• Journal of Information Management
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• v.37 no.4
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• pp.25-47
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• 2006

A terminology system capable of providing interpretations and classification information on a multilingual science and technology(S&T) terminology is essential to establish an integrated search environment for multilingual S&T information systems. This paper aims to build a base system to manage an integrated information system for multilingual S&T terminology search. It introduces a method to build a search system for S&T terminologies internally linked through the multilingual semantic network and a search technique on the multiple linked nodes. In order to provide a foundation for further analysis researches, it also attempts to suggest a basic approach to interpret terminology clusters generated with those two search methods.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1496-1515
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• 2017

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.

• Journal of the Korea institute for structural maintenance and inspection
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• v.4 no.2
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• pp.185-191
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• 2000

The safety problem of aged tunnels has been emphasized. For the effective maintenance, site inspection of tunnel structures and surrounding grounds are required periodically. Also, the determination of safety of tunnels is not a simple problem. So the role experienced engineer in the maintenance is very important and development of an expert system that can perform as the engineers, has been needed. In this study, from the results of numerical analysis in several case, new precision inspection rules which can substitute actual numerical analysis are determined by a commercial program FLAC and regression analysis under various parameters such as material property, lining thickness, overburden and laterial coefficients. They are added to the knowledge base to determine safety of tunnel lining. To verify the expert system, the results are compared with an existing tunnel diagnosis report. It can be concluded that the new rule are well represented the actual numerical analysis under various site conditions. Therefore it is expected that the systematic management for effective maintenance of tunnel structure will be possible.

• Clinical and Experimental Pediatrics
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• v.63 no.11
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• pp.422-428
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• 2020

In today's world, most children are exposed to various manmade electromagnetic fields (EMFs). EMFs are electromagnetic waves less than 300 GHz. A developing child's brain is vulnerable to electromagnetic radiation; thus, their caregivers' concerns about the health effects of EMFs are increasing. EMF exposure is divided into 2 categories: extremely low frequencies (ELFs; 3-3,000 Hz), involving high-voltage transmission lines and in-house wiring; and radiofrequencies (RFs; 30 kHz to 300 GHz), involving mobile phones, smart devices, base stations, WiFi, and 5G technologies. The biological effects of EMFs on humans include stimulation, thermal, and nonthermal, the latter of which is the least known. Among the various health issues related to EMFs, the most important issue is human carcinogenicity. According to the International Agency for Research on Cancer's (IARC's) evaluation of carcinogenic risks to humans, ELFs and RFs were evaluated as possible human carcinogens (Group 2B). However, the World Health Organization's (WHO's) view of EMFs remains undetermined. This article reviews the current knowledge of EMF exposure on humans, specifically children. EMF exposure sources, biological effects, current WHO and IARC opinions on carcinogenicity, and effects of EMF exposures on children will be discussed. As well-controlled EMF experiments in children are nearly impossible, scientific knowledge should be interpreted objectively. Precautionary approaches are recommended for children until the potential health effects of EMF are confirmed.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.480-490
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• 2019

Nowadays, Electronic Medical Record (EMR) has just implemented at few hospitals for Outpatient Department (OPD). OPD is the diversified data, it includes demographic and diseases of patient, so it need to be clustered in order to explore the hidden rules and the relationship of data types of patient's information. In this paper, we propose a novel approach for unsupervised clustering of patient's demographic and diseases in OPD. Firstly, we collect data from a hospital at OPD. Then, we preprocess and transform data by using powerful techniques such as standardization, label encoder, and categorical encoder. After obtaining transformed data, we use some strong experiments, techniques, and evaluation to select the best number of clusters and best clustering algorithm. In addition, we use some tests and measurements to analyze and evaluate cluster tendency, models, and algorithms. Finally, we obtain the results to analyze and discover new knowledge, meanings, and rules. Clusters that are found out in this research provide knowledge to medical managers and doctors. From these information, they can improve the patient management methods, patient arrangement methods, and doctor's ability. In addition, it is a reference for medical data scientist to mine OPD dataset.

• International Journal of Knowledge Content Development & Technology
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• v.11 no.2
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• pp.77-105
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• 2021

In order to establish a digital-based use environment for the provision of new information services suitable for the 5G era, it is necessary to discuss the direction of service provision by the National Library of Children and Young Adults in the 5G era. Based on utilization services in other fields, library services in the 5G era, including the development and provision of employee education and training services, ultra-high-definition and 360-degree realistic contents and education on library use, provision of multi-dimensional realistic media streaming broadcasting services, provision of telepresence education programs, activation of virtual communities, implementation of hologram performance halls/exhibit centers, and provision of unmanned book delivery services, environment monitoring, safety monitoring, and customized services, were proposed. In addition, based on 5G service, 5G technology, and library application direction, advancing into a producing and supporting base for ultra-realistic and immersive contents in the 5G era, strengthening online and mobile services in the non-contact era, and establishing a smart library environment were proposed as the service provision direction for the National Library of Children and Young Adults in the 5G era.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1534-1538
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• 2020

Smart farming education service focuses on the dissemination of farming information that is the farming knowledge, farming skill, and farmer's experiences and knowhow, etc. This farming information is supposed from current activities, farming product and from the experience of farmer on the field. If the information is not available, or if available and not in a form that is amenable to being brought to the end producer then the process stalls at this point. The core component of the automation process for smart farming education service is the creation of a data store which will be a repository for the information of the smart farming education. The farming sector will benefit immensely from the implementation of farming data in farming contents repository which will serve as the knowledge base for the smart farming education service.

• Wind and Structures
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• v.31 no.6
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• pp.575-591
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• 2020

Wind load is typically considered as one of the governing design loads acting on a structure. Understanding its nature is essential in evaluation of its action on the structure. Many codes and standards are founded on state of the art knowledge and include step by step procedures to calculate wind loads for various types of structures. One of the most accepted means for calculating wind load is using Gust Load Factor or base bending Moment Gust Load Factor (MGLF), where codes are adjusted based on local data available. Although local data may differ, the general procedure is the same. In this paper, ASCE 7-16 (2017), which is used as the main reference in the U.S., and Korean Building Code (KBC 2016) are compared in evaluation of wind loads. The primary purpose of this paper is to provide insight on each code from a structural engineering perspective. Herein, discussion focuses on where the two codes are compatible and differ. In evaluating the action of wind loads on a building, knowledge of the dynamic properties of the structure is critical. For this study, the design of four figurative high-rise buildings with dual systems was analyzed.

• The Research Journal of the Costume Culture
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• v.29 no.6
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• pp.865-880
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• 2021

The purpose of this study was to examine job competencies for sales training program development to maximize profits in fashion retailing. An empirical online survey was conducted from September to December 2019, and data was collected from 200 salespeople and store managers working in fashion stores. Results were analyzed using frequency analysis, factor analysis, variance analysis, and regression analysis with SPSS 25.0. The major findings of this study were as follows. First, the most important job competencies identified by fashion store managers were: sales sense know-how, customer service skills, and sales person's fashion style sense, product knowledge, fashion marketing and customer management. The job competency factors for sales training programs included empathy with the customer, product knowledge, communications and networking, basic job requirement, and sales skills. These five factors positively influenced the employment intentions and expectations of work performance of graduates. These factors also had a positive influence on the need of sales training program and intention to participate in retraining. Store managers in fashion retail thought the most appropriate period for on-the-job training was either 2-4 days or more than 1 week. The results of this study can be used as a base to develop training programs for job efficiency for salespeople in fashion retailing.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.