强油非导向变压器热特性分析及优化设计

本文中作者借助流体软件对强油非导向变压器进行了热特性分析,并对变压器内部散热油路及冷却器进行了优化设计.     

Oxidation Performance of Fe-Al/WC Composite Coatings Produced by High Velocity Arc Sprayed at 650 ℃

Fe-Al intermetallics with remarkable high-temperatureintensity and excellent erosion,high-temperatureoxidation and sulfuration resistance are potential lowcost high-temperature structural material.ProducingFe-Al/WC composite coating by high velocity arcspraying(HVAS)on structural materials would notonly obviate the problems faced in fabrication of thesealloys into useful shapes,but also allow the effectiveuse of their outstanding high-temperature performance,which might thus promisingly mak…     

高线粗中轧机"浮动"检测结构设计及故障排除

为克服轧制中因机架锁紧功能降低而产生的机架沿轨座轴向移动的"浮动"现象,对其产生原因进行了分析,并通过改进锁紧装置结构及相关部件,使"浮动"故障得到预防和有效控制并及时排除。     

薄壁防渗墙施工工艺

结合建造薄壁防渗墙工程实例,通过分析几种施工工艺的成墙原理和优缺点,合理选择薄壁防渗墙施工工艺。     

结合阴影抑制的混合高斯模型改进算法

混合高斯模型背景法作为运动目标检测的一种经典方法,已经广泛应用于智能视频监控系统中。但是,传统的混合高斯模型背景法容易将阴影误检测为运动目标的一部分。因此,针对该方法在区分阴影和运动目标方面的不足,提出了一种将混合高斯模型背景法和HSV空间阴影抑制相结合的运动目标检测算法。这种改进算法首先将颜色空间转换到HSV空间,初步提取运动目标,然后再利用阴影的灰度值比背景中的灰度值小,而前景的灰度值比背景中灰度值大的特性,检测出运动目标中的阴影。实验结果表明,这种改进的算法明显提高了检测效果,有效抑制了阴影对运动目标检测的干扰,算法实时性也较好。     

Practical chosen-ciphertext secure Hierarchical Identity-Based Broadcast Encryption

We focus on practical Hierarchical Identity-Based Broadcast Encryption (HIBBE) with semantic security against adaptively chosen-ciphertext attacks (CCA2) in the standard model. We achieve this goal in two steps. First, we propose a new HIBBE scheme that is secure against chosen-plaintext attacks (CPA). Compared with the existing HIBBE scheme that is built from composite-order bilinear groups, our construction is based on prime-order bilinear groups. The much better efficiency of group operations in prime-order bilinear groups makes our proposed HIBBE scheme more practical. Then, we convert it into a CCA2-secure scheme at the cost of a one-time signature. Instead of extending one user hierarchy in the Canetti–Halevi–Katz approach from CPA-secure (\(l+1\))-Hierarchical Identity-Based Encryption [(\(l+1\))-HIBE] to CCA2-secure \(l\)-HIBE, our construction merely adds one on-the-fly dummy user in the basic scheme. We formally prove the security of these two schemes in the standard model. Comprehensive theoretical analyses and experimental results demonstrate that the proposed HIBBE schemes achieve desirable performance.     

Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.