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RISS 인기검색어
- 검색키워드
- 저자 : Jeng L. Huang (검색결과 2 건)
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- 유료
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Probability Density Function for the Critical Current Asymmetry of a HTS dc SQUID
Jen-Tzong Jeng,C. H. Wu,H. C. Yang,H. E. Horng,J. C. Chen,K. H. Huang,K. L. Chen,S. H. Liao,Y. C. Lin 한국물리학회 2006 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.48 No.5I
A dc SQUID consisting of grain boundary Josephson junctions may often be asymmetric owing to the large critical-current spread. In this work, the probability density function (PDF) for the critical-current asymmetry of high-transition-temperature-superconductor (HTS) dc SQUID was investigated. To model the critical-current spread in the grain boundaries, we assumed that the barrier thickness of the Josephson junction fluctuated according to a log-Weibull distribution. The resultant PDF for the critical-current spread that fits the histogram data of critical currents reported in literature was the gamma distribution. With the proposed distribution function, the corresponding PDF for the critical-current asymmetry parameter of a HTS dc SQUID was derived and compared with the experimental result. The proposed gamma distribution function is useful in modeling devices, such as the SQUID array and the superconducting quantum interference grating, consisting of many grain-boundary Josephson junctions.
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Hien D. Le,Jeng L. Huang,Hien D. Le 사단법인 한국계산역학회 2013 Computers and Concrete, An International Journal Vol.12 No.6
Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and non-linear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity–strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.
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