钢筋网对道路混凝土振动的阻隔及对策

道路工程中,钢筋网下的混凝土往往不容易振动密实,钢筋网对振动能量存在阻隔作用。通过比较振动加速度的层间差异和测定分层密度等方法,研究振动能量在混凝土中(钢筋网上、下)的传播情况。研究表明:经过单层钢筋网的阻隔,振动能量在水平方向上约衰减12%,竖直方向上约衰减19%;振捣棒周边混凝土的硬化状态对振动能量的传播有一定影响;钢筋网的存在对其上混凝土的密实无显著影响,对其下混凝土则影响较大;振动频率和振动延续时间是影响振动能量衰减系数的关键因素,适当的增大振动频率、延长振动延续时间可以有效的抑制能量衰减。     

拉索高强镀锌平行钢丝摩擦系数的进一步确定

准确识别斜拉索内平行钢丝间的摩擦系数值是确定拉索局部弯曲应力大小的必要的前提条件。为了进一步精确测定斜拉索高强镀锌平行钢丝摩擦系数值,利用自行设计的装置在所需加力范围内对钢丝施加正压力,所施加的正压力可以达到实桥中拉索工作时所受的正压力值的大小,在加力范围内,测定出一系列钢丝间的摩擦系数值。试验结果表明:随着正压压力的增大,斜拉索内高强镀锌平行钢丝的摩擦系数明显降低;但当压力增大到较大值时摩擦系数的降幅明显减缓。     

荷载与渗压耦合作用下沥青面层层底裂纹扩展分析

基于线弹性断裂力学原理和有限元分析方法,对汽车荷载与动水压力耦合作用的面层层底含裂纹的沥青路面进行裂纹扩展规律研究与分析.结果表明:动水压力的作用明显改变了对称荷载与偏荷载作用下沥青面层层底的裂纹扩展规律,并随着动水压力增大,裂纹尖端呈现张拉型扩展与剪切型扩展共同作用的复合形式,从而加速了沥青路面的破坏.     

基于Matlab/GUI的公交发车频率优化软件开发

设计了一种基于Matlab的公交发车频率优化软件,将目前主要的发车频率确定方法集成到软件中,同时包含公交客流断面图绘制和优化曲线绘制功能,为用户提供多维度的决策手段。利用Matlab/GUI组件搭建能够与用户交互、可视化的计算平台,较好地实现了公交发车频率优化一体化集成功能。最后通过昆明44路车跟车调查数据验证了软件的可行性。     

TDOA Passive Location Based on Cuckoo Search Algorithm

This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.     

Adaptive Sliding-Mode Tracking Control for an Uncertain Nonlinear SISO Servo System with a Disturbance Observer

An adaptive sliding mode controller with a disturbance observer (ASMC-DO) is proposed for the control of a single-input and single-output (SISO) servo system which has uncertain parameters, nonlinear friction, disturbance and input saturation. It is difficult to choose the suitable value of the parameters. The newly designed adaptive method is used to reduce the effects of system time-varying parameters, such as the moment of inertia and the damp coefficient. The robustness of object is improved. A DO is selected to approximate the compound disturbance and to render the estimate error convergent in finite time. The stability and the convergence of the closed-loop system are proved by using the Lyapunov theory. Experimental results show that the proposed ASMC-DO can better satisfy the influence of variable parameters and external disturbance to the control precision of the SISO servo system than other two controllers. The effectiveness of the proposed controller is showed. The control input stability and robust performances of the input saturation system are enhanced and the chattering is reduced.     

Adaptive Multi-Objective Optimization of Bionic Shoulder Joint Based on Particle Swarm Optimization

To get the movement mode and driving mechanism similar to human shoulder joint, a six degrees of freedom (DOF) serial-parallel bionic shoulder joint mechanism driven by pneumatic muscle actuators (PMAs) was designed. However, the structural parameters of the shoulder joint will affect various performances of the mechanism. To obtain the optimal structure parameters, the particle swarm optimization (PSO) was used. Besides, the mathematical expressions of indexes of rotation ranges, maximum bearing torque, discrete dexterity and muscle shrinkage of the bionic shoulder joint were established respectively to represent its many-sided characteristics. And the multi-objective optimization problem was transformed into a single-objective optimization problem by using the weighted-sum method. The normalization method and adaptive-weight method were used to determine each optimization index’s weight coefficient; then the particle swarm optimization was used to optimize the integrated objective function of the bionic shoulder joint and the optimal solution was obtained. Compared with the average optimization generations and the optimal target values of many experiments, using adaptive-weight method to adjust weights of integrated objective function is better than using normalization method, which validates superiority of the adaptive-weight method.     

Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method

Weight reduction has attracted much attention among ship designers and ship owners. In the present work, based on an improved bi-directional evolutionary structural optimization (BESO) method and surrogate model method, we propose a hybrid optimization method for the structural design optimization of beam-plate structures, which covers three optimization levels: dimension optimization, topology optimization and section optimization. The objective of the proposed optimization method is to minimize the weight of design object under a group of constraints. The kernel optimization procedure (KOP) uses BESO to obtain the optimal topology from a ground structure. To deal with beam-plate structures, the traditional BESO method is improved by using cubic box as the unit cell instead of solid unit to construct periodic lattice structure. In the first optimization level, a series of ground structures are generated based on different dimensional parameter combinations, the KOP is performed to all the ground structures, the response surface model of optimal objective values and dimension parameters is created, and then the optimal dimension parameters can be obtained. In the second optimization level, the optimal topology is obtained by using the KOP according to the optimal dimension parameters. In the third optimization level, response surface method (RSM) is used to determine the section parameters. The proposed method is applied to a hatch cover structure design. The locations and shapes of all the structural members are determined from an oversized ground structure. The results show that the proposed method leads to a greater weight saving, compared with the original design and genetic algorithm (GA) based optimization results.     

GNN-CRC: Discriminative Collaborative Representation-Based Classification via Gabor Wavelet Transformation and Nearest Neighbor

Collaborative representation-based classification (CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor (NN) features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.