• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.81-85
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• 2014

This paper investigates the challenges and outlook of textbook teaching and learning material industry. 1. The challenges of teaching and learning material industry. First, classification criteria are needed. Second, Evaluation Standard are needed. Third, quality control is needed. Fourth, ready-made products are insufficient. Fifth, economic policy for teaching and learning material industry is required. Sixth, manage system for teaching and learning material is necessary. Seventh, distribution system for teaching and learning material should be reformed. 2. The outlook of teaching and learning material industry. First, accreditation system will be introduced. Second, teaching and learning material industry will develop continually. Third, eco-friendly and sustainable system will be built. Fourth, multimedia industry will be extended. Fifth, managing system to enhance usage rate will be settled.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.243-250
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• 2011

In this study, we examine the changes of the freight transport business penetration system and its classification through the theoretical study. By searching case studies of related systems in developed countries, this thesis derives problem and suggests improvement.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1379-1383
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• 2005

Introducing the market into the electricity industry lets the multiple participants get into new competition. These multiple participants of the market need new business strategies for providing value added services to customer. Therefore they need the accurate customer information about the electricity demand. Demand characteristic is the most important one for analyzing customer information. In this study load profile data, which can be collected through the Automatic Meter Reading System, are analyzed for getting demand patterns of customer. The load profile data include electricity demand in 15 minutes interval. An algorithm for clustering similar demand patterns is developed using the load profile data. As results of classification, customers are separated into several groups. And the representative curves for the groups are generated. The number of groups is automatically generated. And it depends on the threshold value for distance to separate groups. The demand characteristics of the groups are discussed. Also, the compositions of demand contracts and standard industrial classification in each group are presented. It is expected that the classified curves will be used for tariff design, load forecasting, load management and so on. Also it will be a good infrastructure for making a value added service related to electricity.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.617-627
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• 2021

In the field of on-road motor vehicles, the level for autonomous driving technology is defined according to J3016, proposed by Society of Automotive Engineers (SAE) International. However, in the field of agricultural machinery, different standards are applied by country and manufacturer, without a standardized classification for autonomous driving technology which makes it difficult to clearly define and accurately evaluate the autonomous driving technology, for agricultural machinery. In this study, a method to classify the autonomy levels for autonomous agricultural machinery (ALAAM) is proposed by modifying the SAE International J3016 to better characterize various agricultural operations such as tillage, spraying and harvesting. The ALAAM was classified into 6 levels from 0 (manual) to 5 (full automation) depending on the status of operator and autonomous system interventions for each item related to the automation of agricultural tasks such as straight-curve path driving, path-implement operation, operation-environmental awareness, error response, and task area planning. The core of the ALAAM classification is based on the relative roles between the operator and autonomous system for the automation of agricultural machines. The proposed ALAAM is expected to promote the establishment of a standard to classify the autonomous driving levels of self-propelled agricultural machinery.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.262-269
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• 2008

Chemical products have had an favorable influence on our everyday life, and contributed very much to the development of human culture. According to the rapid change of industry and the development of scientific technique the using chemical products are increasing more and more. Chemical products can have any hazardous property such as flammability or explosiveness. There are occurring many accidents in the international trade due to the different classification and labelling of chemicals produced in various countries. The main purpose of this work is the development of global standard test methods for the chemicals, and the classification and labelling in building block approach by means of the basic technical data. Oxidizing solids, combustible solids, spontaneously combustible materials, water-prohibitive materials, flammable liquids, self-reactive materials and oxidizing liquids have been tested. The results have been classified according to the hazard material safety regulation and the UN regulation, and summarized in a data-base.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.35-43
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• 2020

The National Competency Standards(NCS) has been developed a good curriculum to reflect the demand in the industry. NCS is defined as systemizing the knowledge, skill, and attitude required to perform a task in the industry. It is an effort to reduce the gap between educational requirements and occupational requirements. However, when NCS is applied, the task level is changed to the title of qualification according to the policy outcomes for the government project. And, the NCS has been emphasizing the purpose of license acquisition, rather than using the standard for tasks in the industry. It means that the misuse of NSC hinders utilizing it as the original purpose of standardizing a task. It requires the customer to study the required competency unit with a no-choice option. Moreover, it makes it hard for companies or industry autonomically run the NCS. Therefore, we define the operational range in terms of the education field, the qualification field, and the career field to diffuse and apply NCS, and suggest the effective utilization method.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.208-216
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• 2020

The Integrated Offshore Standard Specification (IOSS) involves Korean shipyards, classification societies, research institutes, the Korean industrial society, engineering companies, and oil companies with the objective of reducing costs and risks without compromising safety in international offshore engineering procurement construction (EPC) projects using new standardized bulk components and qualification procedures. The activities of the IOSS include the analysis of the existing rules and regulations to achieve the best standardization, which is reflected in the best practices, and minimize the variables in regulations and rules. In addition, a standard inventory of shapes and dimensions, referred to as specifications, is proposed in the IOSS. In this paper, the aluminum tertiary standardization part (IOSS S102-1/2 S104: Specification for Structural Tertiary Design) is presented with the details of the procedures, background reviews, and cost-benefit analyses of the design and verification methods for standard designs in the IOSS standardization items. Based on the cost-benefit analysis, the application of standardized aluminum tertiary items to offshore projects has significant advantages in terms of maintenance and repair compared to the carbon steel tertiary items utilized in current industrial practices.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Fashion & Textile Research Journal
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• v.23 no.2
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• pp.226-236
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• 2021

The purpose of this study was to classify the torso shape based on abdominal protrusion caused by changes in the physical characteristics of middle-aged women. This study analyzed 3D shape data of 401 females ranging in age from 40 to 59 years who participated in the 6th Size Korea project. Based on the Size Korea 3D measurement standard, 27 additional items such as height, protrusion, and angle were measured in the 3D scan data. Nine factors were extracted from the analysis of constituent factors of the torso: "vertical size of torso," "flatness and protrusion of abdomen," "torso front extrusion," "upper body height," "bust size and flatness," "size of belly and angle of lower abdomen," "hip length," "hip flatness," and "horizontal size of bust." As a result of the cluster analysis using these nine factors, the torsos of middle-aged women were classified into three types. Type 1 has upper abdominal deposition with a small and long upper body and an advanced abdomen; type 2 has lower abdominal deposition with a small and short torso and a small belly and hip flexion; and type 3 has central abdominal deposition with a big and long torso, large breasts, and protruding abdo¬men front. The middle-aged women were mostly distributed in Type 2. The above results will be useful as basic data for the development of clothing with improved fit to accommodate the changed physical characteristics of middle-aged women.