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.     

开关跳闸的原因及对策

分析日常生产及供电系统中,空气开关(断路器)跳闸的可能原因,并结合现场实际情况给出相应的处理方法与对策.     

Google体系结构分析

为了适应大规模广范围高密度的搜索,Google的网络搜索应用允许在不同的处理器上运行不同的查询;同时,通过对全局索引进行分解,Google也允许在多个处理器上运行单一的查询.Google的主旨是,使用市场上流行的具有通用的PC,以达到大型机所具有的高性能.通过对已有资料进行搜集,对Google的体系结构、工作性能等进行分析,目的是通过对Google的调研,给网格建设提供一定的借鉴.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

基于控制微元的统计自适应增量控制方法

依据系统对模拟量实时控制的要求,提出了基于控制微元的统计自适应增量控制方法,给出了量化控制状态统计图的获取方法,分别找出了扩展控制微元的统计稳定系数、平均统计状态偏差、统计趋势、统计超调、尾偏差等参数与调节增量之问的关系,确定了统计控制策略及其最优化条件,给出了统计预测自适应记忆增量控制器的设计方案.该方法易于微处理器编程,运算速度快,可高效率实现对模拟量的精确控制.     

基于双极偏好控制的多目标粒子群优化算法

考虑双极偏好信息对粒子群的控制作用,提出一种使用双极偏好——正偏好和负偏好引导粒子群向 Pareto 前沿偏好区域进化的方法．根据TOPSIS 决策法思想,将外部种群粒子与正负偏好点的相对贴近度排序作为 外部种群管理和全局最优解更新策略;根据贴近度值确定解集的分布度;选取6 种不同类型的多目标测试函数进行 算法模拟,从世代距离、空间测度和超体积测度3 个指标与基于单极偏好的多目标粒子算法进行性能比较．结果显 示,基于双极偏好控制的多目标粒子群算法的收敛性和综合性能更优秀．