• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.237-254
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• 2005

The vast mechanical systems could be classified as closed loop system, open loop system and open & closed (switching) system. In the closed loop system, the kinematics and dynamics of 3-D mechanisms will be reviewed and closed form solutions using the direction cosine matrix method and reflection transformation method will be introduced. In the open loop system, kinematic & dynamic analysis methods regarding the redundant system which has more degrees of freedom in joint space than those of task space are reviewed and discussed. Finally, switching system which changes its phase between closed and open loop motion is investigated with the principle of dynamical balance. Among switching systems, the human gait in biomechanics and humanoid in robotics are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.1044-1050
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• 2001

This paper presents firstly the method of Euler angle matching designing the transfer alignment using the attitude matching. In this method, the observation directly uses Euler angle difference between MINS and SINS so it needs to describe the rotation vector error to the Euler angle error. The rotation vector error related to the Euler angle error is derive from the direction cosine matrix error equation. The feasibility of the Kalman filter designed for the transfer alignment by Euler angle matching is analyzed by the alignment error results with respect to the roll angle the pitch angle, and the yaw angle matching.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.178-184
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• 2002

This paper deals with the transfer alignment problem of SDINS(StrapDown Inertial Navigation System) subjected to roll and pitch motions of the ship. In order to reduce alignment errors induced by ship body flexure, a linearized error model for the velocity and attitude matching transfer alignment system is first derived by linearizing the nonlinear measurement equation with respect to the dominant y axis component and defining the flexure state of random constant type. And then a robust state estimation scheme is introduced to account for modeling uncertainty of the flexure. By interpreting the simulation results and comparing with the velocity and DCM(Direction Cosine Matrix) partial matching method, it is shown that the proposed method is effective enough to improve the azimuth alignment performance.

• Journal of Korean Academy of Nursing
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• v.46 no.1
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• pp.50-58
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• 2016

Purpose: The purpose of this study was to identify the knowledge structure of cancer survivors. Methods: For data, 1099 articles were collected, with 365 keywords as a Noun phrase extracted from the articles and standardized for analyzing. Co-occurrence matrix were generated via a cosine similarity measure, and then the network analysis and visualization using PFNet and NodeXL were applied to visualize intellectual interchanges among keywords. Results: According to the result of the content analysis and the cluster analysis of author keywords from cancer survivors articles, keywords such as 'quality of life', 'breast neoplasms', 'cancer survivors', 'neoplasms', 'exercise' had a high degree centrality. The 9 most important research topics concerning cancer survivors were 'cancer-related symptoms and nursing', 'cancer treatment-related issues', 'late effects', 'psychosocial issues', 'healthy living managements', 'social supports', 'palliative cares', 'research methodology', and 'research participants'. Conclusion: Through this study, the knowledge structure of cancer survivors was identified. The 9 topics identified in this study can provide useful research direction for the development of nursing in cancer survivor research areas. The Network analysis used in this study will be useful for identifying the knowledge structure and identifying general views and current cancer survivor research trends.

• Korean Journal of Occupational Health Nursing
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• v.24 no.2
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• pp.76-85
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• 2015

Purpose: The purpose of this study was to identify knowledge structure of the Korean Journal of Occupational Health Nursing from 1991 to 2014. Methods: 400 articles between 1991 and 2014 were collected. 1,369 keywords as noun phrases were extracted from articles and standardized for analysis. Co-occurrence matrix was generated via a cosine similarity measure, then the network was analyzed and visualized using PFNet. Also NodeXL was applied to visualize intellectual interchanges among keywords. Results: According to the results of the content analysis and the cluster analysis of author keywords from the Korean Journal of Occupational Health Nursing articles, 7 most important research topics of the journal were 'Workers & Work-related Health Problem', 'Recognition & Preventive Health Behaviors', 'Health Promotion & Quality of Life', 'Occupational Health Nursing & Management', 'Clinical Nursing Environment', 'Caregivers and Social Support', and 'Job Satisfaction, Stress & Performance'. Newly emerging topics for 4-year period units were observed as research trends. Conclusion: Through this study, the knowledge structure of the Korean Journal of Occupational Health Nursing was identified. The network analysis of this study will be useful for identifying the knowledge structure as well as finding general view and current research trends. Furthermore, The results of this study could be utilized to seek the research direction in the Korean Journal of Occupational Health Nursing.

• Journal of the Korean Chemical Society
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• v.27 no.4
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• pp.244-254
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• 1983

Single crystals of sodium thiosulfate $(Na_2S_2O_3) have been grown from the saturated solution by the evaporation method at the optimum condition. Radiation damages in the crystal by ${\gamma}$-irradiation of $20{\times}10^6$ Rontgen have given rise to paramagnetic centers. The anisotropic spectra of each paramagnetic species have been obtained with the X-band EPR spectrometer at room temperature. When an isotropic D.P.P.H. at g value of 2.0036 is based on. ESR Spectra of the single crystal are recorded for each rotation about the perpendicular a, b and c axis with intervals of $10^{\circ}$ from $0^{\circ}$to $180^{\circ}$ in order to find out the properties of the crystal for anglar variation of the anisotropic peaks. The g values are calculated from the line position between the anisotropic peaks and the isotropic peaks of D.P.P.H. and then principal g values and their direction cosines of the species is obtained by the diagonalization of 9 matrix elements of the corresponding g values. From the analysis of the characteristic principal g values and direction cosines for ${\gamma}$-irradiated $Na_2S_2O_3$ crystal, anisotropic peaks corresponding to $SO_2^+, SO_2^- $are identified and the existences of unidentified and unstable paramagnetic defects are verified.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.