• Journal of the Korean Wood Science and Technology
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• v.49 no.4
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• pp.328-335
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• 2021

Considerable differences exist in the characteristics of size reduction and classification because of biomass species. Miscanthus sacchariflorus (M. sacchariflorus) Goedae-Uksae 1 is not used efficiently because of the imperfections of the processing technology for this biomass. Therefore, for the best use of specific biomass, improvement in the feedstock preparation of the biomass for processing, such as pellet manufacturing, is necessary. In this study, a laboratory-scale cone grinder and air classifier were designed and combined to investigate the performance of the combination system for M. sacchariflorus. The average equivalent spherical diameter of particles showed a close relationship with air velocity for air classification. The air velocity range to classify proper particles for pelletization was determined to be 6.0-6.8 m/s. The mass ratios of the collected particles to feed mass for four lengths of chopped M. sacchariflorus were 45.1%:46.1%, 39.1%:46.6%, and 44.1%:52.8% at the first, second, and third steps in simulating the multistep combination system, respectively.

• Journal of the Korean Society for information Management
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• v.14 no.2
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• pp.105-122
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• 1997

A library for the civil engineering field goes on increasing in quantity because of the growth in construction technology and the enlargement in applicable fields of civil engineering. Most of libraries and information centers in construction companies are using Dewey Decimal Classification (DDC) or Korean Decimal Classification (KDC) to classify a library in civil engineering field. It is necessary for the library classification system to be equipped with a more standardized code system, which corresponds to the academical and technical classification for the civil engineering works. This study analyzes the defects of existing classification systems, and then suggests a new classes and divisions classification system, which facilitates to link academic information with technical data, for the civil engineering field. The proposed system is expected to make practical application of information classification system in the construc ion industry and to be applied for the revised edition of KDC.

• KIEAE Journal
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• v.17 no.5
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• pp.51-59
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• 2017

Purpose: This study aims to set up the classification system for providing infill information and draw detailed infill information required by suppliers, thereby promoting the revitalization of long-life housing and utilizing such information as preliminary data for establishing web system, on which infill information required by users in the long-life housing design process are available. Method: For the method of study, the infill information classification system and detailed information were drawn through the analysis of existing building material information systems; and the survey targeting working-level personnel was carried out in order to verify the drawn information system. The results of this study can be summarized as follows. First, the hierarchical classification system (scheme) was selected by quoting the classification system by material type as infill type, after analyzing existing DB information systems and drawing the hierarchical classification system for infill. Second, the comparative analysis between infill was available to users for the detailed infill information of long-life housing, and the essential information and general information were selected for differentiating information. Results: First, the hierarchical classification system (scheme) was selected by quoting the classification system by material type as infill type, after analyzing existing DB information systems and drawing the hierarchical classification system for infill. Second, the comparative analysis between infill was available to users for the detailed infill information of long-life housing, and the essential information and general information were selected for differentiating information. Third, only approximately 30% of the survey respondents recognized the infill of long-life housing, but they did not recognize its difference from existing building materials. Fourth, through the analysis of paths to obtain infill information of long-life housing, it was confirmed that infill information was obtained mostly through books and research papers regarding long-life housing, followed by the existing information systems. The significance of the study lies in that it is differentiated from the previous information system as the information system specialized in the infill of long-life housing was established, and can be used as a measure to revitalize long-life housing market.

• Journal of Korean Library and Information Science Society
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• v.39 no.3
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• pp.345-368
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• 2008

This study investigated the general problems concerning the subject Computer Science in the KDC(Korean Decimal Classification) 4th edition based on the comparative analysis with DDC, NDC, Disciplinary Classification System of Korean Research Foundation and National Standard Science and Technology Classification and Science and Technology Classification of Korea Science and Engineering Foundation, and suggested some ideas for the improvements of them. The subject of Computer science in the KDC 4th edition will be helpful to be improved to integrate in classes 004-005 now separated into two main classes of 000(004-005) and 500(566) in KDC4, to systematize subdivisions, to add new subjects, to delete and relocate some inappropriate subjects and to add notes.

• Journal of Information Technology Services
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• v.18 no.5
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• pp.17-30
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• 2019

With the advent of the Fourth Industrial Revolution, virtual reality, a technology that has recently attracted attention, is emerging as a core technology that will lead the future industry by changing the paradigm of various industries. The development of 3D rendering, computer graphics, and mobile technologies enabled the development of various smart devices and led to the popularization of virtual reality services using them. Recently, with the development of virtual reality-related technology, various devices and contents such as VR-related HMDs are being developed and released. However, since the classification for VR technology has not yet been established, it is difficult to define a range of industries and services to which VR can be applied. Therefore, in this study proposes a service classification system in terms of industries that can apply VR technology and services that can be provided based on the studies on industries and services of VR technology related to the Fourth Industrial Revolution. VR's industrial classification consists of eight industries including entertainment, media, education, medical care, architecture, manufacturing, distribution, tourism and each service is divided into two service categories and composed 16 services. Through the collection and analysis of virtual reality service cases, the service distribution and characteristics of each industry can be analyzed. In addition, we can develop a virtual reality new business model and present a service case for the intersecting areas. This study is expected to be used as a basic research for the activation of virtual reality services in the future.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.59-71
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• 2019

In the rapidly changing industrial environment, the continuous increase in demand for entrepreneurship emphasizes the effective support of the government for the survival and growth of entrepreneurs and the necessity of establishing systematic initiative promotion policies. To this end, Of the total number of enterprises. The purpose of this study is to establish a new classification system for entrepreneurial industry that reflects the trend of entrepreneurship based on convergence technology that emerged during the 4th Industrial Revolution era in order to establish a systematic initiative upbringing policy. In this paper, we propose a new classification system for entrepreneurial ecosystem by using Delphi technique. As a result of the study, the categories of entrepreneurial industry are classified into technology entrepreneurship and general entrepreneurship. Technology entrepreneurship is divided into ICT services, ICT manufacturing, general manufacturing, cultural contents and biotechnology. The results of this study suggest a meaningful implication in the establishment of effective policies to support entrepreneurship in the future by establishing new standards of industry classification system of entrepreneurs.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.64-69
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• 2021

In this paper, a method for improving the defect classification performance in low contrast, ununiformity and featureless steel plate surfaces has been studied based on deep convolution neural network and transfer-learning neural network. The steel plate surface images have low contrast, ununiformity, and featureless, so that the contrast between defect and defect-free regions are not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. A classifier based on a deep convolution neural network is constructed to extract features automatically for effective classification of images with these characteristics. As results of the experiment, AlexNet-based transfer-learning classifier showed excellent classification performance of 99.43% with less than 160 seconds of training time. The proposed classification system showed excellent classification performance for low contrast, ununiformity, and featureless surface images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.285-307
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• 2009

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.