• The Journal of Korean society of community based occupational therapy
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• v.3 no.2
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• pp.47-56
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• 2013

Objective : The objective of this study was understanding the procedure of self-care training in occupational therapy and exploring experienced occupational therapists' perspectives in self-care training Method : A phenomenological study design was used to collect information regarding use of self-care training among occupational therapists working in a different settings. The data collection process was conducted by using a structured interview and survey. Result : The self-care training strategies used by occupational therapists were summarized to four main themes; 1. Different strategies for different age group, 2. Design therapeutic strategy rely on client's natural environment, 3. Use of theoretical background: occupation-based or client-centered, or both. 4. Use of self-awareness stragety; using different method to reflect self-awareness. Conclusion : The self-care training is not just simply conduct ADL training but understanding client's age, environment, theoretical background, and self-awareness of the client. Through eatablishing understandable self-care training strategy according to client's age and environment, the more effective self-care training would be possible.

• KIEE International Transactions on Power Engineering
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• v.4A no.4
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• pp.171-177
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• 2004

In the current age of the information oriented society in which we live, many people use PCs and are dependant on the databases provided by the network server. However, online data can be missed during the occurrence of a blackout and furthermore, power failure can greatly effect Power Quality. This has resulted in the trend of using interruption-free live-line work when trouble occurs in a power system. However, 83% of the population receives an electric shock experience when a laborer is performing interruption-free live-line work. In the interruption-free method, education and training problems have been pinpointed. However, there are few instructors to implement the necessary training. Furthermore, the trainees undergo only a short training period of just 4 weeks. In this paper, to develop a method with no restrictions on time and place and to ensure a reduction in the misuse of materials, immersion type virtual reality (or environment) technology is used. The users of a 3D immersion type VR training system can interact with the system by performing the equivalent action in a safe environment. Thus, it can be valuable to apply this training system to such dangerous work as 'Interruption-free live-line work exchanging COS (Cut-Out-Switch)'. In this program, the user carries out work according to instructions displayed through the window and speaker and cannot perform other tasks until each part of the task is completed in the proper sequence. The workers using this system can utilize their hands and viewpoint movement since they are in a real environment but the trainee cannot use all parts and senses of a real body with the current VR technology. Despite these weak points, when we consider the trends of improvement in electrical devices and communication technology, we can say that 3D graphic VR application has high potentiality.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.3
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• pp.227-239
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• 2021

Purpose: This study used an exploratory sequential approach (mixed methods) design to explore essential meaning through comparing and analyzing the experiences of nursing students in virtual simulation practice and high fidelity simulation practice education in parallel. Methods: The study participants were 20 nursing students, and data were collected through focus group meetings from July 17 to August 5, 2020, and via online quantitative data from November 10 to November 15, 2020. The qualitative data were analyzed using Giorgi's phenomenological method, and the quantitative data were analyzed using descriptive statistics, the Mann-Whitney U test, Kruskal-Wallis H test analysis of variance and Spearman's ρ correlation. Results: The comparison between the two simulation training experiences was shown in five contextual structures, as follows: (1) reflection of the clinical field, (2) thinking theorem vs. thinking expansion, (3) individual-centered learning vs. team-centered learning, (4) attitudes toward participating in practical training, (5) metacognition of personal competency as a prospective nurse, and (6) revisiting the method of practice training. There was a positive correlation between satisfaction with the practice and the clinical judgment ability of high fidelity simulation, which was statistically significant (r=.47, p=.036). Conclusion: Comparing the experiences between virtual simulation practice training and high fidelity simulation practice training, which has increased in demand due to the Coronavirus Disease-2019 pandemic, is meaningful as it provides practical data for introspection and reflection on in-campus clinical education.

• The Journal of Korean Physical Therapy
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• v.34 no.1
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• pp.38-44
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• 2022

Purpose: The purpose of this study was to investigate the effects of complex training on injury, flexibility, and muscle stiffness in high school male football players. Methods: A total of 60 football players were included in the study and were divided into three groups viz. the complex training group (CTG), 11+ training group (11+TG), and traditional training group (TTG). Injuries were recorded based on the prospective investigation method after starting the study, and the flexibility and muscle stiffness of the subjects were evaluated. Results: The research results showed that the injury rate per match was significantly lower in the CTG and 11+TG than the TTG. In the CTG, the flexibility of the hamstrings significantly increased and the stiffness of the rectus femoris (RF), biceps femoris (BF), and tensor fascia latae (TFL) muscles significantly decreased (p<0.05). In the 11+TG, the stiffness of the RF significantly decreased (p<0.05). In the TTG, the flexibility of the hamstrings significantly increased (p<0.05). Hamstring flexibility showed a significantly higher increase in the CTG and TTG compared to the 11+TG (p<0.05). Also, the stiffness of the RF and TFL muscles showed a significantly higher decrease in the CTG compared to the 11+TG and TTG (p<0.05). The stiffness of the BF muscles too showed a more significant decrease in the CTG compared to the TTG (p<0.05). Conclusion: The complex training method of the Fédération International de Football Association (FIFA) 11+ and self-myofascial release (SMFR) as a warm-up program, prevent injuries, enhance flexibility, and lower muscle stiffness of football players in high school. Thus, it is necessary to ensure the widespread use of the complex training program by instructors and players under the supervision of the Korea Football Association (KFA), given its reliability in preventing injuries and improving the performance of football players.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.83-88
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• 2024

