基于双时间点检测的毫米波雷达人流量监测方法

针对现有基于视频监控的人流量统计方案成本高、算法复杂且不利于个人隐私保护的局限性，利用毫米波雷达体积小、成本低、分辨率高的特点，提出了一种基于双时间点检测的人流量监测方法。该方法先获取人体目标散射点位置和多普勒频移信息来构成点云数据，然后根据多普勒频移正负来判断人体的运动方向，并筛选具有高多普勒频移值的点云数据以降低干扰点对聚类结果的影响；在双时间点对特定区域内人员数量进行统计，并根据双时间点之间所获取的点云数据聚类结果对所统计人员数据进行修正。实验结果表明，该方法能够用匿名的方式以较高的正确率统计人员进出。     

Crowd counting in complex scenes based on an attention aware CNN network

Crowd counting with density estimation has been an active research community due to its significant applications in the fields of public security, video surveillance, traffic monitoring. However, Crowd counting for congested scenes often suffers from some obstacles including severe occlusions, large scale variations, noise interference, etc. In this paper, using the first ten layers of a modified VGG16 and dilated convolution layers as the framework, we have proposed a CNN based crowd counting and density estimation model improved by the attention aware modules with residual connections. To tackle the problem of noise interference, convolutional block attention modules have been introduced into the deep network to segment the foreground and background to focus on interest information, refining deeper features of the input image. To improve information transmission and reuse, residual connections are utilized to link 3 attention blocks. Meanwhile, dilated convolution layers keep larger reception fields and obtain high-resolution density maps. The proposed method has been evaluated on three public benchmarks, i.e. Shanghai Tech A & B, UCF-QNRF and MALL, achieving the mean absolute errors of 64.6 & 8.3, 113.8 and 1.68, respectively. The results outperform some existing excellent approaches. This indicates that the proposed model has high accuracy and better robustness, which is suitable for crowd counting and density estimation in various congested scenes.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Parallel color-coding

We present new parallelization and memory-reducing strategies for the graph-theoretic color-coding approximation technique, with applications to biological network analysis. Color-coding is a technique that gives fixed parameter tractable algorithms for several well-known NP-hard optimization problems. In this work, by efficiently parallelizing steps in color-coding, we create two new biological protein interaction network analysis tools: Fascia for subgraph counting and motif finding and FastPath for signaling pathway detection. We demonstrate considerable speedup over prior work, and the optimizations introduced in this paper can also be used for other problems where color-coding is applicable.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

三维圆弧型井眼轨道设计模型的拟解析解

为了研究三维圆弧型井眼轨道设计方程组是否存在解析解以及如何有效求解，使用消去法从多元非线性方程组中推导出其中的某个未知数满足一个特征多项式方程，并且证明了该方程组的拟解析解可以由特征多项式方程的实数根和已知设计参数构成的解析公式计算得到；如果极点多项式非恒等于0，则当特征多项式次数不超过5时，如果有解则为解析解，否则为数值解。通过使用C++类实现多项式加减法、乘法的计算方法，提出了多项式实数根的分隔算法、并用二分法快速求解出多项式全部实数根的计算方案。该方法在数学上经严格推导，计算方案简洁、稳健，在三维井眼轨道设计、智能钻井定向控制中具有应用价值。     

带权决策表的属性约简

属性约简是粗糙集理论的重要应用。考虑将决策表中的每行都作为一条决策规则时，若把表中出现相同决策规则的次数作为权，可得到带权决策表。提出了关于带权决策表的正域约简相应的辨识矩阵并给出了证明，从而得到了约简算法。相比于决策表中的正域约简时发现，通过将决策表转化为带权决策表后，再利用算法1进行约简时，其在一定程度上优于前者。提出了近似分类精度约简相应的辨识矩阵并给出了证明。对于2个算法，在选取的UCI数据集上进行了实验验证。通过实验进一步说明了所提出算法的可行性和有效性。