• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.97-106
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• 2014

A company regards knowledge shared and used within a corporate organization as intellectual capital linked to corporate competences. A great deal of research has been conducted in the past to identify knowledge sharing among knowledge workers. Some papers focus on information technology for automated, efficient, and explicit knowledge sharing. Other papers emphasize the role of social networks to identify the flow of tacit knowledge. Though the role of CoP(Community of Practice) is emphasized to facilitate knowledge management among workers, it is not an easy task to identify the potential members of CoP without voluntary participation of the workers. In this study we adopt a social network approach to analyze knowledge propagation and to identify the potential members of CoP. We suggest a framework for classifying knowledge workers and the result of feasibility study.

• Journal of the Korean Home Economics Association
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• v.42 no.4
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• pp.155-165
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• 2004

The purpose of this study was to examine the effect of motion activities utilizing various of materials(ex, using fabric, stone, wood, percussion instrument) on young children's emotional intelligence and its subareas, including the an ability to identify and control their on emotions, self-motivating skills, the ability to identify other's emotional state, and interpersonal skills. The subjects in this study were 60, 5-year-old preschoolers attending D kindergarten in U city. The experiment w3s implemented in an experimental group 36 times for 12 weeks, three times a week, by using various of materials. The control group was only exposed to the routine phvsical programs according to the 6th curricula. To see if there were any disparities between pretest and posttest results, paired t-test was carried out, and t-test by independent sampling was employed to find out intergroup gaps. Following are the findings of this study. First, the motion activities utilizing various materials made a significant difference to the young children's emotional intelligence. Second, the motion activities atilizing various materials was effective for their ability to identify their own emotions, ability to control their own emotions and self-motivation. Third, the motion activities utilizing various materials didn't bring any significant changes to the young children's ability to identify other's emotions and their interpersonal skills. Thus, motion activities that took advantage of various materials had a positive impact on the development of the emotional intelligence of the young children. We suggest that more attention be paid to motion activities and the formulation of various and systematic motion programs as a way to raise emotional intelligence.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.3
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• pp.140-151
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• 2020

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

• Structural Engineering and Mechanics
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• v.36 no.5
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• pp.643-667
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• 2010

The classical flexibility difference method detects damage by observing the difference of conventional deflection flexibility matrices between pre- and post-damaged states of a structure. This method is not able to identify multiple damage scenarios, and its criteria to identify damage depend upon the boundary conditions of structures. The key point behind the inability and dependence is revealed in this study. A more feasible flexibility for damage detection, the Angle-between-String-and-Horizon (ASH) flexibility, is proposed. The physical meaning of the new flexibility is given, and synthesis of the new flexibility matrix by modal frequencies and translational mode shapes is formulated. The damage indicators are extracted from the difference of ASH flexibility matrices between the pre- and post-damaged structures. One feature of the ASH flexibility is that the components in the ASH flexibility matrix are associated with elements instead of Nodes or DOFs. Therefore, the damage indicators based on the ASH flexibility are mapped to structural elements directly, and thus they can pinpoint the damaged elements, which is appealing to damage detection for complex structures. In addition, the change in the ASH flexibility caused by damage is not affected by boundary conditions, which simplifies the criteria to identify damage. Moreover, the proposed method can determine relatively the damage severity. Because the proposed damage indicator of an element mainly reflects the deflection change within the element itself, which significantly reduces the influence of the damage in one element on the damage indicators of other damaged elements, the proposed method can identify multiple damage locations. The viability of the proposed approach has been demonstrated by numerical examples and experimental tests on a cantilever beam and a simply supported beam.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.87-97
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• 2013

In the RFID system, multiple tags respond in the process of identifying multiple tags in the reader's interrogation zone, resulting in collisions. Tag collision occurs when two or more tags respond to one reader, so that the reader cannot identify any tags. These collisions make it hard for the reader to identify all tags within the interrogation zone and delays the identifying time. In some cases, the reader cannot identify any tags. The reader needs the anti-collision algorithm which can quickly identify all the tags in the interrogation zone. The proposed algorithm efficiently divides tag groups through an efficient separation to respond, preventing collisions. Moreover, the proposed algorithm identifies tags without checking all the bits in the tags. The prediction with efficient separation reduces the number of the requests from the reader.

• Journal of Korean Clinical Nursing Research
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• v.24 no.3
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• pp.336-346
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• 2018

Purpose: This study was conducted to identify nurses'critical thinking disposition, self-leadership and clinical competency in small to medium sized hospitals less than 300beds. Methods: Data were collected using the questionnaire from 227 nurses in Incheon city and Gyeonggi province from March to April, 2017. The data were analyzed using t-test, ANOVA and $Scheff{\acute{e}}^{\prime}s$ test to identify differences in critical thinking disposition, self-leadership and clinical competency. Pearson correlation coefficients were used to identify the correlation among the study variables, and multiple regression was used to identify factors contributing to clinical competency. Results: There were significant differences in critical thinking disposition according to age, marital status, clinical career, career in currently working department and education about leadership. Significant differences in self-leadership were identified according to marital status, work position, working department, work type, education about leadership, and turnover intention. Clinical competency was significantly different depending on age, education, monthly income, work position, career in currently working department, work type, education about critical thinking disposition and education about leadership. Clinical competency was positively correlated critical thinking disposition and self-leadership. Critical thinking deposition, monthly income and self-leadership explained 30.1% of clinical competency of nurses working in small to medium sized hospitals. Conclusion: The results of this study suggest that we need to improve nurses'critical thinking disposition, self-leadership, and the clinical competency.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.45-52
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• 2021

Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.261-270
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• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.

• Korean Circulation Journal
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• v.53 no.11
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• pp.758-771
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• 2023

Background and Objectives: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. Methods: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. Results: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. Conclusions: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

• Journal of Korean Academy of Nursing
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• v.35 no.2
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• pp.292-302
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• 2005

Purpose: Concept analysis was performed on the behavioral concept of endotracheal suctioning (ETS), to identify the goal, to develop astandardized clinical protocol, to identify the antecedents and consequences, and to differentiate the improper use of ETS. Method: Walker & Avant's concept analysis was employed using clinical guidelines, books and review articles in which the procedures of ETS were written in detail and published in Pubmed within the last 20 years. Result: The macro-goal of ETS was to remove accumulated respiratory secretions. Three defining attributes of ETS were identified; catheter, suctioning and asepsis. Each attribute involved empirical referents, such as the size and depth of the catheter, the suction pressure, duration and method for suctioning. The antecedents of ETS were identical to the clinical evidences for the need of ETS such as the nursing assessment data. The consequences of ETS serve as an evaluation criteria on the effectsof ETS based on the goal of ETS. Conclusion: The concept analysis of ETS demonstrates an example of considering a specific nursing protocol of ETS as a behavioral concept, applying concept analysis to it to identify it's key behavioral components as defining attributes and empirical referents and then developing and applying the standard ETS protocol.