• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.343-348
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• 2005

The goal of this paper is to study the joint effect of factors of neural network teaming procedure. There are many factors, which may affect the generalization ability and teaming speed of neural networks, such as the initial values of weights, the learning rates, and the regularization coefficients. We will apply a constructive training algerian for neural network, then patterns are trained incrementally by considering them one by one. First, we will investigate the effect of these factors on generalization performance and learning speed. Based on these factors' effect, we will propose a joint method that simultaneously considers these three factors, and dynamically hue the learning rate and regularization coefficient. Then we will present the results of some experimental comparison among these kinds of methods in several simulated nonlinear data. Finally, we will draw conclusions and make plan for future work.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.413-415
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• 2022

Since human joints can be used as useful information for analyzing human behavior as a component of the human body, many studies have been conducted on human action recognition using joint information. However, it is a very complex problem to recognize human action that changes every moment using only each independent joint information. Therefore, an additional information extraction method to be used for learning and an algorithm that considers the current state based on the past state are needed. In this paper, we propose a human action recognition technique considering the positional relationship of connected joints and the change of the position of each joint over time. Using the pre-trained joint extraction model, position information of each joint is obtained, and bone information is extracted using the difference vector between the connected joints. In addition, a simplified neural network is constructed according to the two types of inputs, and spatio-temporal features are extracted by adding LSTM. As a result of the experiment using a dataset consisting of 9 behaviors, it was confirmed that when the action recognition accuracy was measured considering the temporal and spatial relationship features of each joint, it showed superior performance compared to the result using only single joint information.

• Journal of Technology Innovation
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• v.29 no.2
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• pp.1-30
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• 2021

Recent years have witnessed various attempts of firms to acquire new knowledge. Purchasing intellectual property or merger and acquisition (M&A) can be such attempts, but joint venture can also be an effective way internalizing new complementary assets from external partners. However, due to difficulties in the formation and implementation of learning strategies, many joint ventures have failed to acquire necessary knowledge. In this respect, based on contingency theory and dynamic capability, the current research aims to investigate the failure case of knowledge transfer in an international joint venture - KEFICO established by Hyundai motors and BOSCH. Case firm optimized for hardware technology but did not establish a differentiated learning strategy and organizational structure to acquire software skills, which are intellectuals of different natures. Due to this inconsistency, it was not able for KEFICO to absorb new type of knowledge (skills related to engine control system). This study suggests the theoretical framework illustrating the case and provides some important implications for organizational learning.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.187-194
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• 2021

A person's behavioral recognition is the recognition of what a person does according to joint movements. To this end, we utilize computer vision tasks that are utilized in image processing. Human behavior recognition is a safety accident response service that combines deep learning and CCTV, and can be applied within the safety management site. Existing studies are relatively lacking in behavioral recognition studies through human joint keypoint extraction by utilizing deep learning. There were also problems that were difficult to manage workers continuously and systematically at safety management sites. In this paper, to address these problems, we propose a method to recognize risk behavior using only joint keypoints and joint motion information. AlphaPose, one of the pose estimation methods, was used to extract joint keypoints in the body part. The extracted joint keypoints were sequentially entered into the Long Short-Term Memory (LSTM) model to be learned with continuous data. After checking the behavioral recognition accuracy, it was confirmed that the accuracy of the "Lying Down" behavioral recognition results was high.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.42-49
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• 2018

In this study, we have developed a prediction model for expansion and contraction behaviors of expansion joint in Gwangan Bridge using machine learning techniques and bridge monitoring data. In the development of the prediction model, two famous machine learning techniques, multiple regression analysis (MRA) and artificial neural network (ANN), were employed. Structural monitoring data obtained from bridge monitoring system of Gwangan Bridge were used to train and validate the developed models. From the results, it was found that the expansion and contraction behaviors predicted by the developed models are matched well with actual expansion and contraction behaviors of Gwangan Bridge. Therefore, it can be concluded that both MRA and ANN models can be used to predict the expansion and contraction behaviors of Gwangan Bridge without actual measurements of those behaviors.

• Journal of KIISE
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• v.43 no.6
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• pp.645-652
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• 2016

Joint Attention (JA) has been known to play a key role in human social learning. However, relative impact of different interaction types has yet to be rigorously examined because of limitation of existing methodologies to simulate human-to-human interaction. In the present study, we designed a new JA paradigm with emulating human-avatar interaction and virtual reality technologies, and tested the paradigm in two experiments with healthy adults. Our results indicated that initiating JA (IJA) condition was more effective than responding JA (RJA) condition for social learning in both head and hand interactions. Moreover, the hand interaction involved better information processing than the head interaction. The implication of the results, the validity of the new paradigm, and limitations of this study were discussed.

• Journal of muscle and joint health
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• v.30 no.3
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• pp.197-207
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• 2023

Purpose: This study attempted to discern the factors that influence nursing professionalism, learning agility, and the nursing practice environment on the performance of nurses. Methods: Data were collected from 202 clinical nurses who both consented to participate and who have worked for more than one year in one of five small- and medium-sized hospitals located in three regions of Korea. The data were analyzed using the SPSS/WIN 26.0 statistical programs. Results: The nurses' performance showed a statistically significant correlation with nursing professionalism (r=.50, p<.001), learning agility (r=.54, p<.001), and nursing practice environment (r=.37, p<.001). Factors affecting the results of nurses' performance in relation to the subjects are those of learning agility (β=.33, p<.001), nursing professionalism (β=.25, p<.001), clinical career (β=.24, p=.001), education level (β=.16, p=.011), and nursing practice environment (β=.15, p=.016). Conclusion: To improve the performance of nurses in medium-sized hospitals, it is necessary to develop a nursing practice environment, programs, and strategies for enhancing nursing professionalism and learning agility.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.13-21
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• 2022

In this paper, we propose a learning based joint demosaicing and super-resolution framework which uses only the mosaiced color filter array(CFA) image as the input. As the proposed method works only on the mosaicied CFA image itself, there is no need for a large dataset. Based on our framework, we proposed two different structures, where the first structure uses one deep image prior network, while the second uses two. Experimental results show that even though we use only the CFA image as the training image, the proposed method can result in better visual quality than other bilinear interpolation combined demosaicing methods, and therefore, opens up a new research area for joint demosaicing and super-resolution on raw images.