• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.437-454
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• 2021

This study investgates Korean water technology through the water market perspective and analyses its competitiveness. Based on the water technology classification, water technology competitiveness is analysed through the technological influence index and market dominance index which are based on the extracted water technology patents from the US, Europe, Korea, and Japan for the last decade. As a result, the Korean water technology patents were lack in influence and competitiveness in global market considering the large volume of patents. There are two most tech-influential industries in Korea; manufacturing industry consisting pipes, sterilization, disinfection, and advanced water purification equipment, and construction industry including seawater desalination and water resource development. Due to the domestic usage of the patents, the Korean water technology patents scored low in global market PFS(Patent Family Size) index compared to their CPP(Cites Per Patent) index. The study is meaningful in a way that the analysis on Korean water technology competitiveness using water technology classification system and patent analysis was conducted based on the perspective of the global water market.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.792-799
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• 2023

Non-reactor nuclear facilities are increasing remarkably in Korea combined with advanced technologies such as life and space engineering, and the diversification of the nuclear industry. However, the absence of a basic classification guideline related to the design of non-reactor nuclear facilities has created confusion whenever related projects are carried out. In this paper, related domestic and international technical guidelines are reviewed to present the classification criteria of non-reactor nuclear facilities in Korea. Based on these criteria, the classification of structures, systems and components (SSCs) for safety controls is presented. Using the presented classification criteria, classification of a hot cell facility, a representative non-reactor nuclear facility, was performed. As a result of the classification, the hot cell facility is classified as the hazard category 3, accordingly, the safety class was classified as non-nuclear safety, the seismic category as non-seismic (RW-IIb), and the quality class as manufacturers' standards (S).

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.1
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• pp.39-49
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• 2019

Acoustics are increasingly regarded as a remote-sensing tool that provides the basis for classifying and mapping ocean resources including seabed classification. It has long been understood that details about the character of the seabed (roughness, sediment type, grain-size distribution, porosity, and material density) are embedded in the acoustical echoes from the seabed. This study developed a sophisticated yet easy-to-use technique to discriminate seabed characteristics using a split beam echosounder. Acoustic survey was conducted in Tongyeong waters, South Korea in June 2018, and the verification of acoustic seabed classification was made by the Van Veen grab sampler. The acoustic scattering signals extracted the seabed hardness and roughness components as well as various seabed features. The seabed features were selected using the principal component analysis, and the seabed classification was performed by the K-means clustering. As a result, three seabed types such as sand, mud, and shell were discriminated. This preliminary study presented feasible application of a sounder to classify the seabed substrates. It can be further developed for characterizing marine habitats on a variety of spatial scales and studying the ecological characteristic of fishes near the habitats.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.129-136
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• 2023

In this paper, we describe a rule-based risk classification algorithm to perform Risk-based Inspection (RBI) on imported goods at customs. The RBI system is a method to automatically select which cargos have to be inspected and manage potential risks in boarder. In this study, we designed a rule-based risk classification algorithm for RBI solutions and implemented them using the Svelte web application framework. The risk classification algorithm proposed in this paper uses different indicative risk factors such as HS code, country of origin, importer's reliability, trade relationships, and logistics routes to classify cargos into Green, Yellow, and Red channels. To achieve this, we assigned risk categories to each risk factor and randomly generated risk scores within a specific range for each risk category. This system is expected to contribute to the increased efficiency of customs operations and protect public safety by minimizing the risk of imported hazardous materials.

• 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.