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Nowadays, blockchain technology is expected to promote the quality control of traditional industry due to its traceability, transparency and non-tampering characteristics. Although blockchain could offer the traditional industry new energy, there are still some predictable difficulties in the early stage of its application, such as the structure of the blockchain-based system, the role of regulators in the system and high transaction fee by block packing. In this paper, we establish a pioneering quality control system for the green composite wind turbine blade supply chain based on blockchain technology. Firstly, the framework of this system is proposed to ensure that the quality of the product could not only be examined and verified by regulator, but also be monitored by other related nodes. Next, we develop a new way to store the data by hash fingerprint and the cost of transaction fees is significantly reduced in the case of a large amount of data. Then, the information on-chain method is developed to realize the data traceability of each node. At last, the tests of this system are carried out to prove its validity, the satisfactory results are obtained and information supervision and sharing role of the regulators are discussed.
Hang Yu; Senlai Zhu; Jie Yang. The Quality Control System of Green Composite Wind Turbine Blade Supply Chain Based on Blockchain Technology. Sustainability 2021, 13, 8331 .
AMA StyleHang Yu, Senlai Zhu, Jie Yang. The Quality Control System of Green Composite Wind Turbine Blade Supply Chain Based on Blockchain Technology. Sustainability. 2021; 13 (15):8331.
Chicago/Turabian StyleHang Yu; Senlai Zhu; Jie Yang. 2021. "The Quality Control System of Green Composite Wind Turbine Blade Supply Chain Based on Blockchain Technology." Sustainability 13, no. 15: 8331.
In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.
Hang Yu; Senlai Zhu; Jie Yang; Yuntao Guo; Tianpei Tang. A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data. Sensors 2021, 21, 4971 .
AMA StyleHang Yu, Senlai Zhu, Jie Yang, Yuntao Guo, Tianpei Tang. A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data. Sensors. 2021; 21 (15):4971.
Chicago/Turabian StyleHang Yu; Senlai Zhu; Jie Yang; Yuntao Guo; Tianpei Tang. 2021. "A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data." Sensors 21, no. 15: 4971.
Cellulose-fiber-reinforced plain weave composites absorb lots of water from humid environments because of their inherent susceptibility to moisture. Moisture absorption experiments with cellulose fiber plain weave composites have been reported by some researchers; however, few theoretical studies have been performed to date to predict their moisture diffusion behavior. In this paper, the moisture diffusion behavior of cellulose-fiber-reinforced plain weave composite is predicted using a novel superposition method considering its microweave pattern. The overall moisture uptake of the composite is treated as moisture absorption superposition of the fiber bundles part, resin part, undulated fiber bundles and resin-rich part in the unit cell. The moisture diffusion of the undulated fiber bundles and resin-rich part is more complicated than the other parts; thus, a solution for a unique three-phase diffusion problem is used to solve this special moisture diffusion issue. Both finite element analysis and experiments are carried out to validate the proposed approach, with the results showing that the predictions can effectively characterize the moisture diffusion behavior of cellulose-fiber-reinforced plain weave composites.
Hang Yu; Jie Yang. Predictions of Moisture Diffusion Behavior of Cellulose-Fiber-Reinforced Plain Weave Epoxy Composites. Polymers 2021, 13, 2347 .
AMA StyleHang Yu, Jie Yang. Predictions of Moisture Diffusion Behavior of Cellulose-Fiber-Reinforced Plain Weave Epoxy Composites. Polymers. 2021; 13 (14):2347.
Chicago/Turabian StyleHang Yu; Jie Yang. 2021. "Predictions of Moisture Diffusion Behavior of Cellulose-Fiber-Reinforced Plain Weave Epoxy Composites." Polymers 13, no. 14: 2347.