• The KIPS Transactions:PartD
• /
• v.12D no.6 s.102
• /
• pp.829-836
• /
• 2005

The way to assign a splitting criterion for correct object classification is the main issue in all decisions trees. This paper describes new splitting rules for classification in order to find an optimal split point. Unlike the current splitting rules that are provided by searching all threshold values, this paper proposes the splitting rules that we based on the probabilities of pre assigned intervals. Our methodology provides that user can control the accuracy of tree by adjusting the number of intervals. In addition, we applied the proposed splitting rules to a set of image data that was retrieved by parameterized feature extraction to recognize image objects.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.2
• /
• pp.133-138
• /
• 2002

In this paper, we propose an integrated GUI environment of parallel fuzzy inference system fur pattern classification of remote sensing data. In this, as 4 fuzzy variables in condition part and 104 fuzzy rules are used, a real time and parallel approach is required. For frost fuzzy computation, we use the scan line conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. We design 4 fuzzy processor unit to be operated in parallel by using FPGA. As a GUI environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be used in a pattern classification system requiring a rapid inference time in a real-time.

• Journal of Korea Technology Innovation Society
• /
• v.9 no.3
• /
• pp.496-513
• /
• 2006

Ministry of Science and Technology(MOST) has a plan to establish the National Science and Technology Information System(NTIS). For successful NTIS, there are three pre-standardizations. Standard classification is the one of the three standardizations. In this paper, the validity of current National Standard Classification of Science and Technology(NSCST) is analyzed for three aspects. One is the duplication of NSCST, one another is the high ratio of incorrectness of information changed by mapping table and the last is the incorrectness of the mapping table itself. So for the successful NTIS a new Science and Technology Classicication shoud be considered.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06c
• /
• pp.26-30
• /
• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed our approach consists of three stages: (i) data pre-processing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

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

• Journal of Broadcast Engineering
• /
• v.24 no.3
• /
• pp.430-439
• /
• 2019

The CNN (convolutional neural network) based single degradation restoration method shows outstanding performance yet is tailored on solving a specific degradation type. In this paper, we present an algorithm of multi-degradation classification and restoration. We utilize the CNN based algorithm for solving image degradation classification problem using pre-trained Inception-v3 network. In addition, we use the existing CNN based algorithms for solving particular image degradation problems. We identity the restoration order of multi-degraded images empirically and compare with the non-reference image quality assessment score based on CNN. We use the restoration order to implement the algorithm. The experimental results show that the proposed algorithm can solve multi-degradation problem.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.2
• /
• pp.569-574
• /
• 2023

For maintenance of concrete structures, it is necessary to identify and maintain various defects. With the current method, there are problems with efficiency, safety, and reliability when inspecting large-scale social infrastructure, so it is necessary to introduce a new inspection method. Recently, with the development of deep learning technology for images, concrete defect classification research is being actively conducted. However, studies on contamination and spalling other than cracks are limited. In this study, a variety of concrete defect type classification models were developed through transfer learning on a pre-learned deep learning model, factors that reduce accuracy were derived, and future development directions were presented. This is expected to be highly utilized in the field of concrete maintenance in the future.

• Journal of the Korean housing association
• /
• v.19 no.3
• /
• pp.31-38
• /
• 2008

Recently, Seoul residential real estate market showed a big change, especially in 2007. The residential property price in Seoul had been mainly affected by 5 provideces: Kangnam-gu, Seocho-gu, Songpa-gu, Gangdong-gu and Yangchun-gu, but these providences started to show the decrease in price while the other providences ironically showed the opposite direction. Therefore, this project was derived from this phenomenon recognition and the necessity as the new market trend requires. The pre-research was carried out with the point of social-population academic view, but this project provides the analysis on the new market trend by simplifying the complex valuation indexes, originated from the pre-research. In result, the aspects of the change could be categorized into time-manner classification and territorial-manner classification, in cope with the change in the satisfaction degree on the residential environment and the selection tendency of the residential property. Based on the the moving-preferred area criteria, the territorial classification was categorized into 3 areas: 5 providences, which showed the initial decrease in real estate price (area 1), the other Kangnam area (area 2), and Kangbuk area (area 3). The result illustrated the reasonable change in the satisfaction degree on the residential environment and the selection tendency of the residential property. This project was able to reach the following conclusion : Firstly, the housing development planning should be devised by the residential environment, including the view and the natural environment, not by the area. Secondly, the housing development planning in the other Kangnam area (area 2) and Kangbuk area (area 3) should embrace the business function, not the housing development only. Last, the housing development planning in Kangbuk area (area 3) should be able to enhance education and culture function and be connected by various transportation system. This project analyzes the change in the satisfaction degree on the residential environment and the selection tendency of the residential property. Thereafter, this project has the purpose of providing the aid in understanding of the basis of housing development information.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.27-33
• /
• 2021

The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.519-519
• /
• 2023

Estuarine dams are a recent and global phenomenon. While estuarine dams can provide the benefit of improved freshwater resources, they can also alter estuarine processes. Due to the wide range of estuarine types and estuarine dam configurations, the effect of estuarine dams on estuaries is not well understood in general. To develop a systematic understanding of the effect of estuarine dam location and freshwater discharge interval on a range of estuarine types (strongly stratified, partially mixed, periodically stratified, and well-mixed), this study used a coupled hydrodynamic-sediment dynamic numerical model (COAWST) and compared flow, sediment transport, and morphological conditions in the pre- and post-dam estuaries. For each estuarine type, scenarios with dam locations at 20, 55 and 90 km from the mouth and discharge intervals of a discharge every 0.5, 3, and 7 days were investigated. The results were analyzed in terms of change in tide, river discharge, estuarine classification, and sediment flux mechanism. The estuarine dam location primarily affected the tide-dominated estuaries, and the resonance length was an important length scale affecting the tidal currents and Stokes return flow. When the location was less than the resonance length, the tidal currents and Stokes return flow were most reduced due to the loss of tidal prism, the dead-end channel, and the shift from mixed to standing tides. The discharge interval primarily affected the river-dominated estuaries, and the tidal cycle period was an important time scale. When the interval was greater than the tidal cycle period, notable seaward discharge pulses and freshwater fronts occurred. Dams located near the mouth with large discharge interval differed the most from their pre-dam condition based on the estuarine classification. Greater discharge intervals, associated with large discharge magnitudes, resulted in scour and seaward sediment flux in the river-dominated estuaries, and the dam located near the resonance length resulted in the greatest landward tidal pumping sediment flux and deposition in the tide-dominated estuaries.