Experimental and physical approaches on a novel semiconducting-ionic membrane fuel cell

Semiconducting-ionic membranes (SIMs) have exhibited significant superiority to replace the conventional ionic electrolytes in solid oxide fuel cells (SOFCs). One interesting phenomenon is that the SIMs can successfully avoid the underlying short-circuiting issue and power losses while bringing significantly enhanced power output. It is crucial to understand the physics in such devices as they show distinct electrochemical processes with conventional fuel cells. We first presented experimental studies of a SIM fuel cell based on a composite of semiconductor LiCo_0.8Fe_0.2O₂ (LCF) and ionic conductor Sm-doped CeO₂ (SDC), which achieved a remarkable power density of 1150 mW cm^?2 at 550 °C along with a high open circuit voltage (OCV) of 1.04 V. Then, for the first time we used a physical model via combining a semiconductor-ionic contact junction with a rectifying layer which blocks the electron leakage to describe such unique SIM device and excellent performance. Current and power are the most important characteristics for the device, by introducing the rectifying layer we described the SIM physical nature and new device process. This work presented a new view on advanced SIM SOFC science and technology from physics.     

Cross-Layer Optimization of Dynamic Packet Assignment for Video Transmission Over IEEE 802.11e Networks

Video transmission over IEEE 802.11e wireless networks still shows poor performance for large bandwidth demand and frequently changed environments. Thus, several enhancements of IEEE 802.11e were proposed. On the other hand, big frames and simultaneous sending of adjacent frames always cause packet dropping for buffer overflow. In the past, we proposed an IEEE 802.11e enhancement named DFAA and a content aware mechanism to solve the above problems. The motivation of this paper is to find a proper way to integrate these two mechanisms. A DFAA enhancement (DFAA-E) is proposed to make up the insufficiency of content aware mechanism. Experiments results show that the combination of DFAA-E and content aware mechanism improves the video decoded quality greatly. And its performance can be further enhanced by selecting the suitable settings of certain parameters.     

Tunable Spectrum Selectivity for Multiphoton Absorption with Enhanced Visible Light Trapping in ZnO Nanorods

Observation of visible light trapping in zinc oxide (ZnO) nanorods (NRs) correlated to the optical and photoelectrochemical properties is reported. In this study, ZnO NR diameter and c‐axis length respond primarily at two different regions, UV and visible light, respectively. ZnO NR diameter exhibits UV absorption where large ZnO NR diameter area increases light absorption ability leading to high efficient electron–hole pair separation. On the other hand, ZnO NR c‐axis length has a dominant effect in visible light resulting from a multiphoton absorption mechanism due to light reflection and trapping behavior in the free space between adjacent ZnO NRs. Furthermore, oxygen vacancies and defects in ZnO NRs are associated with the broad visible emission band of different energy levels also highlighting the possibility of the multiphoton absorption mechanism. It is demonstrated that the minimum average of ZnO NR c‐axis length must satisfy the linear regression model of Z _p,min = 6.31d to initiate the multiphoton absorption mechanism under visible light. This work indicates the broadening of absorption spectrum from UV to visible light region by incorporating a controllable diameter and c‐axis length on vertically aligned ZnO NRs, which is important in optimizing the design and functionality of electronic devices based on light absorption mechanism.     

Effect of ground ginger on dough and biscuit characteristics and acrylamide content

Food Science and Biotechnology - Effect of different contents of ground ginger [0%, 1%, 3%, 5%, and 7% (w/w)] on flour quality, dough and biscuit characteristic and acrylamide content were...     

Space Efficient Quantization for Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have shown outstanding performance in the fields of computer vision, natural language processing, and complex system analysis. With the improvement of performance with deeper layers, DCNNs incur higher computational complexity and larger storage requirement, making it extremely difficult to deploy DCNNs on resource-limited embedded systems (such as mobile devices or Internet of Things devices). Network quantization efficiently reduces storage space required by DCNNs. However, the performance of DCNNs often drops rapidly as the quantization bit reduces. In this article, we propose a space efficient quantization scheme which uses eight or less bits to represent the original 32-bit weights. We adopt singular value decomposition (SVD) method to decrease the parameter size of fully-connected layers for further compression. Additionally, we propose a weight clipping method based on dynamic boundary to improve the performance when using lower precision. Experimental results demonstrate that our approach can achieve up to approximately 14x compression while preserving almost the same accuracy compared with the full-precision models. The proposed weight clipping method can also significantly improve the performance of DCNNs when lower precision is required.

