• Journal of the Korea Society of Computer and Information
• /
• v.26 no.3
• /
• pp.9-17
• /
• 2021

In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.

• Convergence Security Journal
• /
• v.20 no.4
• /
• pp.113-123
• /
• 2020

The fourth industrial revolution can be called the hyper-connected-based intelligent revolution triggered by advanced information technology and intelligent technology, and the basis for implementing these technologies is 'data'. This study proposes a way to strategically use data in order to lead this intelligent revolution in the defense area. First of all, implications through analysis of domestic and international trends and prior research and current status of defense data management were analyzed, and four directions for development were presented. If the government composes conditions for building, releasing, sharing, distribution, and convergence of defense data considering the environment of national defense in the future, it is expected that it will serve as a foundation and a shortcut to be a digitalized strong military through smart defense innovation in the era of the fourth industrial revolution.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.1
• /
• pp.1-8
• /
• 2023

In this paper, we propose a training method of a recommendation learning model that analyzes the battlefield situation and recommends a suitable hypothesis for the current situation. The proposed learning model uses the preference determined by comparing the two hypotheses as a label data to learn which hypothesis best analyzes the current battlefield situation. Our model is based on Siamese neural network architecture which uses the same weights on two different input vectors. The model takes two hypotheses as an input, and learns the priority between two hypotheses while sharing the same weights in the twin network. In addition, a score is given to each hypothesis through the proposed post-processing ranking algorithm, and hypotheses with a high score can be recommended to the commander in charge.

• Convergence Security Journal
• /
• v.23 no.1
• /
• pp.109-115
• /
• 2023

The future outlook for defense faces various and challenging environments such as the acceleration of uncertainty in the global security landscape and limitations in domestic social and economic conditions. In response, the Ministry of National Defense seeks to address the problems and threats through defense innovation based on scientific and technological advancements such as artificial intelligence, drones, and robots. To introduce advanced AI-based technology, it is essential to integrate and utilize data on IT environments such as cloud and 5G. However, existing traditional security policies face difficulties in data sharing and utilization due to mainly system-oriented security policies and uniform security measures. This study proposes a paradigm shift to a data value-based security policy based on theoretical background on data valuation and life-cycle management. Through this, it is expected to facilitate the implementation of scientific and technological innovations for national defense based on data-based task activation and new technology introduction.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.647-654
• /
• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• Smart Media Journal
• /
• v.12 no.5
• /
• pp.65-72
• /
• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.951-956
• /
• 2023

In the current situation where systematic and active disaster management is becoming more important, domestic libraries do not have their own disaster management plans or support systems. In order to improve these problems, this study looked at overseas cases. Among them, we looked at Japan, where related cases and research are actively underway due to its exposure to various geopolitical disasters. In particular, we focused on cases of public-private cooperation established after the Great East Japan Earthquake. Association's Library Disaster Response Committee and saveMALK, a voluntary network of experts. The Library Disaster Response Committee played a central role in organizing donations and volunteer activities, and saveMALK played a role in collecting and sharing information by forming a collective intelligence among relevant experts. This analysis of the Japanese case has positive implications for building collaborative disaster management system.

• Journal of Korean Clinical Health Science
• /
• v.12 no.1
• /
• pp.1668-1677
• /
• 2024

Objective: This study investigated the awareness of occupational therapy in the community in occupational therapy majors through a survey. This purpose is to investigate the awareness of occupational therapy in the community among occupational therapy majors and establish a theoretical foundation. Methods: The research subjects were surveyed among occupational therapy majors at M University living in Gyeongsangnam-do, and analyzed 141 questionnaires from September 2023 to December 2023. Results: The results of this study that education and awareness improvement are needed to increase awareness of occupational therapy in the community, and it was found that continuing education and case sharing are effective. Activation of home rehabilitation and continuous health management. This institutional development can induce employment activity through community rehabilitation, and activate programs in connection with adult day care centers. For the development of community occupational therapy, participation in education and development of customized treatment are necessary, and patient It should be developed to help with movement and movement, and it has been shown that it can also affect the quality of life of patients. In addition, cutting-edge technologies such as artificial intelligence are expected to be applied to remote support, telemedicine, etc., and are applied to dementia, cognitive patients, and central nervous system patients. In order to institutionalize occupational therapy in the community, it is helpful in daily life, nursing, and management. It was said that this would be helpful for community participation. Conclusion: This study investigated the awareness of occupational therapy in the community among occupational therapy majors. Education and awareness improvement are needed to increase awareness of occupational therapy in the community. Education to improve the professionalism of occupational therapists, strengthening connectivity with other majors, and local organizations. It is believed that collaboration with the local community and institutional supplementation tailored to the needs of the local community were necessary.

