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The mist two-phase flow often occurs in the flow measurement of vortex flowmeter. In order to improve the measurement accuracy of vortex flowmeter in mist flow, the characteristics of vortex convection velocity based on crosscorrelation measurement was analyzed in this paper. The vortex-induced pressure fluctuations were acquired by the transient pressure sensors for convection velocity estimation. The experiment tests under various conditions (pressure, gas flowrate and liquid flowrate) were carried out on the developed mist flow facility with film extraction device. The vortex convection velocity and the correlation coefficient were extracted, then the effect of pressure, gas flowrate and droplet content on the vortex convection characteristics were analyzed. The results indicate that the dimensionless convection coefficient is an almost constant with the increase of droplet mass loading, and the correlation coefficient has a positive relationship with droplet mass loading, which are good features for the vortex metering in mist flow, hence is helpful for the further improvement of vortex correlation metering accuracy in mist flow.

in order to help the patients with lower extremity dyskinesia to carry out rehabilitation training task through the exoskeleton, a prediction model of lower extremity joint continuous motion based on the combination of GA-BP neural network and limited amplitude filtering is proposed in this paper. The relationship between surface electromyography and lower-limb joints’ motion using genetic algorithm to optimize the parameters of BP neural network, is studied mainly. Based on optimizing the surface electromyography filtering method, the relationship between the surface electromyography (SEMG) and the knee joint angle mapping were constructed by GA-BP neural network. The model error can be reduced by the optimized filtering of the output results and the continuous prediction of knee joint changes can be achieved too. The root-mean-square error of the predicted knee joint angle before and after optimization is reduced by 24% which is proved by experiments. In addition, the effect of different muscles on joint angle and the robustness of the prediction model are analyzed by reducing the input value of surface electromyography.

This paper proposes to design an intelligent watering robot designed to reduce human resources, reduce the labor intensity of manual spraying, improve the efficiency of watering bonsai, and accomplish the automatic intelligent function of watering bonsai. By selecting stm32f4 based on ARM Cortex-M4 core as the control center of mobile robot, linear CCD image acquisition sensor TSL1401 as line patrol module, microcontroller MSP430, YL-69 soil moisture detection sensor as soil moisture detection module, NRF24L01 wireless transceiver chip as a wireless communication module, it can automatically walk along a predetermined path and perform watering operations on bonsai that needs to be watered. In addition, a D-H coordinate system was established for the RPP spray manipulator mounted on the robot mobile platform. On this basis, the Lagrangian approach was employed in deriving the dynamic model of a three-DOF manipulator, and the dynamic simulation analysis is carried out to analyze the force/torque change curve of the manipulator, which can provide a theoretical basis for the dynamic design and dynamic control of the manipulator.

A method of gait movement of lower limbs for a rehabilitation robot is proposed in this article. The gait detection system is established using acceleration sensors, gyroscopes and magnetometers as the attitude detection devices. According to the movement mechanism of human limbs, attitude sensors are respectively installed in the lateral middle position of the left and right thighs and calves, and the collected data is transmitted to a microprocessor through IIC communication. The processing terminal displays the data curve in real time while saving the collected data. Since the acceleration sensors, magnetometers and gyroscopes all have a certain degree of zero drift, ellipsoid fitting which based on least squares method is employed to calibrate, and then quaternion method is adopted to solve the attitude algorithm. The second-order Kalman filtering for attitude compensation is used in this research work. Finally, the simulation and real experimental results proved that the ellipsoid fitting can be well calibrated, in addition, second-order Kalman filtering is capable of performing good attitude fusion. Furthermore, during walking, the characteristic states such as turning and unstable walking can be collected, which is more obvious than the states collected by the three-dimensional gait analyzer.

