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The Euler-Bernoulli beam theory is used to establish the vibration differential equation of the rigid catenary, the cantilever support device is equivalent to the spring, and the pantograph is equivalent to the three mass block model. The double-pantograph-catenary (DPC) dynamic coupling model is established by using the penalty function, and utilize Newmark- $\beta $ solved the model, verifying the reliability of the model with the field test contact forces of the rigid catenary system (RCS) of Xi’an Metro in China, and illustrating the vibration characteristics and fluctuation propagation characteristics of the rigid catenary structure in combination with the fluctuation propagation theory of beam and the sag model of rigid catenary. Based on the model, the dynamic interaction mechanism between the pantograph and the rigid catenary is studied, including the influence of different spans, speeds on the dynamic contact force of the DPC, the flow field of the pantograph is analyzed with ANSYS, and the influence of train induced wind in the tunnel is studied in combination with the DPC model. The results show that when the train operates on RCS with a double pantograph, the contact force fluctuation of the trailing pantograph (TP) is more severe than that of the leading pantograph (LP); Among the three common spans of 6m, 8m, and 10m, when the speed is less than 80 km/h, the span of 8m or 10m should be selected, when the speed is 80–140 km/h, the span of 6m or 8m should be selected, when the speed is greater than 140 km/h, the span should be the minimum 6m; With the increase of the wind velocity and the angle of attack, the fluctuation of the pantograph-catenary contact force of the RCS increases, and compared to the LP, the contact force fluctuation of the TP is greater.

Evaluating the potential utilization of hybrid energy systems and determining the multi-scale optimal operation strategy is critical to power system planning in the context of energy structure adjustment, especially for large-scale hybrid energy systems. Considering the long-term and short-term complementary characteristics, this paper puts forward a coordinated optimization framework for the integrated energy system in the world’s largest multi-energy complementary base on Yellow River’s upper reaches. The main procedures are as follows: 1) cross-correlation method is introduced for individually analyzing the long- and short-term complementary characteristics of wind power, photovoltaic, and hydropower in this multi-energy complementary base; 2) a double-layer model combining the long-term optimal operation model and short-term optimal operation model for determining the proportion of multiple energy and optimizing the maximum peak-shaving ability; 3) Large-Scale System Decomposition-Coordination Method is applied for solving the proposed double-layer operation model. The results show that wind power 23%, photovoltaic 35%, hydropower 42% can keep the most stable generation in the long-term complementary operation. This proportion results can improve the system peak regulation capacity with 50.8% (sunny day’s morning peak) and 24.2% (rainy day’s morning peak) in the optimal short-term operation.

This study introduces a wearable soft knee exoskeleton that aids active knee motions during walking. It is mainly driven by vacuum-actuated rotary actuators. In this paper, the overall design of the exoskeleton is introduced. Moreover, the design of the vacuum-actuated rotary actuator is introduced, and the correspondence among the interior air pressure, rotation angle and output force of the actuator under a vacuum condition and during the transition from the vacuum to an equal atmospheric pressure condition were studied. Then, the corresponding relations among the pressure, angle and torque of the actuator were obtained to construct a knee torque model. Furthermore, we introduced in detail the control system of the exoskeleton, including a gait estimation model and knee torque model. The control system analysed knee angle information measured by IMUs and the air pressure of the actuators measured by air pressure sensors. Then, by the calculations performed by both the gait estimation model and knee torque model, the corresponding commands for the aerodynamic switch, pressures and air flow rates were determined. According to the commands, a micro air pump and valves provided quantitatively positive or negative pressure for the actuators to generate gait-consistent auxiliary torques, stretching torques and bending torques that were able to meet the needs of the knee during walking. Finally, a cardiopulmonary exercise test was used to quantitatively evaluate the exoskeleton. The results show that under the same load condition, the metabolic cost of walking is reduced by an average of 6.85% when the exoskeleton is worn.

Time-varying channel estimation is a well-known challenge for the wireless communications in the high mobility environments. In this paper, we propose a basis expansion model (BEM) based channel estimation algorithm utilizing orthogonal spatial domain subspace projection. The channel estimation is taken on each subspace and the estimation results of all subspaces are combined to obtain the final time-varying channel parameters. In our proposed scheme, the wave number spectrum shifting is utilized to suppress the time-varying level on each subspace and the wave number spectrum recovery is adopted in the combination process. Simulation results are provided to verify our proposed scheme.

