• 한국문헌정보학회지
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• 제11권
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• pp.43-75
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• 1984

Information science is the study how in formation is transferred and all the intermediate steps of collecting, organizing, interpreting, storing, retrieving, disseminating and trans foming information. Professional education means the transfer of knowledge, the development of cognitive abilities and the infusion of professional attitudes. Training may be defined as practice-based instruction in the development and use of professional skills. Each is affected by the confluence of social, economic and technological realities of the environment where the learning takes place. We have witnessed controversy about methods of curriculum revision and change. Should information science courses be added to the traditional library science curriculum or should the new approaches be integrated within the subject matter of each individual course? The article is based upon the assumption that education for librarianship is at a turning point. To provide this information, 25 curricula of colleges and universities were analysed to assist in the study. Also 32 information professionals were asked to assist in the study. In the experimental part of this study, curricula based on the education and training of library and information profession als were examined. The most frequently offered compulsory course 'Introduction to Information Science' exposes students to a new way of looking at library and information problems. Information retrieval, library automation, computer programming, data processing, indexing and abstraction, communication, system analysis has offered. These indicate a curriculum slowly shift from traditional librarianship to an emphasis on computerization and automation. Also from a questionnaire listing 58 events might influence library and information science education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권8호
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• 국제초고층학회논문집
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• 제10권4호
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• pp.299-310
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• 2021

External fire spread has the potential to breach vertical compartmentation and violate the fire safety strategy of a building. The traditional design solution to this has been the use of non-combustible materials and spandrel panels but recent audits show that combustible materials are widespread and included in highly complex systems. Furthermore, most jurisdictions no longer require detailing of spandrel panels under many different circumstances. These buildings require rapid investigation using rational scientific methods to be able to adequately classify the fire risk. In this work, we use an extensive experimental campaign of material-scale data to explore the critical parameters driving upward flame spread. Two criteria are outlined using two different approaches. The first evaluates the time to ignition and the time to burnout to assess the ability for a fire to spread, and can be easily determined using traditional means. The second evaluates the preheated flame length as the critical parameter driving flame spread. A wide range of cladding materials are ranked according to these criteria to show their potential propensity to flame spread. From this, designers can use conservative approaches to perform fire risk assessments for buildings with combustible materials or can be used to aid decision-making. Precise estimates of flame spread rates within complex façade systems are not achievable with the current level of knowledge and will require a substantial amount of work to make progress.

• 동아시아경상학회지
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• 제10권4호
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• pp.39-45
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• 2022

Purpose - This short paper reviews the debate dealing with the relationship between strategy and traditional strategy approach on the one hand, and business models and new business model approach on the other hand and tries to offer useful direction to be more helpful to theoretical discussions. Research design, data, and methodology - This paper makes a theoretical analysis to explain the confusion surrounding the debate, reviewing mainly literature survey papers and finds theoretical conjecture and its limitations in order to present useful direction to the future theoretical work. Result - In order to comprise its diversity, business model studies should consider the characteristics of each firm, sector, and market. Adding further elements which are related to each sector or market, theoretical studies can capture the diverse phenomena related to business model and business model innovation. Conclusion - The traditional strategy perspective can be utilized to the Business model phenomenon in the case of incumbent firms and non-digital sector and existing markets. Meanwhile the new business model perspective can be utilized to business model phenomena in case of start-ups and digital sector and emerging markets. Reconciling two perspectives, the studies dealing with the business model should focus on the characteristics of firms, markets, and knowledge from the perspective of business model innovation.

• Journal of Microbiology and Biotechnology
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• 제32권2호
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• pp.195-204
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• 2022

Chinese chive (Allium tuberosum Rottler) has potential risks associated with pathogenic bacterial contamination as it is usually consumed raw. In this study, we investigated the microbiota of Chinese chives purchased from traditional markets and grocery stores in March (Spring) and June (Summer) 2017. Differences in bacterial diversity were observed, and the microbial composition varied across sampling times and sites. In June, potential pathogenic genera, such as Escherichia, Enterobacter, and Pantoea, accounted for a high proportion of the microbiota in samples purchased from the traditional market. A large number of pathogenic bacteria (Acinetobacter lwoffii, Bacillus cereus, Klebsiella pneumoniae, and Serratia marcescens) were detected in the June samples at a relatively high rate. In addition, the influence of the washing treatment on Chinese chive microbiota was analyzed. After storage at 26℃, the washing treatment accelerated the growth of enterohemorrhagic Escherichia coli (EHEC) because it caused dynamic shifts in Chinese chive indigenous microbiota. These results expand our knowledge of the microbiota in Chinese chives and provide data for the prediction and prevention of food-borne illnesses.

