Porous SiC using polycarbosilane/camphene solutions: Roles of freeze casting parameters

In order to control the pore characteristics and macroscopical performance of porous ceramics, roles of the freeze casting parameters are the key points. Herein, aligned dendritic porous SiC was fabricated by freeze casting of PCS-camphene solutions with different solid loading, freeze front velocity, temperature gradient, and freezing temperature. Influence of these parameters on the microstructure and compressive strength of porous SiC was investigated. With increasing the PCS content, freeze temperature, freeze front velocity or temperature gradient, degree of undercooling of the camphene was increased, resulting in the formation of smaller pore size, decreased porosity and increased compressive strength. Compared to variables of freeze temperature and temperature gradient, increased freeze front velocity was more efficiency in improving the compressive strength of porous SiC, owing to the formation of smaller pore size and longer secondary dendritic crystals. Promising micron-sized porous SiC with high porosity (79.93 vol%) and satisfactory strength (15.84 MPa) was achieved for 10% PCS-camphene solution under optimized freezing conditions.     

An ultrasonic study on ternary xPbO–(45-x)CuO–55B2O3 glasses

A series of xPbO–(45-x)CuO–55B₂O₃ glasses (5 ≤ x ≥ 40 mol %) were prepared by the melt-quenching technique. The X-ray diffraction (XRD) patterns of the prepared glasses are found to have amorphous structure. An extensive ultrasonic study has been made to explore the structural role of PbO and CuO in the borate network. Various elastic properties were calculated from the measured data of density and ultrasonic velocity. Ultrasonic velocity and elastic moduli revealed broad humps at about 20 mol % PbO, which are attributed to the borate anomaly. Below 20 mol % PbO, all Pb²⁺ ions are considered to be entering the borate network as a glass modifier. This results in the transforms the borate network from an open structure to a denser three-dimensional structure due to BO₃ → BO₄ conversion. Beyond 20 mol, addition of PbO results in the formation of metaborate, pyroborate, and orthoborate units with NBOs. This weakness the glass structure and decrease both ultrasonic velocity and elastic moduli. The elastic properties were predicted and quantitatively analyzed by taking into account the effect of boron coordination number on the compositional and structural parameters involved in Makishima–Mackenzie's theory, ring deformation model and bond compression model. An excellent agreement between the computed theoretical and experimental elastic moduli, micro-harness and Poisson's ratio was achieved for majority of samples.     

Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.     

Frequency response data-based peak filter design applied to MIMO large-scale high-precision scan stage

A large-scale high-precision scan stage is important equipment in the industrial productions of micro-fabrication such as flat panel display (FPD) lithography systems. Designing controllers for multi-input multi-output (MIMO) systems is time-consuming and needs experience because of the interaction between each axis and many controller tuning parameters. The aim of this study is to develop a peak filter design method based on frequency response data to reduce repetitive disturbance. This data-based approach does not use the model and only uses the frequency response data of the controlled system and the disturbance spectrum calculated from the scanning error data (Contribution 1). The peak filter is designed by convex optimization and satisfies robust stability conditions for six-degree-of-freedom systems (Contribution 2). The control performance of the designed peak filter is experimentally demonstrated with an industrial MIMO large-scale high-precision scan stage in reducing the scanning error of the main stroke of the translation along the $x$-axis (Contribution 3).     

复杂地质条件下地下厂房吊车梁岩壁成型控制爆破

针对雅安小关子水电站地下厂房吊车梁岩壁的特点 ,提出了对岩壁保护层的双层光面爆破方案及主要的爆破参数。爆破效果表明 ,所确定的控制爆破方案及设计参数是正确的 ,为类似的控制爆破工程提供了有益的经验     

Analysis of pressure and heat distribution in a dilating or contracting filter chamber with two outlets using multivariate spectral quasilinearization method

The development of efficient filters is an essential part of industrial machinery design, specifically to increase the lifespan of a machine. In the filter chamber design considered in this study, the magnetic material is placed along the horizontal surface of the filter chamber. The inside of the filter chamber is layered with a porous material to restrict the outflow of unwanted particles. This study aims to investigate the flow, pressure, and heat distribution in a dilating or contracting filter chamber with two outlets driven by injection through a permeable surface. The proposed model of the fluid dynamics within the filter chamber follows the conservation equations in the form of partial differential equations. The model equations are further reduced to a steady case through Lie's symmetry group of transformation. They are then solved using a multivariate spectral-based quasilinearization method on the Chebyshev–Gauss–Lobatto nodes. Insights and analyses of the thermophysical parameters that drive optimal outflow during the filtration process are provided through the graphs of the numerical solutions of the differential equations. We find, among other results, that expansion of the filter chamber leads to an overall decrease in internal pressure and an increase in heat distribution inside the filter chamber. The results also show that shrinking the filter chamber increases the internal momentum inside the filter, which leads to more outflow of filtrates.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.