidity, and mortality were prospectively collected and reviewed for comparison between the groups. Results: Demographics and REBOA variables did not differ between groups. The time required from arterial puncture to balloon inflation was significantly shortened from 9.0 to 5.0 minutes (P=0.003). There were no complications associated with REBOA after the course. Mortality did not show any statistical difference before and after the course. Conclusions: The endovascular training for REBOA pilot course, which uses a modified form of flipped learning, realistic simulation of ultrasound-guided catheter insertion and balloon manipulation, and competence assessment, significantly improved procedural performance during resuscitation of trauma patients.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.384-384
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• 2022

과거의 우리나라 대설피해 양상을 살펴보면 지역적으로 집중되어 피해가 발생하는 것이 특징이다. 그러나 현재는 전국적으로 대설피해가 가중되는 추세이며, 이에 따라 대설피해에 대비 가능한 대책의 강구가 필요한 실정이다. 그러나 피해 발생 시 정확한 피해 예측으로 사전에 재난을 대비가 가능한 수준의 연구는 미흡한 실정이다. 따라서 본 연구에서는 다양한 통계기법과 기계학습 기법을 이용하여 대설로 인해 발생한 피해액을 개략적으로 예측이 가능한 모형을 개발하고자 하였다. 대설피해액 예측 모형은 다중회귀분석, 서포트 벡터 머신, 인공신경망 기법, 랜덤포레스트 기법을 이용하여 총 4가지 기법으로 개발하였으며, 독립변수로 사회·경제적 요소, 기상요소를 사용하였고, 종속변수로는 1994년부터 2020년까지 발생한 대설피해 이력의 대설피해액을 사용하였다. 결과적으로 4가지 예측 모형의 예측력 검증 및 기법 간의 예측력을 비교하여 개발한 모형의 적용성을 검토하였다. 본 연구 결과에서 제시한 모형의 개선방안 및 업데이트 방안을 참고하여 후속 연구가 진행된다면 미래에 전국적으로 확대될 대설피해에 대한 대비가 가능할 것으로 기대되며 복구비 및 예방비 투자의 지역적 우선순위를 분석하여 선제적인 대비가 가능할 것으로 판단된다.

• Fourth Industrial Review
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• v.4 no.1
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• pp.1-9
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• 2024

Purpose: Education is the part of a fundamental human right across the world. In recent years, the trend of virtual education has increased tremendously. The paper aims to find the impact of adoption, accessibility, interactions, knowledge, and satisfaction on the success of transformation towards virtual education. Research design, data and methodology: Primary data has been gathered through the use of responses from students taking admission in virtual higher education to standardized questionnaires. Of the 250, only 122 were considered complete and have been used in further studies. Convinced random sampling method has been used. The results were evaluated using the Likert Five-Point Scale. For applying these statistical tools software SmartPLS and SPSS 19 have been used. The fitness of the model has been re-checked through an Artificial Neural Network (ANN). Result: Results derived that adoption, accessibility, and interactions have a significant impact on knowledge, knowledge influences satisfaction level and satisfaction have a meaningful impact on the success of transformation towards virtual education. Conclusion: It can be concluded that virtual education has the potential to change the future of the education system and its potential in India. The highest importance is due to satisfaction (100%), adoption (98.7%), knowledge (91.4%), accessibility (62%), and interaction (29.2%).

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Journal of Pharmacopuncture
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• v.27 no.2
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• pp.142-153
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• 2024

Objectives: This study aimed to analyze the educational needs of interns and residents in Korean medicine as the first step in developing an education program to improve their research competencies. Methods: A mixed-method design, incorporating both quantitative and qualitative data collection methods, was used to investigate the educational needs for research competencies among interns and residents working in Korean medicine hospitals nationwide. Data were collected through online surveys and online focus group discussions (FGDs), and processed using descriptive statistical analysis and thematic analysis. The study results were derived by integrating survey data and FGD outcomes. Results: In total, 209 interns and residents participated in the survey, and 11 individuals participated in two rounds of FGDs. The majority of participants felt a lack of systematic education in research and academic writing in postgraduate medical education and highlighted the need for nationally accessible education due to significant disparities in the educational environment across hospitals and specialties. The primary barrier to learning research and academic writing identified by learners was the lack of knowledge, leading to time constraints. Improving learners' research competencies, relationship building, autonomy, and motivation through a support system was deemed crucial. The study also identified diverse learner types and preferred educational topics, indicating a demand for learner-centered education and coaching. Conclusion: This study provides foundational data for designing and developing a program on education on research competencies for interns and residents in Korean medicine and suggests the need for initiatives to strengthen these competencies.