In this paper, genetic algorithm is proposed to optimize the mechanical structure. In order to assist patients with lower limb movement disorder in rehabilitation training, assist patients to stand, and achieve the purpose of rehabilitation training, a lower limb rehabilitation mobile device was designed. Because the patient is in the early stage of recovery, lower limbs cannot stand on their own. The weight reduction mechanism is an important part in the whole mobile equipment. The weight reduction mechanism can reduce the requirement of leg strength and satisfy the precondition of early rehabilitation training. At present, the orthogonal method has been used to design the weight reduction mechanism. Using genetic algorithm to optimize mechanical structure can achieved the goal of reducing the volume and weight of the structure without decreasing the output force. From the optimization simulation results, it can be seen that the genetic algorithm can be used to optimize the mechanical structure design, and the volume of the weight reduction mechanism decreases by 48%. Compared with orthogonal design, genetic algorithm is an efficient structural optimization method.

Autonomous underwater vehicle (AUV) plays an important role in complex underwater environments such as marine surveys, due to their flexibility and autonomy. One of the main challenge for AUVs is autonomous navigation technology for the safety and effectiveness of AUV missions. Simultaneous localization and mapping (SLAM) plays an important role in autonomous navigation. However, a suitable optimization algorithm can not only improve the performance of navigation, but also can save running time for AUV. In this paper, we adopted two algorithms of optimization in Incremental Smoothing and mapping (iSAM) for AUV. Several sea trials were performed at Jiaozhou Bay, Qingdao City, Shandong Province. Compared with the result of trajectory using Dog-Leg for optimization, the Gauss-Newton track is more close to the ground truth. At the same time, RMSE of Gauss-Newton trajectory and the computational time is superior obviously to that of Dog-Leg. We consider the Gauss-Newton method is more suitable to perform optimization in iSAM for AUV.

In order to solve the problems of low filtering accuracy and poor real-time performance of standard algorithm in strong nonlinearity, a SVD Unscented Particle Filter (SVDUPF) based on strong tracking and Kullback-Lerbler Distance (KLD) resampling is proposed. Firstly, the stability of covariance matrix is guaranteed by singular value decomposition. Then the multi-fading factor adaptive adjustment covariance matrix in the strong tracking theory is introduced. Finally, the number of particles required for the next filtering iteration is determined according to the KLD sampling principle. Eliminating unnecessary particles and using the minimum number of particles on the premise of ensuring accuracy can improve navigation accuracy, achieve powerful tracking and reduce computational complexity. By comparing the results of the simulation Extended Kalman Filter (EKF) algorithm, the feasibility and application of the Unscented Particle Filter (UPF) algorithm and SVDUPF in AUV navigation positioning algorithm are tested. The correctness of SVDUPF algorithm is proved.

In order to improve the overall reliability of the Autonomous Underwater Vehicle (AUV), and actuator play an important role in AUV systems. Therefore, research on actuator fault diagnosis was carried out. The objective of this paper is to address the problem of Fault Diagnosis of the AUV actuator under winding faults. The goal is pursued through Finite Impulse Response (FIR) and Principal Component Analysis (PCA). The results showed that the PCA could be applied to the actuator fault diagnosis of AUV, and solved the problem of determining the fault source.

The safety of cables is an important factor affecting the safety of production, and the safety detection of running state is an important way to ensure safety. When the current passing through the cable exceeds the safe value, the gas will be released in accordance with a certain rule, which is related to the current value and the overcurrent time. This paper measures the concentration of SO2, NOx, CO2, CO and hydrogen halide (mainly HCl) released by the cable when different currents passes through the cable, and then selects the three conditions closest to the conditions to be measured in the working condition database. The improved entropy weight method is used to calculate the safety index of the cable and the length of the cable over current. A set of measuring system is designed and built for example verification. The validity of the method is proved by an example.

