• Journal of Intelligence and Information Systems
• /
• v.23 no.1
• /
• pp.47-67
• /
• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.39 no.4
• /
• pp.50-65
• /
• 2021

In this study, the water flow system by measuring the flow-way type and distance of flow path that composes the Gugok through literature survey, field survey, and map work on Gugok gardens in Korea whose existence has been confirmed, while investigating and analyzing watersheds, river orders, and river grades. It was intended to reveal the watershed distribution and stream morphological characteristics of the Gugok gardens and to use them as basic data for future enjoyment and conservation of the Gugok gardens. The conclusion of the study is as follows. First, Of the 93 Gugok gardens that have been confirmed to exist, it was found that 11 places(11.8%) were found to have a descending(top-down) type of Gugok that develops while descending along a stream. Second, As a result of analysis of the length of the flow path for each valley, Okryudonggugok(玉流洞九曲, Namsan-gugok) in Gimcheon, Gyeongsangbuk-do was found to have the shortest length of 0.44km among the surveyed valleys, while the flow distance of Muheulgugok(武屹九曲) located in Seongju-gun and Gimcheon-si, Gyeongsangbuk-do was 31.1km, showing the longest flowing distance. The average flow path length of the Gugok Garden in Korea was 6.24km, and the standard deviation was 4.63km, indicating that the deviation between the 'curved type'e and the 'valley type' was severe. In addition, 14(15.1%) Gugok gardens were found to be partially submerged due to dam construction. Third, As a result of analyzing the waters area where Gugok garden is located, the number of Nakdong river basins was much higher at 52 sites(55.9%), followed by the Hangang river basin at 27 sites(28.7%), the Geum river basin at 9 sites(9.7%), and the Yeongsan river and Seomjin river basins at 5(5.4%). Fourth, All Gugok gardens located in the Han river region were classified as the Han river system, and the Gugok garden located on the Nakdong river was classified as the main Nakdong river system, except for 7 places including 5 places in the Nakdong Gangnam Sea water system and 2 places in the Nakdong Gangdong sea water system. As a result of synthesizing the river order of the flow path where Gugok garden is located, Gugok, which uses the main stream as the base of Gugok, is 3 places in the Hangang water system, 5 places in the Nakdong river system, 2 places in the Geumgang water system, and 1 place in the Yeongsangam/Seomjin river system. A total of 11 locations(11.5%) were found, including 36 locations(38.2%) in the first branch, 29 locations(31.2%) in the second branch, and 16 locations(17.0%) in the third branch. And Gugok garden, located on the 4th tributary, was found to be Taehwa Five-gok(太華五曲) set in Yonghwacheon Stream in Cheorwon in the Han river system, and Hoenggyegok(橫溪九曲) in Yeongcheon Hoenggye Stream in the Nakdong river system. Fifth, As a result of the river grade analysis of the rivers located in the Gugok garden Forest, the grades of the rivers located in the Gugok garden were 13 national rivers(14.0%), 7 local first-class rivers(7.5%), and 74 local second-class rivers(78.5%) was shown.