• Journal of Wind Energy
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• v.14 no.3
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• pp.25-33
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• 2023

In order to secure the safety of increasing offshore activities such as offshore wind farm maintenance and fishing, IMPACT, a mid-term marine weather forecasting system, was established by predicting marine weather up to 7 days in advance. Forecast data from the Korea Hydrographic and Oceanographic Agency (KHOA), which provides the most reliable marine meteorological service in Korea, was used, but wind speed and wave height forecast errors increased as the leading forecast period increased, so improvement of the accuracy of the model results was needed. The Model Output Statistics (MOS) method, a post-correction method using statistical machine learning, was applied to improve the prediction accuracy of wave height, which is an important factor in forecasting the risk of marine activities. Compared with the observed data, the wave height prediction results by the model before correction for 6 to 7 days ahead showed an RMSE of 0.692 m and R of 0.591, and there was a tendency to underestimate high waves. After correction with the MOS technique, RMSE was 0.554 m and R was 0.732, confirming that accuracy was significantly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1301-1316
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• 2024

In modern warfare, missiles play a pivotal role but typically spend the majority of their lifecycle in long-term storage or standby mode, making it difficult to detect failures. Preemptive detection of missiles that will fail is crucial to preventing severe consequences, including safety hazards and mission failures. This study proposes a contamination-based stacking ensemble model, employing the local outlier factor (LOF), to detect such missiles. The proposed model creates multiple base LOF models with different contamination values and combines their anomaly scores to achieve a robust anomaly detection. A comparative performance analysis was conducted between the proposed model and the traditional single LOF model, using production-related inspection data from missiles deployed in the military. The experimental results showed that, with the contamination parameter set to 0.1, the proposed model exhibited an increase of approximately 22 percentage points in accuracy and 71 percentage points in F1-score compared to the single LOF model. This approach enables the preemptive identification of potential failures, undetectable through traditional statistical quality control methods. Consequently, it contributes to lower missile failure rates in real battlefield scenarios, leading to significant time and cost savings in the military industry.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.97-109
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• 2016

In Korea, the survival rate of start-up of 5-year after foundation is as low as 29.6% of the country. This low survival rate is from because of insufficient resources in start-ups compared to those of mid-sized companies. Therefore, the marketing characteristics of entrepreneurship has emerged as a major cause. Therefore, In this study, because learning orientation, marketing experience, competition orientation and etc are differently owned in start-ups, marketing impact to marketing strategy in start-up companies are differently investigated. Therefore, the relationship of learning orientation, marketing experience, competition Orientation with marketing strategies was examined. Based on this, Business performance was examined to suggest contents related to eco-system of start-up companies to representative of start-up companies. For this study, Survey was conducted for 250 start-up entrepreneurs within 3 and half year since foundation from Nov. 20 to Dec. 20, 2015. In result of data-cleaning, 207 meaningful samples were gathered. Based on these, conclusion was obtained. Using SPSS 20.0 statistical program, frequency analysis, reliability analysis, correlation analysis and regression analysis were conducted. the following conclusions were drawn. First, in the impact of marketing environment of Phase 1 start-up companies on marketing strategy, product strategy, distribution strategy and promotion strategy were positively affected by learning orientation, marketing experience and competition orientation. Second, in the effect of 2nd phase marketing strategy to business performance, the financial performance and the non-financial performance. Were positively affected by product strategy, distribution strategy and promotion strategies. Third, The effect of learning orientation, marketing experience and competition orientation to financial performance was positively mediated by product strategy and distribution strategy among 3rd phase meditation strategies. the effect of learning orientation, marketing experience and competition orientation to non-financial performance was positively mediated by products strategy. In comprehensive summary, in order to increase business performance in start-up companies, marketing strategy should be applied in. Especially, the role of learning orientation and marketing experience is vital. In increasement of business performance to characteristics of star up marketing, financial performance can be increased by product strategy and distribution strategy. And, both of financial and non-financial performance can be increased by product strategy. Therefore, in conducting of marketing characteristics of start-up, to increase business performance, the apply of marketing strategy to marketing characteristics of start-up should be required.