• 수산해양교육연구
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• 제18권3호
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• pp.341-363
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• 2006

All industries must be cope with fast technological progress along with the economic changes experience. However, a fishery and ocean industry are something yet to study base data for HRD and classifying occupation. Therefore, this study points to major problems which related useful data of information on the fishery and ocean industry. The purpose of this study is to classify fishery and ocean occupations by KSCO. The study is carried out though review of the literature, field investigation, direct interview and an experts' meeting of 5 field majors. A proposed classification of fishery and ocean occupations is modified on several times by the meeting of experts' group. Finally, a systematic classification of fishery and ocean occupations is as follows. First of all, first rank change from fishing to fishery industry. And the second rank, fishery and ocean occupations were classified into four categories bases on the systematic and comprehensive, as it were production fisheries, fishery products and processing, fishery supplies and infrastructure, fishery services. Each rank of classifying occupation is from two to four steps based on the occupation cluster.

• 한국산학기술학회논문지
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• 제18권3호
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• pp.185-198
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• 2017

2000년대 이후 국내 제조업 성장률은 지속적으로 하락하고 있으며, 매출 및 고용이 감소하고 있는 추세이다. 주요 선진국은 제조업 경쟁력 강화를 위한 다양한 전략을 수립하여, ICT 기술과의 융합을 통한 제조업 혁신을 도모하고 있다. 제조업 혁신의 핵심은 제품 설계 단계에서 모델링 및 시뮬레이션(M&S) 기술을 통해 신제품 개발 시간 및 비용을 절감하는 것이다. M&S 산업은 산업 가치사슬의 상위분야에 속하며, 타 산업으로의 파급효과가 큰 업종이다. 그러나 국내 M&S 기업의 역량은 선진국 대비 매우 취약한 상황이며, M&S 기업의 정의 및 분류조차 이루어지지 않고 있다. 본 논문에서는 한국표준산업분류를 분석하여 M&S 서비스 기업들이 포함되는 5개의 산업 세세분류를 도출하고, 11,822개의 관련 기업 중 3단계의 선정 절차에 따라 307개의 M&S 서비스 기업을 도출하였다. 이중 211개의 M&S 서비스 기업 현황을 조사하여 국내 M&S 기업의 역량을 분석하였다. 제조업에 대한 의존도가 높은 우리 경제의 특성을 고려할 때, 향후 지속적인 경제성장을 위해서는 고급 두뇌산업인 국내 M&S 서비스의 역량 향상 및 생태계 조성을 통한 제조업의 경쟁력 강화가 필요하다.

• 한국비블리아학회지
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• 제7권1호
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• pp.193-230
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• 1994

The systematic classification of database is much debated issue currently in telecommunication industry. Nevertheless, the attempt to build the systematic model is nowadays nowhere to be found. The purpose of this study is to gain a general overview relating to this subject and to make out a draft for the development of standard model. Relating th the study for the databases classification, it was classified from the 9 points of view： manufacturer, subject, processed form (level), (re)presented form, language, completion state and updating cycle, retrieval method, communication media, and use.

• 한국레이저가공학회지
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• 제5권3호
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• pp.21-30
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• 2002

In automotive industry, the studies about light weight vehicle and improving the productivity have been accomplished. For that, TRIP steel was developed and research for the laser welding process have been performed. In this study, the monitoring system using photodiode was developed for laser welding process of TRIP steel. With measuring light, neural network model for estimating bead width and tensile strength was made and weld quality classification algorithm was formulated with fuzzy inference method.

• 한국산업융합학회 논문집
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• 제23권2_2호
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• pp.163-171
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• 2020

The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

• 디지털산업정보학회논문지
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• 제12권4호
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• pp.33-40
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• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• 디지털산업정보학회논문지
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• 제14권4호
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• 디지털산업정보학회논문지
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• 제17권1호
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• pp.7-14
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• 2021

With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.219-220
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• 2021

It is considered that the construction industry is one of the pivotal players in the national economy in terms of Gross Domestic Production (GDP) and employment. Behind the positive role of this industrial sector to the national economy, the construction industry generates approximately 50 % of the total waste generation from all the industrial sectors. There are several measures to mitigate the adverse impacts of the construction waste such as reduce, reuse and recycle. Recycling would be one of the effective strategies for waste minimisation, which would be able to reduce the demand upon new resources as well as enhance reusing the construction materials on sites. The automated construction waste classification system would make it possible not only to reduce the amount of labour input but also mitigate the possibility of errors during the manual classification process. In this study, we proposed an automated waste segmentation and classification system for recycling the construction and demolition waste in the real construction site context. Since the practical application to the real-world construction sites was one of the significant factors to develop the system, a YOLACT (You Only Look At CoefficienTs) algorithm was chosen to conduct the study. In this study, it is expected that the proposed system would make it possible to enhance the productivity as well as the cost efficiency by reducing the manpower for the construction and demolition waste management at the construction site.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.