• Journal of Korean Academy of Nursing
• /
• v.54 no.2
• /
• pp.119-138
• /
• 2024

Purpose: This study aimed to identify research trends related to emotional leadership among nurse managers by conducting a systematic literature review and meta-analysis. This study sought to derive insights that could contribute to improving emotional leadership in nursing practice. Methods: A systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. Databases including PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, Web of Science, Research Information Sharing Service, Koreanstudies Information Service System, Korean Medical Database, KoreaMed, ScienceON, and DBpia were searched to obtain papers published in English and Korean. Literature searches and screenings were conducted for the period December 1, 2023 to December 17, 2023. The effect size correlation (ESr) was calculated for each variable and the meta-analysis was performed using the statistical software SPSS 29.0, R 4.3.1. Results: Twenty-five (four personal, six job, and fifteen organizational) relevant variables were identified through the systematic review. The results of the meta-analysis showed that the total overall effect size was ESr = .33. Job satisfaction (ESr = .40) and leader-member exchange (ESr = .75) had the largest effect size among the job and organizational-related factors. Conclusion: Emotional leadership helps promote positive changes within organizations, improves organizational effectiveness, and increases member engagement and satisfaction. Therefore, it is considered an important strategic factor in improving organizational performance.

• Journal of Intelligence and Information Systems
• /
• v.17 no.3
• /
• pp.131-146
• /
• 2011

It is becoming more essential than ever for office professionals to become competent in information collection/gathering and problem solving in today's global business society. In particular, office professionals do not only assist simple chores but are also forced to make decisions as quickly and efficiently as possible in problematic situations that can end in either profit or loss to their company. Since office professionals rely heavily on their tacit knowledge to solve problems that arise in everyday business situations, it is truly helpful and efficient to refer to similar business cases from the past and share or reuse such previous business knowledge for better performance results. Case-based reasoning(CBR) is a problem-solving method which utilizes previous similar cases to solve problems. Through CBR, the closest case to the current business situation can be searched and retrieved from the case or knowledge base and can be referred to for a new solution. This reduces the time and resources needed and increase success probability. The main purpose of this study is to design a system called COKRS(Case-based reasoning Office Knowledge Recommender System) and develop a prototype for it. COKRS manages cases and their meta data, accepts key words from the user and searches the casebase for the most similar past case to the input keyword, and communicates with users to collect information about the quality of the case provided and continuously apply the information to update values on the similarity table. Core concepts like system architecture, definition of a case, meta database, similarity table have been introduced, and also an algorithm to retrieve all similar cases from past work history has also been proposed. In this research, a case is best defined as a work experience in office administration. However, defining a case in office administration was not an easy task in reality. We surveyed 10 office professionals in order to get an idea of how to define a case in office administration and found out that in most cases any type of office work is to be recorded digitally and/or non-digitally. Therefore, we have defined a record or document case as for COKRS. Similarity table was composed of items of the result of job analysis for office professionals conducted in a previous research. Values between items of the similarity table were initially set to those from researchers' experiences and literature review. The results of this study could also be utilized in other areas of business for knowledge sharing wherever it is necessary and beneficial to share and learn from past experiences. We expect this research to be a reference for researchers and developers who are in this area or interested in office knowledge recommendation system based on CBR. Focus group interview(FGI) was conducted with ten administrative assistants carefully selected from various areas of business. They were given a chance to try out COKRS in an actual work setting and make some suggestions for future improvement. FGI has identified the user-interface for saving and searching cases for keywords as the most positive aspect of COKRS, and has identified the most urgently needed improvement as transforming tacit knowledge and knowhow into recorded documents more efficiently. Also, the focus group has mentioned that it is essential to secure enough support, encouragement, and reward from the company and promote positive attitude and atmosphere for knowledge sharing for everybody's benefit in the company.