• Asia pacific journal of information systems
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• 제18권2호
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Steel and Composite Structures
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• 제29권6호
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• 대한약침학회지
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• 제27권2호
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• pp.142-153
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• 2024

Objectives: This study aimed to analyze the educational needs of interns and residents in Korean medicine as the first step in developing an education program to improve their research competencies. Methods: A mixed-method design, incorporating both quantitative and qualitative data collection methods, was used to investigate the educational needs for research competencies among interns and residents working in Korean medicine hospitals nationwide. Data were collected through online surveys and online focus group discussions (FGDs), and processed using descriptive statistical analysis and thematic analysis. The study results were derived by integrating survey data and FGD outcomes. Results: In total, 209 interns and residents participated in the survey, and 11 individuals participated in two rounds of FGDs. The majority of participants felt a lack of systematic education in research and academic writing in postgraduate medical education and highlighted the need for nationally accessible education due to significant disparities in the educational environment across hospitals and specialties. The primary barrier to learning research and academic writing identified by learners was the lack of knowledge, leading to time constraints. Improving learners' research competencies, relationship building, autonomy, and motivation through a support system was deemed crucial. The study also identified diverse learner types and preferred educational topics, indicating a demand for learner-centered education and coaching. Conclusion: This study provides foundational data for designing and developing a program on education on research competencies for interns and residents in Korean medicine and suggests the need for initiatives to strengthen these competencies.

• 복식문화연구
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• 제28권4호
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• pp.527-543
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• 2020

The aim of this study is develop a state-funded education and training curriculum to contribute to the development of the fashion industry infrastructure. This will be achieved based on the service sector by the competitive clothing sales personnel and fashion startups. The study was conducted using a qualitative research method. The participants were 20 representatives of fashion-related companies and employees from one traditional market and two fashion outlets in Seogu, Gwangju. Data was collected from September 2015 to January 2017 by demand surveys and in-depth interviews. These were conducted on the same day at each clothing store office. In addition, existing literature was also reviewed. The collected data were first summarized into 64 meaning units from which three themes were derived by arranging, classifying, and analyzing the data. The findings of the study are as follows. First, the education and training curriculum for fashion job creation is aimed at job-oriented field-types with the objective of cultivating professional skills for online to offline fashion professionals. Second, the curriculum for fashion advisors was developed to consisted of 8 courses of 150 hours, including job knowledge, a foreign language, fashion-specific knowledge, fashion marketing & VMD, store management know-how, clothing repair, field trip, and internship. Third, the curriculum for fashion entrepreneurs consisted of 8 courses of 106 hours, including entrepreneurship, fashion practice, startup, field trip, finance & taxation accounting, marketing, social enterprise course, and internship.

• 산업경영시스템학회지
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• 제40권4호
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• pp.46-54
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• 2017

'Consulting', which is the main research topic of the knowledge service industry, is a field of study that is essential for the growth and development of companies and proliferation to specialized fields. However, it is difficult to grasp the current status of international research related to consulting, mainly on which topics are being studied, and what are the latest research topics. The purpose of this study is to analyze the research trends of academic research related to 'consulting' by applying quantitative analysis such as topic modeling and statistic analysis. In this study, we collected statistical data related to consulting in the Scopus DB of Elsevier, which is a representative academic database, and conducted a quantitative analysis on 15,888 documents. We scientifically analyzed the research trends related to consulting based on the bibliographic data of academic research published all over the world. Specifically, the trends of the number of articles published in the major countries including Korea, the author key word trend, and the research topic trend were compared by country and year. This study is significant in that it presents the result of quantitative analysis based on bibliographic data in the academic DB in order to scientifically analyze the trend of academic research related to consulting. Especially, it is meaningful that the traditional frequency-based quantitative bibliographic analysis method and the text mining (topic modeling) technique are used together and analyzed. The results of this study can be used as a tool to guide the direction of research in consulting field. It is expected that it will help to predict the promising field, changes and trends of consulting industry related research through the trend analysis.