A new admissibility condition of discrete-time singular systems with time-varying delays

This paper gives a novel delay-dependent admissibility condition of discrete-time singular systems with time-varying delays. For convenience, the time-varying delay is assumed to be the sum of delay lower bound and the integral multiples of a constant delay. Specially, if the constant delay is of unit length, the delay is an interval-like time-varying delay. The proposed admissibility condition is presented and expressed in terms of linear matrix inequality (LMI) by Lyapunov approach. Generally, the uncertainty of time-varying delay would lead to conservatism. In this paper, this critical issue is tackled by accurately estimating the time-varying delay. Consequently, the proposed admissibility condition is less conservative than the existing results, which is demonstrated by a numerical example.     

Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units

Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interference or lack of calibration. In this study, an effective approach based on the fusion of the general regression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.     

Analysis of gene expression programming for approximation in engineering design

To reduce the computational cost of implementing computer-based simulations and analyses in engineering design, a variety of metamodeling techniques have been developed and used for the construction of metamodels. Metamodels, also called approximation models and surrogate models, can be used to make a replacement of the expensive simulation codes for design and optimization. In this paper, gene expression programming (GEP) algorithm in the evolutionary computing area is investigated as an alternative metamodeling technique to provide the approximation of a design space. The approximation performance of GEP is tested on some low-dimensional mathematical and engineering problems. A comparative study is conducted on GEP and three common metamodeling techniques in engineering design (i.e., response surface methodology (RSM), kriging and radial basis functions (RBF)) for the approximation of the low-dimensional design space. Multiple evaluation criteria are considered in the comparison: accuracy, robustness, transparency and efficiency. Two different sample sizes are adopted: small and large. Comparative results indicate that GEP can achieve the most accurate and robust approximation of a low-dimensional design space for small sample sets. For large sample sets, GEP also presents good prediction accuracy and high robustness. Moreover, the transparency of GEP is the best since it can provide clear function relationships and factor contributions by means of compact expressions. As a novel metamodeling technique, GEP shows great promise for metamodeling applications in a low-dimensional design space, especially when only a few sample points are selected and used for training.     

电子政务数据共享模式研究

互联网的广泛应用,改变了社会公众对政府信息需求的观念。加强电子政务数据共享是社会发展的必然要求。为了保证政务信息的有效共享,实现不同部门不同类型数据的转换和重用,提出了一种新的基于公开密钥基础设施体系构造数据共享模式。在新的数据共享模式中,使用电子凭证替代传统的纸质凭证,增加了行政审批事务办理的快捷性、安全性、低碳环保性。为了进一步提高电子凭证的效率,将其与云计算无缝融合。通过对具体应用案例的分析,表明新的数据共享模式具有安全性、可行性和实用性。     

Study on heat conduction and adsorption/desorption characteristic of MIL-101/few layer graphene composite

Journal of Porous Materials - Aiming at the poor heat conduction performance of porous MIL-101 applied in adsorption cooling process, few layer graphene (FLG) was selected as a promising thermal...     

焊接热源有效利用率的测试计算法

给出实际热输入值是正确求解焊接热弹塑性问题和焊接残余应力与变形问题的前提;因而,准确地确定热源有效利用率η,在焊接力学的发展中是一个急待解决的命题。本文推荐采用的“测试计算法”提供了一个能够准确、简捷地求得η值的新途径。引入被焊材料的熔化潜热系数后,给出了线热源加热焊缝金属至熔化温度(处于液态)时的实际热有效利用率ηt 的表达式。在四种不同类型的薄板材料上进行了钨极氩弧焊试验,测试计算结果与理论值相符合。理论推导与试验结果均证明,形成焊缝所需要的线能量的无量纲数值等于焊缝金属熔化热有效利用率ηt 的倒数。文中给出的焊接规范参数选择原则也适用于符合线热源条件的其它熔焊热源。η值不但与焊接工艺方法、规范参数有关,而且也随被焊材料的热物理特性不同而异。     

挤压变形对Mg95Zn4.3Y0.7合金组织和性能的影响

研究了在250℃、不同挤压比下挤压变形对Mg95 Zn4.3 Y0.7合金组织及性能的影响。结果表明，Mg95 Zn4.3 Y0.7经过挤压变形后，合金中晶界处的共晶相破碎，弥散分布至晶粒内部，并且晶粒显著细化。同时，随着挤压比的增大，晶粒细化程度增加，合金的力学性能单调增加。当挤压比为16时，合金晶粒尺寸为5-8μm，抗拉强度为288.9MPa，显微硬度HV值为117.8。     

长轴表面激光淬火变形与控制

应用激光淬火强化技术控制长轴的淬火变形，分析了激光淬火变形的原因和影响因素。研究结果表明，激光淬火不仅可使长轴振摆变形量控制在很小的范围内，而且可将已变形的长轴校直到很高的精度。     

Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions

The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition. In this study, a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions. First, a novel stacked autoencoder(NSAE) is constructed using a denoising autoencoder, batch normalization, and the Swish activation function. Second, a series of source-domain NSAEs with multisensor vibration signals is pretrained. Third, the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs. Finally, a modified voting fusion strategy is designed to obtain a comprehensive result. The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method. The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample, thereby outperforming the existing methods.     

PAN/FPU Composite Nanofiber Membrane with Superhydrophobic and Superoleophobic Surface as a Filter Element for High-Efficiency Protective Masks

Recently, because of the outbreak of COVID-19, the demand for various types of filter elements in protective materials has increased globally. Furthermore, new requirements for the filtration performance of PM2.5 liquid (oil) particles have been put forward. In this work, Superhydrophobic and superoleophobic composite nanofibers with excellent filtration capacity for oil and salt particles are developed through the modification of polyacrylonitrile (PAN) by fluoro-polyurethane (FPU) doping. The results show that the PAN/FPU composite nanofibers doped with 9 wt% FPU has a uniform fiber morphology with a diameter of 240 ± 30 nm. Compared to the pure PAN nanofibers, the water-based contact angle of PAN/FPU increases from 90 ± 5° to 151 ± 5°, and the oil-based contact angle increases from 58 ± 2° to 152 ± 3°. Importantly, at a high flow rate of 95 L min⁻¹, the filtration efficiency of the PAN/FPU nanofiber membrane for 0.3 µm oil particles increases from 92 ± 1% to 99.2 ± 0.1%. After cyclic loading, the filtration efficiency of 0.3 µm oil particles remains above 98%. Meanwhile, the filtration efficiency for 0.3 µm salt particles remains at 98.23 ± 0.1%. The PAN/FPU nanofiber membrane developed in this work is effective in applications and has good market prospects as a protective filtration material.