• Journal of Agricultural Extension & Community Development
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• v.22 no.2
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• pp.175-190
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• 2015

Most studies examining the sub-categories of agro-industry used to access an inter-industry analysis. However, These are some limitations that researchers set sub-categories differently according to their needs. Thus, This study aims to empirically explore the agro-industry sub-categories by area of academic research on agricultural science. The National Standard Science and Technology Classification(NSSTC) codes were used to classify academic research on agricultural science. This codes were examined the sub-categories using Korean input-output statistics industry and product classification by hiring 220 departments of 37 agricultural colleges. Results showed that studies using an inter-industry analysis coincided in terms of agricultural production, but showed differences in forward and backward linkage industries and services. Forward linkages industry were clearly limited to industries in which agricultural products are inputted as raw materials. Then, in terms of services related to agriculture, Previous studies represent fields such as transport and real estate, which are not included. Moreover, Research institutions overlooked by previous studies occupy an important position.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_1
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• pp.169-176
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• 2022

Prognostics and health management (PHM) is recently converging throughout the industry, one of the trending issue is to detect abnormal conditions at decanter centrifuge during water treatment facilities. Wastewater treatment operation produces corrosive gas which results failures on attached sensors. This scenario causes frequent sensor replacement and requires highly qualified manager's visual inspection while replacing important parts such as bearings and screws. In this paper, we propose anomaly detection by measuring the vibration of the decanter centrifuge based on the video camera images. Measuring the vibration of the screw decanter by applying the optical flow technique, the amount of movement change of the corresponding pixel is measured and fed into the LST M model. As a result, it is possible to detect the normal/warning/dangerous state based on LSTM classification. In the future work, we aim to gather more abnormal data in order to increase the further accuracy so that it can be utilized in the field of industry.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.1-5
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• 2011

In recent years, worldwide production of solar wafers increased rapidly. Therefore, the solar wafer technology in the developed countries already has become an industry, and related industries such as solar wafer manufacturing equipment have developed rapidly. In this paper we propose the color classification method of the polycrystalline solar wafer that needed in manufacturing equipment. The solar wafer produced in the manufacturing process does not have a uniform color. Therefore, the solar wafer panels made with insensitive color uniformity will fall off the aesthetics. Gaussian mixture models (GMM) are among the most statistically mature methods for clustering and we use the Gaussian mixture models for the classification of the polycrystalline solar wafers. In addition, we compare the performance of the color feature vector from various color space for color classification. Experimental results show that the feature vector from YCbCr color space has the most efficient performance and the correct classification rate is 97.4%.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.515-518
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• 2002

KM(Knowledge Management) is factor more paradigm of period than survival factor. Stewart, Sveiby etc., a scholar was Present to definition, knowledge classification and measurement method. KMS(Knowledge Management System) was made by scholar theory. But, it is hardly adapt to construction industry. Because it is have property that construction industry have one product, recieve-industry etc. Therefore, we must knowledge classification and measurement method that property of construction industry. So, we can effectively manage to knowledge of construction industry. And, knowledge of construction industry will evaluate according to property. Measure method of construction company will find through benchmarking that measure method of construction company is analyze to case.

• Proceedings of the CALSEC Conference
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• 2002.01a
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• pp.86-106
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• 2002

Informatization is a process that corporation's external environmental factors and internal environmental factors influence as complex. is a phenomenon that appears via this process. To evaluate that informatization was propeled well or informatization level is high can be dangerous work extremely by only once-over-lightly some factors, organization information ability is superior or infrastructure is constructed well. Therefore, an evaluation for industrial information systems that consider corporation's external environment and internal environment configurationally and objective estimation through this is required in national dimension. This research sorted types of business using types of business classification of 2001 EIII(Evaluation Indices of Industrial Informatization) laying stress on corporation's product and product production process for reflecting various industrial classification. And we are dividing whole our country corporations by manufacture industry, the construction industry, distribution industry, service industry, banking industry 5 types of business. To see such classed types industry classification from consistent viewpoint, we saw them within new framework, purchase, operation, physical distribution, marketing and sale. service etc. laying stress on primary businesses except support businesses of planning, financial management etc. To draw special quality of business center from primary business of each types of business, we draw industry classification Key Capability that centers when plans corporation's corporate strategy and information strategy. And we deducted industrial classification key production business connected with industry classification Key Capability. After drawing an evaluation items for industrial information systems in informatization analysis viewpoint laying stress on drawn businesses. Finally we did Case Study by making out an evaluation for industrial information systems questionnaire that considers special quality of manufacturing industry. Through EIII that consider the industrial classification, we could know that it explains the corporation's purchase, production, distribution in general and detail.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.45-52
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• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• Journal of Fisheries and Marine Sciences Education
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• v.20 no.1
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• pp.23-35
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• 2008

The purposes of this study are to analyze problems in industrial classification of fisheries in Korea and to suggest future directions. Based on a thorough review of relevant literature, the study proposes a five-level scheme for classifying fisheries. The highest level should be the fisheries industry, and the next highest level ought to be fisheries. The medium level should include fishing, aquaculture, and fishery service industries. At the fourth level, fishing is to be further divided into sea fishery and inland fishery, aquaculture into sea-surface aquaculture and inland aquaculture, and fishery service industries into integrated fishery service and fishery distribution service. The lowest level is the most detailed. At this level, sea fishery is split into deep sea fishery, offshore fishery, and coastal fishery; sea-surface aquaculture consists of sea aquaculture, seed production aquaculture, and food organism aquaculture; integrated fishery service is further classified into fishery-related service and fishery information service.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.55-65
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• 2020

With the development of display hardware, image interpolation techniques have been used in various fields such as image zooming and medical imaging. Traditional image interpolation methods, such as bi-linear interpolation, bi-cubic interpolation and edge direction-based interpolation, perform interpolation in the spatial domain. Recently, interpolation techniques in the discrete cosine transform or wavelet domain are also proposed. Using these various existing interpolation methods and machine learning, we propose decision tree classification-based image interpolation methods. In other words, this paper is about the method of adaptively applying various existing interpolation methods, not the interpolation method itself. To obtain the decision model, we used Weka's J48 library with the C4.5 decision tree algorithm. The proposed method first constructs attribute set and select classes that means interpolation methods for classification model. And after training, interpolation is performed using different interpolation methods according to attributes characteristics. Simulation results show that the proposed method yields reasonable performance.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.20-24
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• 2017

For process improvement and cutting down on expenses in solar cell industry, it is necessary to improve recycling process of wafer manufacturing. In this research, a study is introduced to develop classifier which is for recycling of abrasive. First of all, recycling process of wafer manufacturing is analyzed. And then, 3 steps of experiments such as oil removal, impurities removal and classification were executed. For the classification of slurry, a classifier is designed and manufactured. From experiments, it is verified that ultra sound vibration and flux are very important factors for classification. By experiencing the recycling processes and making devices, the technique can be initiated industry if needed such as decreasing waste and cutting down on expenses.