     

$$H_{infty }$$ H ∞ State Estimation for Stochastic Jumping Neural Networks with Fading Channels Over a Finite-Time Interval

Neural Processing Letters - This paper is concerned with the global Mittag-Leffler synchronization schemes for the Caputo type fractional-order BAM neural networks with multiple time-varying delays...     

Selfie retoucher: subject-oriented self-portrait enhancement

Sharing self-portraits starts trending nowadays with the boom of social networks and the rise of smartphones. However, limited by the hardware capabilities, self-portraits taken by the front cameras of portable media devices usually face quality problems such as an incomplete field of view and poor lighting style. In our paper, we introduce a selfie retoucher which enhances a self-portrait with the help of N supporting photos that share the same scene and similar shooting time. With the extra information brought by the supporting photos, a lager field of view and a better lighting style can be achieved. To accomplish this, we propose a novel subject-oriented self-portrait enhancement method with a cascaded illumination unification and photos registration framework. Based on the correspondences extracted from the input 1+N photos, our method estimates and updates the illumination and registration coefficients in a cascaded manner. Moreover, a subject-oriented enhancement algorithm is proposed to enhance the face of the photographer in the self-portrait. We adopt a face-specific illumination correction process over the self-portrait to further improve the visual quality of the subject. After the enhancement, we globally fuse the aligned photos by a Markov Random Field based optimization method. During the fusion, a body map is additionally derived from the subject for guidance. Experimental results demonstrate that the proposed method achieves high-quality results in this novel application scenario.

     

An approach to multiplicative linguistic group decision making based on possibility degrees

In order to compare two uncertain multiplicative linguistic variables, a possibility degree formula has been developed and its properties are also studied. Then a possibility degree matrix (also considered as a reciprocal preference relation) is constructed to compare a collection of uncertain multiplicative linguistic variables. Two models are further provided to derive the priority vector from the possibility degree matrix based on the additive consistency and multiplicative consistency. Especially, if the parameters are assigned specific values, then the models reduce to the existing ones. A group decision making method has been developed to deal with the situations where the preferences on alternatives are expressed by uncertain multiplicative linguistic variables. In this method, the possibility degree matrix is first constructed, from which the priority of alternatives is obtained using the developed models. At the end, an example is given to illustrate the proposed method.     

The Nucleation Potency of In Situ-Formed Oxides in Liquid Iron

Metallurgical and Materials Transactions A - The nucleation potency of iron oxides was verified experimentally through nucleation undercooling of liquid iron using aerodynamic levitation technology...     

Coupled Ablation and Heat Transfer Analysis of Lower Head for Reactor Pressure Vessel under Severe Accident Condition

After a reactor core melts accident, the solid wall of the reactor pressure vessel (RPV) will be inevitably eroded by the melting core which contains large density of heat flux. The analysis of the coupled ablation and heat transfer of the lower head for RPV is of great theoretical significance to the effectiveness demonstration of water injection in reactor pit and the confirmation of the residual wall thickness of RPV. In this work, numerical simulations were carried out based on the RPV model of CPR1000 using the CFD software FLUENT 17.2. Based on dynamic mesh model and user-defined function (UDF) redevelopment, a fully coupling calculation model considering the transient ablation and heat conduction of solid wall of RPV, the redistribution of heat flux density in RPV inner wall and the subcooled boiling of RPV outer wall was established. Both two-phase flow pattern in the reactor pit and temperature field of RPV solid wall ablation within 9 000 s were obtained and the minimum residual wall thickness and the occurrence location were determined by analysis. The results show that it is feasible to use dynamic mesh to capture wall ablation. The fully coupling calculation model has certain advantages in analyzing the transient ablation process of RPV under severe accident.