• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.31-38
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• 2015

In this paper, we propose the ensemble-based classification model which extracts just new data patterns from the streaming-data by using clustering and generates new classification models to be added to the ensemble in order to reduce the number of data labeling while it keeps the accuracy of the existing system. The proposed technique performs clustering of similar patterned data from streaming data. It performs the data labeling to each cluster at the point when a certain amount of data has been gathered. The proposed technique applies the K-NN technique to the classification model unit in order to keep the accuracy of the existing system while it uses a small amount of data. The proposed technique is efficient as using about 3% less data comparing with the existing technique as shown the simulation results for benchmarks, thereby using clustering.

• Smart Media Journal
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• v.7 no.4
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• pp.37-43
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• 2018

Most of fruit quality classification has been done by time consuming, inaccurate and intensive manual labor. This study proposed an automated fruit grading system based on appearances and internal flavors. In this study, image processing technique and a weight checker were used to measure the value of appearance features and the near infrared spectroscopy analysis method was used to estimate the value of internal flavors. Additionally, I suggested 8x8x5x5 ANN based fruit quality classifier model to grade fruits quality. The proposed automated fruit quality classification system is expected to be very beneficial for many farms where heavy manual labor is usually needed for fruit quality classification.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.4
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• pp.65-77
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• 2007

Site response analyses were performed based on equivalent linear technique using shear wave velocity profiles of 162 sites collected around the Korean peninsula. The site characteristics, particularly the shear wave velocities and the depth to the bedrock, are compared to those in the western United States. The results show that the site-response coefficients based on the mean shear velocity of the top 30m ($V_{S30}$) suggested in the current code underestimates the motion in short-period ranges and overestimates the motion in mid-period ranges. The current Korean code based on UBC is required to be modified considering site characteristics in Korea for the reliable estimation of site amplification. From the results of numerical estimations, new regression curves were derived between site coefficients ($F_{a}\;and\;F_{v}$) and the fundamental site periods, and site coefficients were grouped based on site periods with reasonable standard deviations compared to site classification based on $V_{S30}$. Finally, new site classification system and modification of design response spectra are recommended considering geotechnical characteristics in Korea.

• IE interfaces
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• v.12 no.4
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• pp.551-556
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• 1999

In this paper, we compare the performance of three data mining classification algorithms(neural network, decision tree, logistic regression) in consideration of various characteristics of categorical input and output data. $2^{4-1}$. 3 fractional factorial design is used to simulate the comparison situation where factors used are (1) the categorical ratio of input variables, (2) the complexity of functional relationship between the output and input variables, (3) the size of randomness in the relationship, (4) the categorical ratio of an output variable, and (5) the classification algorithm. Experimental study results indicate the following: decision tree performs better than the others when the relationship between output and input variables is simple while logistic regression is better when the other way is around; and neural network appears a better choice than the others when the randomness in the relationship is relatively large. We also use Taguchi design to improve the practicality of our study results by letting the relationship between the output and input variables as a noise factor. As a result, the classification accuracy of neural network and decision tree turns out to be higher than that of logistic regression, when the categorical proportion of the output variable is even.

• Korean Institute of Interior Design Journal
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• v.22 no.1
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• pp.19-27
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• 2013

The objective of this study was to establish Facility classification for military barracks among military facilities. The military barracks are the place where soldiers spend most of their time. Thus, a new type of space in military barracks is required to improve the quality of life of the soldiers and make the military more advanced for national defense. The research method was to derive problems through a survey of the previous literature and case studies and to select target places in the Army, Navy, Air Force, and Marine based on the derived problems. An improvement scheme was proposed by developing criteria for military barracks spaces through a questionnaire survey. The following results were obtained: Facility classification inside of national defense military facility standard should be reorganized. The alternative plan is demanded for some camp which has no need about setting up the office facility. And the study of reasonable facility area after improvementing facility categorization is required.

• Korean Journal of Computational Design and Engineering
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• v.12 no.5
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• pp.382-394
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• 2007

Nowadays, manufacturing industries undergo constantly growing pressures for global competitions, and they must shorten time and cost in product development and production to response varied customers' requirements. Digital virtual manufacturing is a technology that can facilitate effective product development and agile production by using digital models representing the physical and logical schema and the behavior of real manufacturing systems including products, processes, manufacturing resources and plants. For successful applications of this technology, a digital virtual factory as a well-designed and integrated environment is essential. In this paper, we developed a new classification and coding system for effective managements of digital virtual factory objects, and implement a supporting application to verify and apply it. Furthermore, a digital virtual factory layout management system based on the classification and coding system has developed using XML, Visual Basic.NET and FactoryCAD. By some case studies for automotive general assembly shops of a Korean automotive company, efficient management of factory objects and reduction of time and cost in digital virtual factory constructions are possible.

• Journal of the Korean Society for information Management
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• v.15 no.2
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• pp.47-68
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• 1998

Although the research information resources on Internet are spread out on thousands of computers, it is not always easy to get them on the right time by the right manner. The purpose of this study is to use DDC(Dewey Decimal Classification) scheme in subject-based directory search engine for research information resourcees to aid retrieval on the Internet. For the design of classification code, this study followed 'systematic order' of DDC to arrange subjects from the general o the specific in a logical order, and for the design of classification dictionary, 'Relative Index' of DDC was used to bring together the various aspects of subjects.

• Tunnel and Underground Space
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• v.6 no.3
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• pp.197-208
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• 1996

Due to the constraints in pre site-investigation for tunnel, it is essential to redesign the support structures suitable for rock mass conditions such as rock strength, ground water and discontinuity conditions for safe tunnel construction. For the selection of optimum support, it is very important to carry out the rock mass classification and in-situ measurement in tunnelling. In this paper, in a mountain tunnel designed by NATM in hard rock, the selectable system for optimum support has been studied. The tunnel is situated at Chun-an in Kyungbu highspeed railway line with 2 lanes over a length of 4, 020 m and a diameter of 15 m. The tunnel was constructed by drill & blasting method and long bench cut method, designed five types of standard support patterns according to rock mass conditions. In this tunnel, face mapping based on image processing of tunnel face and rock mass classification by RMR carried out for the quantitative evaluation of the characteristics of rock mass and compared with rock mass classes in design. Also, in-situ measurement of convergence and crown settlement conducted about 30 m interval, assessed the stability of tunnel from the analysis of monitoring data. Through the results of rock mass classification and in-situ measurement in several sections, the design of supports were modified for the safe and economic tunnelling.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.88-98
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• 2001

in this paper, we imlemented design pattern retrieval system for efficient managemant and reusability of design patterns. Pattern is conssisted of property information and meta information id used for similarity measurement on classification and retrieval of patterns.Meta information od used for UML modeling of patterns. We classified design patterns with the empirical scope in addition to Gamma's basic classification. also we used E-SARM for retrieval represented UML diagram with pattern meta information, and simulated the environment so as to obtain best result on applying to retrieval of design pattern. This system is able ro resister new patterns through pattern viewer and manages these patterns with property informaiton and meta information. Thus this system supports efficient management of patterns, UML modeling, priority pattern retrieval, higher reusability and reduces pattern selection cost.