In this research, we propose a Semi-Supervised learning based railroad surface defect detection method. The Resnet50 model, pretrained on ImageNet, was employed for the training. Data without labels are randomly selected, and then labeled to train the ResNet50 model. The trained model is used to predict the results of the remaining unlabeled training data. The predicted values exceeding a certain threshold are selected, sorted in descending order, and added to the training data. Pseudo-labeling is performed based on the class with the highest probability during this process. An experiment was conducted to assess the overall class classification performance based on the initial number of labeled data. The results showed an accuracy of 98% at best with less than 10% labeled training data compared to the overall training data.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.11
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• pp.596-603
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• 2004

This paper proposes an expert system based on the pattern recognition method which can enhance the accuracy and effectiveness of real-time bus reconfiguration strategy for the transfer of faulted load when a main transformer fault occurs in the automated substation. The minimum distance classification method is adopted as the pattern recognition method of expert system. The training pattern set is designed MTr by MTr to minimize the searching time for target load pattern which is similar to the real-time load pattern. But the control pattern set, which is required to determine the corresponding bus reconfiguration strategy to these trained load pattern set is designed as one table by considering the efficiency of knowledge base design because its size is small. The training load pattern generator based on load level and the training load pattern generator based on load profile are designed, which are can reduce the size of each training pattern set from max L/sup (m+f)/ to the size of effective level. Here, L is the number of load level, m and f are the number of main transformers and the number of feeders. The one reduces the number of trained load pattern by setting the sawmiller patterns to a same pattern, the other reduces by considering only load pattern while the given period. And control pattern generator based on exhaustive search method with breadth-limit is designed, which generates the corresponding bus reconfiguration strategy to these trained load pattern set. The inference engine of the expert system and the substation database and knowledge base is implemented in MFC function of Visual C++ Finally, the performance and effectiveness of the proposed expert system is verified by comparing the best-first search solution and pattern recognition solution based on diversity event simulations for typical distribution substation.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.233-242
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• 2021

In this paper, as a liberal arts course for non-majors, we proposed a supervised learning artificial neural network parameter optimization method and a basic education method for activation function to design a basic artificial neural network subject curriculum. For this, a method of finding a parameter optimization solution in a spreadsheet without programming was applied. Through this training method, you can focus on the basic principles of artificial neural network operation and implementation. And, it is possible to increase the interest and educational effect of non-majors through the visualized data of the spreadsheet. The proposed contents consisted of artificial neurons with sigmoid and ReLU activation functions, supervised learning data generation, supervised learning artificial neural network configuration and parameter optimization, supervised learning artificial neural network implementation and performance analysis using spreadsheets, and education satisfaction analysis. In this paper, considering the optimization of negative parameters for the sigmoid neural network and the ReLU neuron artificial neural network, we propose a training method for the four performance analysis results on the parameter optimization of the artificial neural network, and conduct a training satisfaction analysis.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.47-55
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• 2020

In this paper, acting of Meryl Streep, who was recognized as a method actor in Hollywood, was analyzed, and this paper tried to find out how Meryl Streep utilizes the American acting method to improve her acting skills. To do this, we first looked at the concepts and theories of the American acting method, and analyze the acting of Meryl Streep through three films: "Sophie's Choice", "The Devil Wears Prada", and "The Iron Lady". Analysis results show that Meryl Streep utilizes all of the American acting methods, such as "Emotional memory", "Imagination", "Given Circumstance", and "Repetition" without any particular distinction. Meryl Streep uses not a specific acting theory, but selects appropriate training for herself. According to the analysis of this paper, we can understand the acting method that Meryl Streep uses, and shows some way to develop the acting training. The result of this paper can be used to the acting training, and it will be an opportunity to follow in the footsteps of one of the greatest actress.

• Smart Structures and Systems
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• v.29 no.1
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.