• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.101-110
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• 2023

Radiation and radioisotopes have a high value in terms of utilization that can be used in convergence with various fields. However, due to the specificity of radiation, the use of radiation and radioisotopes is more difficult than in other industrial fields and also involves complex regulations. There are no clear industrial technology standards in these fields. Therefore, the growth of the radiation industry, especially including small companies, is being delayed. Since industrial technology standards play an important role in providing an institutional basis for the continuous development and settlement of domestic technology, the development of technical standards for the radiation and radioisotope industries can lead to systematic growth of the domestic radiation industry. To this end, the technology classification of the radiation industry was promoted and classified into 7 major categories, and detailed classification was divided according to the characteristics of each technology. In addition, a demand and perception survey on the need for industrial technology standards was conducted on RI licensed institutions and companies, and as a result, 61.4% responded that it was necessary, and in particular, they recognized the need for radiation safety(63.3%). In this paper, the technical classification for the radiation field is presented as the first step in the development of industrial technical standards for the radiation industry. In addition, the plan of the current status information and preparation of standard procedures of each category will be discussed.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.381-388
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• 2015

The rapid growth of the World Wide Web and online information services has generated and made accessible a huge number of text documents. To analyze texts, selecting important keywords is an essential step. In this paper, we propose a feature selection method that combines a term frequency-inverse document frequency technique and symmetrical conditional probability. The proposed method can identify features with N-gram, the sequential multiword. The effectiveness of the proposed method is demonstrated through a real text data from the machine learning repository, University of California, Irvine.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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• pp.91-99
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• 2006

A POP system, which collects manufacturing data from the shop floors and supply them to higher level systems, should be maintained and upgraded according to the change of production environment such as new product introduction. This situation leads to the need of a cost-effective system development methodology. In this paper, a methodology based on the classification and the similarity comparison of manufacturing processes is proposed. In this, a new product is classified according to the similarity of its manufacturing processes, which enables recycling of existing system modules. The proposed methodology has been tested in the case of an electronics parts manufacturing company, where a POP system is implemented. The result shows that the proposed methodology can save time and efforts for system implementation.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.587-592
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• 2020

This paper analyzed the curriculum of computer and software-related majors educating the core IT-related skills needed for the 4th Industrial Revolution. The analysis was conducted on 158 majors classified as applied software, computer science and computer engineering according to the standard classification of university education units by the Standard Classification Committee of the Korean Council of University Education. The current status of introduction of curricular divided into the fields of Internet of Things(IoT) & mobile, cloud & big data, artificial intelligence(AI), and information security was analyzed among the contents of education in the relevant departments. According to the analysis, an average of 81.6% of the majors for each group of curricular organized related subjects into the curriculum. The Curriculum Response Index for the 4th industrial revolution(CRI4th) by major, calculated by weighting track operations by education sector, averaged 27.5 point out of 100 point. And the IoT & mobile sector had the highest score of 42.3 points.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.6
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• pp.128-140
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• 2003

The purpose of this research was to present characteristics for the classification of biotopes and classification method of biotopes as basic data for ecological landscape planning in Metropolitan Daegu. The results of this study were as follows. 1) The study identified fifteen characteristics for classification of biotopes. Ecological landscape characteristics were divided into structural and functional factors. There are six structural factors such an inclination, and nine functional factors such as temperature. 2) The study area was separated into sixty eight biotope types. For example, an industrial district was divided into two biotope types: a biotope type of an industrial district with abundant green space, and a biotope type of an industrial district with scarce green space. 3) In the result of cluster analysis using the average linkage method between groups, biotope groups were divided into fifteen clusters and biotope groups were divided into seven clusters. Each cluster was named according to the features of a descriptive statistics analysis. For example, cluster 8 was identified as a biotope type with an impermeable pavement rate of more than 90 percent and an afforestation rate under 10 percent. 4) Fifteen biotope groups were converted to land use patterns for remote application and utilization of urban biotope in city planning. Biotope groups of a building area beyond an intermediate floor with an afforestation rate under 20-30 percent was converted to a land use pattern such as a tall apartment complex or commercial district. When examining the characteristics that were established in this research, there was a limit to achieve the objective of grade-classification because of a lack of related basic data. The research of landscape ecological characteristics for the classification of biotopes could not be completed due to a lack of time and resources, thus the study of ecological landscape characteristics will be accomplished over time.

• Resources Recycling
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• v.27 no.2
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• pp.11-23
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• 2018

The need for remanufacturing automotive parts is required due to the depletion of resources, rising raw material prices and strengthening environmental regulations. For remanufacturing, stable supply and demand of core must be accompanied. At present, remanufacturing companies collect cores through various routes, but the recovery rate of cores from the End-of-Life Vehicles is low. If we can systematically collect cores from hundreds of thousands of ELVs that were generated each year, the recovery rate of the core for remanufacturing will be further improved. Therefore, in this paper, we tried to establish a classification system for the ELV as a method for collecting the cores from the ELV. First, we selected the elements affecting the classification and determined the scope for the evaluation. The final rating classification is established by calculating the weights among the influence elements. Finally, through the case study, the dismantling grade of the actual ELV was evaluated to derive the second grade.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.23-31
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• 2024

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.3
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• pp.345-352
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• 2014

Objectives: This study is aimed to describe the current situation about urinary biomarker N-methylformamide(NMF) for workers exposed to N,N-dimethylformamide(DMF) according to industrial classification. Materials: Special health examination records of the workers who had undergone urinary biological monitoring in 2013 were collected. The numbers and percentage of workers, whose urinary NMF values were above the limit of detection(LOD) and above the biological exposure index(BEI) were calculated. Health relatedness with DMF as judged by their doctors was also described. All description was classified according to the $9^{th}$ Korean Standard Industrial Classification(KSIC). Results: It appeared that most workers exposed to DMF belong to manufacturing section(80.7%). The geometric mean(GM) values of urinary NMF were 6.25 mg/L, 3.54, and 3.86 for the manufacturing section, professional, scientific and technical activities section, and for the construction section respectively. In detail, it seemed that division of textiles(except apparel) (GM 7.51 mg/L), division of leather, luggage and footwear(11.59 mg/L), and division of rubber and plastic products(6.89 mg/L) were highly exposed to DMF with a high percentage of workers with urinary NMF values above BEI. This was probably due to the effect of skin absorption that the division of leather, luggage and footwear showed the highest urine NMF GM. Conclusions: It seemed that workers in manufacture industries such as textile, leather, luggage, footwear, rubber and plastic products were highly exposed to DMF. So, efforts should be focused on those industries in order to effectively diminish worker's exposure. Further studies to compare DMF air-monitoring with bio-monitoring according to industrial classification should be considered.