• Korean Journal of Radiology
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• v.24 no.5
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• pp.478-479
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• 2023

• Journal of Digital Convergence
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• v.13 no.10
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• pp.579-586
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• 2015

Sport talent development center have been operating in order to support sports gifted children of the training in the early finding and selecting potential elementary school students for sports. For this purpose, elementary students in grades 2~6 are selected through basal fitness and physique test. However qualitative assessment of the student was not in progress, but depending on current physique and fitness totally sports talented children were selected. Therefore this study was to develop a tool to determine giftedness based on the observation method to assess the athletic potential of gifted children based on Yoon's competition intelligence(2011). For this purpose of study, sub-factors were extracted through expert consultations. Based on the extracted sub-factors, such as training intelligence, learning ability practical intelligence were extracted for practical intelligence, and finally 16 evaluation questions were proposed to evaluate sports talented children. Proposed questions will be helpful for quality evaluation of sports gifted children, as well as using as a method for discovering sports gifted children.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• Journal of The Korean Institute of Defense Technology
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• v.5 no.3
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• pp.1-7
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• 2023

Recently, in the defense field, efforts are being made to collect data by building data centers to incorporate artificial intelligence technology. Weapon system training data can be used as input data for artificial intelligence models and can be used as high-quality data to maximize training performance and develop military strategies. However, training data contains personal information such as the names and military numbers of the personnel who operated the equipment, and training records that reveal the characteristics of the weapon system. If such data is passed on to the enemy, not only the specifications and performance of the weapon system but also the proficiency of each operator may be exposed. In this paper, we propose a pseudonym processing methodology for education and training data security and also suggest a direction for revising related laws.

• Iberoamérica
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• v.13 no.1
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• pp.351-388
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• 2011

Many global business failures have been ascribed to a lack of intercultural competence, especially to a lack of an adequate conceptualization and definition of intercultural competence, focusing instead on the knowledge, skills, and attributes that appear to be its antecedents. Intercultural competence should be perceived as multifaceted important components of global management capabilities. Depending on the related concepts of intercultural competence, such as global mindset, intercultural sensitivity, and cultural intelligence, dynamic aspects of intercultural competence as learning process are suggested. Also, the domain of intercultural competence in the context of global management or business comprised three dimensions - perception management, relationship management, and self management. Each dimension is characterized by facets that further delineate aspects of intercultural competence. With respect to the domain of intercultural competence, appropriateintercultural training methods should be designed. In practice, human resource managers may benefit from gaining knowledge about which measures to use for identifying employee's weakness in intercultural competence in order to create appropriate training programs.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.13 no.1
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• pp.183-198
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• 2002

In this paper we discuss what Competitive Intelligence means, why it is growing in importance in the business world and how Library and Information Science Schools in the United States are responding to the opportunities presented by Competitive Intelligence. As a conclusion, we discuss some of the implications of our findings for Library and Information Science education in Korea.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.461-467
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• 2020

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1146-1151
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• 2018

Recently, researches on the intelligence of electric power facilities have been trying to apply artificial intelligence techniques as computer platforms have improved. In particular, faults occurring in substation should be able to quickly identify possible faults and minimize power fault recovery time. This paper presents fault location technique for 154kV substation using neural network. We constructed a training matrix based on the operating conditions of the circuit breaker and IED to identify the fault location of each component of the target 154kV substation, such as line, bus, and transformer. After performing the training to identify the fault location by the neural network using Weka software, the performance of fault location discrimination of the designed neural network was confirmed.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• Journal of Home Health Care Nursing
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• v.29 no.3
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• pp.319-328
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• 2022

Purpose: This study investigated the relationship between artificial intelligence ethical awareness, bioethics awareness, and person-centered care of general hospital nurses. Methods: The participants were 192 nurses. Data were analyzed using descriptive statistics, t-test, analysis of variance, and Pearson's correlation coefficient with the SPSS program. Results: The average points for artificial intelligence ethical awareness, bioethics awareness, and person-centered care were 2.93, 2.77, and 3.50, respectively. Artificial intelligence ethical awareness and bioethics awareness had statistically significant negative relationships. Artificial intelligence ethical awareness, bioethics awareness, and person-centered care were not significantly correlated. Conclusion: Education, training, and organizational support are needed to improve artificial intelligence ethics awareness, bioethics awareness, and person-centered care for general hospital nurses.