• International journal of advanced smart convergence
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• 제9권4호
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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.

• 방사선산업학회지
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• 제17권1호
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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.

• 대한산업공학회지
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• 제41권4호
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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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• 제12권2호
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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.

• 산업경영시스템학회지
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• 제29권4호
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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.

• 한국전자통신학회논문지
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• 제15권3호
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• pp.587-592
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• 2020

본 논문은 4차 산업 혁명에 필요한 핵심 IT 관련 기술을 교육하는 컴퓨터 및 소프트웨어 관련 전공의 교육과정을 분석하였다. 분석은 대학교육협의회 표준분류위원회의 대학교육 편제단위 표준분류에 따라 응용 소프트웨어와 컴퓨터과학 및 컴퓨터공학으로 분류 된 158개 전공을 대상으로 수행하였다. 해당 학과의 교육내용 중 사물인터넷과 모바일, 클라우드와 빅데이터, 인공지능, 그리고 정보보안의 분야로 구분한 교과과정의 도입 현황을 분석하였으며, 분석결과 각 교육과정 군에 대해 평균 81.6%의 전공이 관련 교과목을 교과과정에 편성한 것으로 나타났다. 교육분야별 트랙 운영 등에 가중치를 부여하여 계산한 전공별 4차산업혁명 대응지수는 100점 만점에 평균 27.5로 나타났으며, 사물인터넷 및 모바일 분야가 42.3점으로 가정 높게 나타났다.

• 한국조경학회지
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• 제30권6호
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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.

• 자원리싸이클링
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• 제27권2호
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• pp.11-23
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• 2018

자원고갈, 원자재 가격상승 그리고 환경규제 강화 등의 원인으로 자동차 부품의 재제조 필요성이 요구되고 있다. 재제조를 위해서는 코어의 안정적인 수급이 수반되어야 한다. 현재 재제조 기업은 다양한 경로를 통하여 코어를 회수하고 있지만, 폐자동차에서 발생하는 코어의 회수율은 저조한 편이다. 매년 수십만대가 발생하는 폐자동차에서의 코어를 체계적으로 회수할 수 있다면 재제조를 위한 코어의 회수율을 더욱 향상시킬 수 있을 것이다. 따라서 본 논문에서는 폐자동차에서 발생하는 코어를 회수하기 위한 방안으로 폐자동차에 대한 등급분류체계를 확립하고자 하였다. 우선 등급 분류에 영향을 주는 요소들을 선정하고 평가를 위한 범위를 결정하였다. 다음 영향요소들 간의 가중치를 산정하여 최종 등급 분류 체계를 구축하였다. 마지막으로 사례연구를 통해 실제 입고된 폐자동차의 해체 등급을 평가하여 2등급의 판정을 도출하였다.

• 대한안전경영과학회지
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• 제26권2호
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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.

• 한국산업보건학회지
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• 제24권3호
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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.