Web Tension and Speed Control in Roll-to-Roll Systems

Roll-to-roll (R2R) printing shows great potential for high-throughput and cost-effective production of flexible electronics, including solar cells, wearable sensors, and so on. In roll-to-roll process, precise control of the web speed and tension is critical to ensure product quality, since improper web speed and tension would lead to severe damages to the substrates. In this chapter, we will focus on the advanced control algorithms of web tension and speed control in roll-to-roll system. Two concepts of control algorithms will be presented, which are model-based control and data-based control. For model-based control algorithms, the modeling of web dynamics and an application of robust H∞ controller will be reviewed; for data-based control algorithms, two methods of neural network control learning methods will be introduced, and the application of neural network control in web tension and speed control will be presented. Moreover, performances of different control algorithms are compared

Identifying vital edges in Chinese air route network via memetic algorithm

Due to its rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of highest topological importance, for which we provide an extensive explanation from the microscope of view. Our findings also offer new insights to understanding and optimizing other real-world network systems

(2E,4E)-2-Cyano-5-dipropyl­amino-N,N-dimethyl­penta-2,4-dienamide

In the title compound, C14H23N3O, the n-propyl group is disordered over two orientations with an occupancy ratio of 0.778 (3):0.222 (3). In the crystal, mol­ecules are linked by pairs of weak C—H⋯O inter­actions into inversion dimers with an R 2 2(14) graph-set motif

Utjecaj egzogenih enzima na senzorsku kakvoću tieguanyin oolong čaja ljetne berbe iz kineske pokrajine Anxi

In order to att enuate the bitt er taste and improve the aroma of the summer tieguanyin oolong tea from the Chinese Anxi county, the eff ects of processing treatment with exogenous laccase and α-galactosidase on tea sensory quality and related compounds were investigated. The solutio ns of laccase and/or α-galactosidase were sprayed on the tea leaves before the fi rst drying process. The sensory evaluation results showed that the sensory quality of the tea was signifi cantly enhanced with the enzymatic treatment. The combined application of laccase a t 8.25 and α- galactosidase at 22 U per kg of fresh tea shoots achieved the most satisfying sensory quality. Further analysis of fl avour-related constituents was carried out by HPLC and GC-MS. The HPLC analysis showed that the contents of catechins and total polyphen ols were reduced, compared to the untreated group, by 11.9 and 13.3 % respectively, and the total soluble sugars and water extract content were increased by 19.4 and 6.6 % respectively, aft er the treatment with both enzymes. The decrease of catechins and total polyphenols reduced the bitt erness and astringency of the summer tea, while the increase of total soluble sugars and water extract content improved the sweetness and mellow taste. The aromatic compound data from GC-MS showed that the total essential oil content in these tea samples co-treated with laccase and α-galactos idase increased significantly, in which aldehydes, alcohols, esters and alkenes increased by 23.28, 37.05, 20.10 and 38.99 %, respectively. Our data suggest that the exogenous enzymatic treatment can enhance the summer oolong tea quality, especially its taste and aroma.U radu je ispitan utjecaj tretiranja mladica tieguanyin oolong čaja ljetne berbe iz kineske pokrajine Anxi egzogenim enzimima, i to lakazom i α-galaktozidazom, na ublažavanje gorkog okusa te poboljšanje arome i senzorske kakvoće čaja. Prije početka sušenja, mladice su čaja bile prskane otopinama lakaze i/ili α-galaktozidaze. Rezultati senzorske analize pokazuju da su enzimskom obradom bitno poboljšana organoleptička svojstva čaja. Najbolji su rezultati postignuti tretiranjem mladica otopinom 8,25 U lakaze i 22 U α-galaktozidaze po kilogramu svježih mladica. Pomoću metoda HPLC i GC-MS ispitani su sastojci arome, pri čemu je metodom HPLC utvrđeno da se nakon obrade enzimima udjel katehina smanjio za 11,9 %, a ukupnih polifenola za 13,3 %, te povećao udjel ukupnih topljivih šećera za 19,4 % i vodenog ekstrakta za 6,6 %, u usporedbi s netretiranim uzorkom. Smanjenjem udjela katehina i ukupnih polifenola smanjila se gorčina i trpkost čaja, a povećanjem udjela ukupnih topljivih šećera i vodenog ekstrakta dobiven je čaj blagog i slatkog okusa. Rezultati analize aromatskih spojeva metodom GC-MS pokazuju da se ukupni udjel eteričnih ulja u uzorcima čaja tretiranim s oba enzima bitno povećao, i to aldehida za 23,28 %, alkohola za 37,05 %, estera za 20,10 % i alkena za 38,99 %. Dobiveni podaci upućuju na zaključak da se obradom oolong čaja ljetne berbe egzogenim enzimima može poboljšati okus i aroma čaja

Restarted Hessenberg method for solving shifted nonsymmetric linear systems

It is known that the restarted full orthogonalization method (FOM) outperforms the restarted generalized minimum residual (GMRES) method in several circumstances for solving shifted linear systems when the shifts are handled simultaneously. Many variants of them have been proposed to enhance their performance. We show that another restarted method, the restarted Hessenberg method [M. Heyouni, M\'ethode de Hessenberg G\'en\'eralis\'ee et Applications, Ph.D. Thesis, Universit\'e des Sciences et Technologies de Lille, France, 1996] based on Hessenberg procedure, can effectively be employed, which can provide accelerating convergence rate with respect to the number of restarts. Theoretical analysis shows that the new residual of shifted restarted Hessenberg method is still collinear with each other. In these cases where the proposed algorithm needs less enough CPU time elapsed to converge than the earlier established restarted shifted FOM, weighted restarted shifted FOM, and some other popular shifted iterative solvers based on the short-term vector recurrence, as shown via extensive numerical experiments involving the recent popular applications of handling the time fractional differential equations.Comment: 19 pages, 7 tables. Some corrections for updating the reference

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process