• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.3 s.106
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• pp.249-258
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• 2006

In this letter, we propose a novel printed helix antenna for RFID reader in UHF band. The printed strip line of the antenna is first wound up outside a polygonal shaped layer and then the winding continues on an inner layer to control the overall gain and the radiation pattern. In addition, the winding pitch angles on each layer have either negative or positive values resulting in the broad CP bandwidth. The detail structure of the antenna was optimized using Pareto genetic algorithm(GA), so as to obtain excellent performances for RFID reader antennas. The optimized two-layered polygonal helix was fabricated on the cardboard of a flexible substrate and the performances were measured and compared with the simulations. The fabricated antenna was made up of copper tape which can adhere to a flexible cardboard and had 21.4 % matching bandwidth, 31.9 % CP bandwidth, readable range of $5.5m^2$ with kr=3.2. Also based on the current distribution of the strip line of the antenna and sensitivity of the antenna bents points, we confirmed that the antenna has the quarter-wave transformer near the feed for the broad matching bandwidth and radiates the traveling wave for the broad CP bandwidth using the bent strip line.

• Journal of Environmental Impact Assessment
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• v.25 no.5
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• pp.345-356
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• 2016

In this study, auto-calibration method for water quality model was compared and analyzed using QUAL2Kw, which can estimate the optimum parameters through the integration of genetic algorithm and QUAL2K. The QUAL2Kw was applied to the Sum River which is greatly affected by the pollution loads of Wonju city. Two auto-calibration methods were examined: single parameter application for the whole river reach and separate parameter application for each reach of multiple reaches. The analysis about CV(RMSE) and fitness of the GA show that the separate parameter auto-calibration method is better than the single parameter method in the degree of precision. Thus the separate parameter auto-calibration method is applied to the water quality modelling of this study. The calibrated QUAL2Kw was used for the three scenarios for the water quality management of the Sum River, and the water quality impact on the river was analyzed. In scenario 1, which improve the effluent water quality of Wonju WWTP, BOD and TP concentrations of the Sum River 4-1 station which is representative one of Mid-Watershed, are decreased 17.7% and 29.1%, respectively. And immediately after joining the Wonjucheon, BOD and TP concentrations are decreased 50.4% and 40.5%, respectively. In scenario 2, Wonju water supply intake is closed and multi-regional water supply, which come from other watershed except the Sum River, is provided. The Sum River water quality in scenario 2 is slightly improved as the flow of the river is increased. Immediately after joining the Wonjucheon, BOD and TP concentrations are decreased 0.18mg/L and 0.0063mg/L, respectively. In scenario 3, the water quality management alternatives of scenario 1 and 2 are planned simultaneously, the Sum River water quality is slightly more improved than scenario 1. Water quality prediction of the three scenarios indicates that effluent water quality improvement of Wonju WWTP is the most efficient alternative in water quality management of the Sum River. Particularly the Sum River water quality immediately after joining the Wonjucheon is greatly improved. When Wonju water supply intake is closed and multi-regional water supply is provided, the Sum River water quality is slightly improved.

• Steel and Composite Structures
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• v.34 no.5
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• pp.681-698
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• 2020

The paper addresses contribution to the modeling and optimization of major machinability parameters (cutting force, surface roughness, and tool wear) in finish dry hard turning (FDHT) for machinability evaluation of hardened AISI grade die steel D₃ with PVD-TiN coated (Al₂O₃-TiCN) mixed ceramic tool insert. The turning trials are performed based on Taguchi's L₁₈ orthogonal array design of experiments for the development of regression model as well as adequate model prediction by considering tool approach angle, nose radius, cutting speed, feed rate, and depth of cut as major machining parameters. The models or correlations are developed by employing multiple regression analysis (MRA). In addition, statistical technique (response surface methodology) followed by computational approaches (genetic algorithm and particle swarm optimization) have been employed for multiple response optimization. Thereafter, the effectiveness of proposed three (RSM, GA, PSO) optimization techniques are evaluated by confirmation test and subsequently the best optimization results have been used for estimation of energy consumption which includes savings of carbon footprint towards green machining and for tool life estimation followed by cost analysis to justify the economic feasibility of PVD-TiN coated Al₂O₃+TiCN mixed ceramic tool in FDHT operation. Finally, estimation of energy savings, economic analysis, and sustainability assessment are performed by employing carbon footprint analysis, Gilbert approach, and Pugh matrix, respectively. Novelty aspects, the present work: (i) contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45-60 HRC, (ii) demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing ceramic tool in hard turning processes considering technological, economical and ecological aspects, which are helpful and efficient from industrial point of view, (iii) provides environment friendliness, cleaner production for machining of hardened steels, (iv) helps to improve the desirable machinability characteristics, and (v) serves as a knowledge for the development of a common language for sustainable manufacturing in both research field and industrial practice.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 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 Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.