In this paper, the object detection algorithm based on deep learning running on the robot platform is studied and optimized. The p has high requirements for the detection efficiency and scale invariance of the algorithm. In order to improve the detection accuracy on all scales and keep the balance between speed and accuracy, we propose the following methods: Aiming at the problem of low detection accuracy of object detection algorithm for scale changing objects, the traditional image pyramid technology of computer vision is used to verify its effectiveness in improving the detection accuracy of the algorithm for scale changing objects. Then, by embedding the image pyramid into the network, the memory consumption caused by the traditional pyramid is reduced, and the detection accuracy of the algorithm for different scale objects is improved. A new feature fusion recalibration structure is designed. Feature fusion can fuse the low-level location information and high-level semantic information. The recalibration assigns the importance weight of the channel of the feature maps. This structure can effectively improve the detection accuracy of the algorithm at all scales without losing too much speed. We apply these two structures to YOLO. The accuracy of the improved algorithm has a significant improvement and the algorithm can run at 16 FPS on a TITAN Xp GPU.

Localization is one of the most fundamental problems for mobile robot. Aiming at the phenomenon that robot is prone to be lost in navigation under severe occlusion, a robust localization system combining vision and lidar is proposed in this paper. The system is split into off-line stage and online stage. In the off-line stage, this paper introduces a method of actively detecting and recording visual landmarks, and an off-line visual bag-of-words is generated from the recorded landmarks training. In the online stage, the prediction and update phase of Adaptive Monte Carlo Localization (AMCL) are improved respectively to enhance the performance of localization. The prediction phase generates the proposal distribution according to the prior information obtained through retrieving visual landmarks, and the newly proposed measurement model that selects reliable beams of lidar as the observation is to update the prediction. Experiments is carried out under strict conditions, that is 60% of the lidar is occluded, 1/12 of the beams are regarded as observation, and only 300 particles were adopted at most, it is shown that, no matter in the global localization or pose tracking, the localization system proposed in this paper performs much better than the state of art localization algorithm AMCL.

A new contactless velocity measurement sensor for bubble/slug flow in small scale pipes is developed with the contactless impedance detection (CID) technique. The new sensor consists of two same-designed CID modules (an upstream and a downstream) and an information processing module. In the upstream/downstream CID module, the capacitive reactance elimination principle is introduced to eliminate the adverse influence of coupling capacitances, and the analog phase sensitive demodulation (APSD) technique is adopted to obtain the fluid impedance signals (real part and imaginary part) of the gas-liquid two-phase flow. In the information processing module, the cross-correlation method is introduced to determine two initial bubble/slug velocities with real part and imaginary part of the fluid impedance, respectively. Then for each flow pattern, a velocity fusion model is established to fuse the two initial velocities. Based on the flow pattern identification result by a flow pattern classifier, the corresponding velocity fusion model is applied to determine the fused bubble/slug velocity. Three prototypes of the new sensor are developed and experiments are carried out. Experimental results show that the new sensor has good velocity measurement performance for bubble/slug flow in small scale pipes. For all the three prototypes, the maximum relative errors of velocity measurement are less than 3.0%. Results also indicate that the capacitive reactance elimination can successfully overcome the adverse influence of the coupling capacitances and taking full advantage of the total fluid impedance signals is an effective way to improve the velocity measurement accuracy.

A globally consistent map is the basis of indoor robot localization and navigation. However, map built by Rao-Blackwellized Particle Filter (RBPF) doesn’t have high global consistency which is not suitable for long-term application in large scene. To address the problem, we present an improved RBPF Lidar SLAM system with loop detection and correction named LCPF. The efficiency and accuracy of loop detection depend on the segmentation of submaps. Instead of dividing the submap at fixed number of laser scan like existing method, Dynamic Submap Segmentation is proposed in LCPF. This segmentation algorithm decreases the error inside the submap by splitting the submap where there is high scan match error and later rectifies the error by an improved pose graph optimization between submaps. In order to segment the submap at appropriate point, when to create a new submap is determined by both the accumulation of scan match error and the particle distribution. Furthermore, LCPF uses branch and bound algorithm as basic detector for loop detection and multiple criteria to judge the reliability of a loop. In the criteria, a novel parameter called usable ratio was proposed to measure the useful information that a laser scan containing. Finally, comparisons to existing 2D-Lidar mapping algorithm are performed with a series of open dataset simulations and real robot experiments to demonstrate the effectiveness of LCPF.

For an active distribution system (ADS) that integrates high levels of distributed generators (DGs), the control dimension of such a wide and dynamic set of resources would become overwhelming. This study proposes a novel pinning group consensus (PGC)-based distributed coordination control to simplify ADS control by using the virtual clusters building block concept. A given ADS is rethought as coupled small virtual clusters, including virtual microgrid clusters and virtual power plant clusters; accordingly, the ADS can be coordinated by controlling a small number of selected pinning agents instead of a huge number of DGs. The predefining of the PGC values for these pinning agents, which comprehensively considers both clusters features and DG capacities, is the most distinguishing work for the proposed control scheme and can lead to effective global coordination in a distributed manner. Simulation cases under normal/disturbed/emergency conditions verify the effectiveness and advantages of the proposed scheme.