Properties of electrospun superconducting and magnetoresistive nanowires

Supraleitende Nanofasern und magnetoresistive Nanodrähte wurden durch Elektrospinnen und entsprechende Temperaturbehandlung hergestellt. Der Mechanismus des Elektrospinnens wird vorgestellt. Die Kernpunkte der Herstellung von Nanobändern und die Idee des Auffangens von parallelen Nanofasern werden demonstriert. Um supraleitende oder magnetoresistive Phasen unter Erhalt der Faserstruktur zu erzeugen, wird die Temperaturbehandlung basierend auf einer thermogravimetrischen Analyse vorgeschlagen. Die Untersuchung der supraleitenden Nanofasern basiert auf den zwei Kuprat- Supraleitern La1.85Sr0.15CuO4 und Bi2Sr2CaCu2O8 (Bi-2212). Ein Vergleich der supraleitenden Eigenschaften zwischen La1.85Sr0.15CuO4 Nanodrähten und Nanobändern wird vorgestellt. Die magnetischen und elektrischen Eigenschaften der Bi-2212 - Netzwerke, mit reiner Bi-2212 Phase, aber auch Pb - und Li - dotierten Phasen, werden präsentiert. Das erweiterte Bean-Modell des kritischen Zustandes wird zur Bestimmung der kritischen Stromdichte angewendet und ein Modell eines Netzwerks bestehend aus Josephson-Kontakten wird vorgeschlagen, um die einzigartigen elektrischen Eigenschaften zu erklären. In einem eigenen Abschnitt werden die Eigenschaften einer einzelnen, dicken Bi-2212 Faser demonstriert. Die Eigenschaften von magnetoresistiven Nanodrahtnetzwerken werden anhand des Perowskits La1−xSrxMnO3 untersucht. Der Einfluss des Dotierungsgrads von Sr auf die magnetischen und magnetoresistiven Eigenschaften wird diskutiert. Zum Schluss werden die Eigenschaften von hybriden La1.85Sr0.15CuO4/La0.7Sr0.3- MnO3 - Nanodrahtnetzwerken präsentiert.Superconducting nanofibers (nanowires and nanoribbons) and magnetoresistive nanowires were fabricated by the electrospinning technique accompanied with appropriate thermal treatment. The mechanism of electrospinning is introduced. The key points of producing nanoribbons and the idea of parallel nanofiber collection are demonstrated. To obtain the superconducting or magnetoresistive phases while maintaining the fiber structure, a thermal treatment based on the thermal gravity analysis is proposed. The investigation of the superconducting nanofibers is based on two cuprate superconducting materials: La1.85Sr0.15CuO4 and Bi2Sr2CaCu2O8 (Bi-2212). A comparison of the superconductivity between La1.85Sr0.15CuO4 nanowires and nanoribbons is presented. The magnetic and electric properties of the Bi-2212 nanowire networks are presented, including a comparison between pure Bi-2212, Pb-doped Bi-2212, and Li-doped Bi-2212 nanowire networks. The extended critical state model is applied for the critical current density estimation, and a Josephson junction network model is proposed to explain the unique features of the electric properties. As a special section, the properties of a single Bi-2212 thick fiber are also demonstrated. The characterization of the magnetoresistive nanowire networks is based on the perovskite materials La1−xSrxMnO3. The influence of the Sr doping level on the magnetic properties and magnetoresistance is discussed. At the end, the properties of La1.85Sr0.15CuO4/La0.7Sr0.3MnO3 hybrid nanowire networks are presented

Research of sound absorption characteristics for the periodically porous structure and its application in automobile

The paper researches sound absorption characteristics of a periodically porous structure by combing transfer matrix method and a standing wave tube experiment. It is shown in results that the sound absorption effect of the periodical structure is better than that of a single-layer porous structure with the same thickness in the range of the low-frequency band. In addition, for the periodical structure with the same thickness, the sound absorption effect will not continuously increase along with the period number increase, and there is an optimal period number. In order to verify the actual sound absorption effect of the periodically porous structure, the paper tries to apply the structure in an automobile. Modal experiment is conducted to the automobile body, and the experimental results are compared with the simulated results. The relative errors are controlled within 5 % which is the critical value in engineering, and it indicates that the finite element model is reliable and can be used in the subsequent acoustic calculation. It is found in panel contribution analysis that noise in automobile is seriously influenced by the roof. Therefore, the periodically porous structure and a single-layer porous structure with the same thickness are applied to the roof panel. It is shown that the periodically porous structure has a more prominent sound absorption effect when it is compared with that of the other structures. This research also lays a foundation for broadening this application in the other structures

End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture are stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generative-discriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-art methods in both photo-sketch generation and face sketch verification.Comment: 8 pages, 6 figures. Proceeding in ACM International Conference on Multimedia Retrieval (ICMR), 201

Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model

In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time. Given such a DMDP with states $S$, actions $A$, discount factor $\gamma\in(0,1)$, and rewards in range $[0, 1]$ we provide an algorithm which computes an $\epsilon$-optimal policy with probability $1 - \delta$ where \emph{both} the time spent and number of sample taken are upper bounded by $$O\left[\frac{|S||A|}{(1-\gamma)^3 \epsilon^2} \log \left(\frac{|S||A|}{(1-\gamma)\delta \epsilon} \right) \log\left(\frac{1}{(1-\gamma)\epsilon}\right)\right] ~.$$ For fixed values of $\epsilon$, this improves upon the previous best known bounds by a factor of $(1 - \gamma)^{-1}$ and matches the sample complexity lower bounds proved in Azar et al. (2013) up to logarithmic factors. We also extend our method to computing $\epsilon$-optimal policies for finite-horizon MDP with a generative model and provide a nearly matching sample complexity lower bound.Comment: 31 pages. Accepted to NeurIPS, 201