• Journal of the Korean Society for information Management
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• v.15 no.3
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• pp.45-71
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• 1998

There is increasing importance of a system to reorganize explosive expansion of internet information resources efficiently; therefore, an increasing concern about classification system as an instrument for facilitating an access to a specific subject and improving efficiency in information retrieval. Comparing the hierarchical structure and access methodology of internet-based classification system with those of library classification such as Dewey Decimal Classification through their structural aspects and retrival process, this paper proposes the proper classification system in internet environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1089-1098
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• 2018

In this paper, we propose an autonomous feeding robot and its obstacle classification system using ultrasonic sensors to secure the driving safety of the robot and efficient feeding operation. The developed feeding robot is verified by operation experiments in a cattle shed. In the proposed classification algorithm, not only the maximum amplitude of the ultrasonic echo signal but also two gradients of the signal and the variation of amplitude are considered as the feature parameters for object classification. The experimental results show the efficiency of the proposed classification method based on the Support Vector Machine, which is able to classify objects or obstacles such as a human, a cow, a fence and a wall.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.169-180
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• 2021

Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.

• International Commerce and Information Review
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• v.6 no.3
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• pp.41-61
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• 2004

Today, most companies are needed to become interested on e-Biz and information management system. Especially, Data classification format system was very important for application to effective and efficiency management decision support. They should include main entry which consists of department, employee's name, title, publication date. Now, each company is using eleven different methods on data classification format system. In this paper finding result was as follows, in other words, general management document case using the nine date classification methods and special report management document ca se using the twodata classification methods. The aim of this study is to investigate problems that the present data classification format system has and some concerns that should be taken into account in case of the modification of the data classification system and change into a new one. This study is based on the survey in that the company managergave to 35 companies throughout the nation. As a result, the survey indicates that the crucial concerns of the participating managers are ineffective management information source and the duplication of data classification systems. This paper is the transcendental study the introduction of data classification format systems to business companies in Korea. This paper provided the fundamental data for the effective business process reengineering in business activity for management information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.1998-2014
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• 2016

In recent years, cloud computing services based on smart phones and other mobile terminals have been a rapid development. Cloud computing has the advantages of mass storage capacity and high-speed computing power, and it can meet the needs of different types of users, and under the background, mobile cloud computing (MCC) is now booming. In this paper, we have put forward a new classification-based virtual machine placement (CBVMP) algorithm for MCC, and it aims at improving the efficiency of virtual machine (VM) allocation and the disequilibrium utilization of underlying physical resources in large cloud data center. By simulation experiments based on CloudSim cloud platform, the experimental results show that the new algorithm can improve the efficiency of the VM placement and the utilization rate of underlying physical resources.

• Steel and Composite Structures
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• v.37 no.2
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• Journal of Technology Innovation
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• v.3 no.1
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• pp.213-236
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• 1995

Advanced Manufacturing Technology(AMT), a comprehensive collection of new technologies for the efficiency and flexibility of manufacturing systems has received a growing attention recently, AMT consists of various industrial and technological components, homogeneous in some aspects while heterogeneous in others. Thus, it is difficult but necessary task to construct a classification framework in which the relationship among individual technologies are depicted in a meaningful fashion. In this, paper, we propose a hierarchical framework in which the objective and criteria of classification are decomposed into three level: industrialization, development and application of AMT. At the first and highest level, the main interest is to "industrialize" AMT. The major actors at this level are policy makers(public sector) and top management(private sector) and the primary classification criterion is the interrelationship between industry and technology. At the middle level exist system engineers whose main objective is to "develop" new technologies and/or systematize individual technologies. At the final and bottom level, shop floor managers need to "apply" AMT in order to enhance the efficiency and flexibility of manufacturing process. It should be stressed that, as a whole, the above three levels should be interactively linked to that each level contributes to the balanced development of AMT.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.85-91
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• 2021

Aggregate waste separator with rotating separating net was designed for applying classification process of construction waste. In order to evaluate the performance of the aggregate waste separator, according to the type of waste, standardized waste samples are prepared using acrylic. The appropriate operating point was evaluated by the classification efficiency and misclassification rate of recycled aggregate according to the control frequency of the blower operating and inlet position of the separating net. The classification efficiency at the operating point of the aggregate waste separator was evaluated through flow analysis assuming recycled aggregate and waste sample as particles. As a result of the performance test, when the distance. between the conveyor belt and the inlet was 0.2m, the classification efficiency was 95%, but the misclassification rate of recycled aggregate was 2% or more, which satisfies the classification efficiency and the misclassification rate of less than 2%. The operating point was shown at a control frequency of 58Hz at a suction distance of 0.254m. As a resu lt of flow analysis, there was no misclassification of recycled aggregate. In order to redu ce constru ction waste in the existing recycled aggregate production process, adsorption system using a rotating separating net that can be operated as an installation type was built.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.57-68
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• 2005

Vehicle classification system in Korea is operated by two different types depending on operating purpose and place. 8-category classification system operates in Expressway and Provincial road, and 11-category classification system operates in National highway. These different operations decrease the efficiency of practical use of gathering data. Therefore, this study proposes new-modified vehicle classification system for solving this problem. For classification, this study not only focuses on mechanic survey system which is based on vehicle specs, it's also focuses on the applicability of roadside survey. This proposed classification system considers the tendency to vary of vehicle types, and the compatibility with the other classification systems. This system might be the most suitable system for our present situation.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.709-726
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• 2018

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.