ALBERT

Description: In order to model the effect of mixture parameters and material properties on the hardened properties of, prestressed self-compacting concrete (SCC), and also to investigate the extensions of the statistical models, a factorial design was employed to identify the relative significance of these primary parameters and their interactions in terms of the mechanical and visco-elastic properties of SCC. In addition to the 16 fractional factorial mixtures evaluated in the modeled region of −1 to +1, eight axial mixtures were prepared at extreme values of −2 and +2 with the other variables maintained at the central points. Four replicate central mixtures were also evaluated. The effects of five mixture parameters, including binder type, binder content, dosage of viscosity-modifying admixture (VMA), water-cementitious material ratio (w/cm), and sand-to-total aggregate ratio (S/A) on compressive strength, modulus of elasticity, as well as autogenous and drying shrinkage are discussed. The applications of the models to better understand trade-offs between mixture parameters and carry out comparisons among various responses are also highlighted. A logical design approach would be to use the existing model to predict the optimal design, and then run selected tests to quantify the influence of the new binder on the model.

Description: Materials, Vol. 11, Pages 1405: Reduced Graphene Oxide and Its Modifications as Catalyst Supports and Catalyst Layer Modifiers for PEMFC Materials doi: 10.3390/ma11081405 Authors: Sergey A. Grigoriev Vladimir N. Fateev Artem S. Pushkarev Irina V. Pushkareva Natalia A. Ivanova Valery N. Kalinichenko Mikhail Yu. Presnyakov Xing Wei Reduced graphene oxide (RGO) and RGO modified by ozone (RGO-O) and fluorine (RGO-F) were synthesized. Pt nanoparticles were deposited on these materials and also on Vulcan XC-72 using the polyol method. The structural and electrochemical properties of the obtained catalysts were investigated in a model glass three-electrode electrochemical cell and in a laboratory PEM fuel cell. Among the RGO-based catalysts, the highest electrochemically active surface area (EASA) was obtained for the oxidized RGO supported catalyst. The EASA of the fluorine-modified RGO-supported catalyst was half as big. In the PEM fuel cell the performance of RGO-based catalysts did not exceed the activity of Vulcan XC-72-based catalysts. However, the addition of an RGO-O-based catalyst to Vulcan XC-72-based catalyst (in contrast to the RGO-F-based catalyst) allowed us to increase the catalyst layer activity and PEM fuel cell performance. Possible reasons for such an effect are discussed.

Description: Sensors, Vol. 18, Pages 3142: From Signal to Image: Enabling Fine-Grained Gesture Recognition with Commercial Wi-Fi Devices Sensors doi: 10.3390/s18093142 Authors: Qizhen Zhou Jianchun Xing Wei Chen Xuewei Zhang Qiliang Yang Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios.

Description: We consider a wireless network strategy based on successive interference cancellation (SIC) for mine locomotives. We firstly build the original mathematical model for the strategy which is a non-convex model. Then, we examine this model intensively, and figure out that there are certain regulations embedded in it. Based on these findings, we are able to reformulate the model into a new form and design a simple algorithm which can assign each locomotive with a proper transmitting scheme during the whole schedule procedure. Simulation results show that the outcomes obtained through this algorithm are improved by around 50% compared with those that do not apply the SIC technique.