Performing and robust navigation systems for Autonomous Underwater Vehicles (AUVs) play a critical role in scientific and industrial missions (e.g. exploration and surveillance of archaeological sites, reconnaissance and patrolling for military operations, Oil & Gas operations). The quality of the navigation algorithm for the estimation of the AUV navigation state affects the performance of the overall system strongly. Underwater Navigation methods can be divided into three main classes, Kalman filter-based, particle filter-based and graph-based, the advantages and disadvantages of each class are overviewed. In this paper, the authors present a performance comparison between three underwater navigation algorithms based on extend Kalman filter (EKF), particle filter (PF), incremental smoothing and mapping (iSAM). These approaches have been currently tested offline running on the experimental data collected with the Sailfish AUV during different missions on the bridge of Jiaozhou Bay and Menlou Reservoir. Different navigation algorithms have been tested by experimental data. The research's outcome has shown the advantage and disadvantage of these underwater navigation algorithms.

Video segmentation is a necessary step for object tracking. Existing methods that are used to extract object from the background based on an intensive sequence of searching all across the frames, thus this process performs lots of searching works result with low efficiency, whereas other methods obtain segmentation by clustering pixels which resulting in over-segmentation. Inspired by breakthroughs in semantic segmentation, in this paper, we propose to combine appearance and dynamic cues, which is a common conception and plays a key role in successfully segmenting objects in videos. To implement this idea, we combine Deep Convolutional Neural Network (DCNN) and optical flow information of two continuous frames. To overcome the difficulty of segmentation of underwater object in videos induced by the presence of different types of suspension particle from like the water droplets and dust particles to the poor lighting and over lighting conditions, In this work, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and a simple color resign method are used to enhance details and reduce greenish and bluish effects. Some DCNN variants are applied to semantic segmentation and achieve great efficiency. Specifically, because DCNN can obtain different spatial scale information, as a DCNN variant, DeepLab gets a good performance in semantic segmentation. By using atrous convolution, DeepLab network's filters can observe greater receptive field without reducing the feature map dimension, therefore this structure keeps global and position information. Consequently, we compromises above mentioned methods, The optical flow estimation is carried out on the image processed by the CLAHE method, and the accurate segmentation results are obtained by using the DeepLab network. Experiments show good performance of our method.

As an important platform for oceanographic survey, Autonomous Underwater Vehicles (AUV) have been widely applied to many fields such as the ocean research, oil and gas exploitation, fishing and military. The importance of underwater acoustic positioning become more obvious in AUV research missions, ultra short baseline (USBL) because of its lightweight and other advantages with widespread concern. The positioning accuracy of the USBL can be affected by its own or external factors, although a lot of effort has been spent on improving these weaknesses, the efficient and effective method has yet to be developed. A novel positioning system based Adaptive Kalman filter(AKF) is designed for USBL in AUV, AKF can adaptively adjust the measurement covariance matrix and the process noise covariance matrix by utilizing latest measurement data. Experimental results demonstrate that our system outperforms than simple USBL positioning in positioning accuracy.

Through the complex underwater environment, Autonomous Underwater Vehicle (AUV) must use an effective and accurate navigation algorithm to ensure precise localization and autonomous navigation without satellite navigation system. As a development of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) has the advantages of simple implementation, high filtering accuracy and good convergence. Nevertheless, unknown system noise statistics will cause decreased filtering accuracy or even divergence. This paper proposed a navigation algorithm combined the Extreme Learning Machine (ELM) and UKF (ELM-UKF) for the purpose of improving the navigation accuracy. The proposed algorithm uses ELM to establish a system model including error terms to correct the system error. Through the sea trial in Menlou Reservoir using own AUV platform, Sailfish, the performance of AUV navigation based on ELM-UKF algorithm is significantly superior to the standard EKF and standard UKF, revealing that the proposed algorithm successfully improving the accuracy of navigation system.

As we all known, autonomous navigation system is the basic of AUV movement, besides, accurate position estimation and reliable measurement are the keys of it. In order to avoid accumulating position errors in the long voyage by using navigation algorithm, we can depend on GPS. Due to the influence of diving and floating process, GPS data would slip. The slips affect the accuracy of navigation seriously and cause errors for AUV in path planning. Little slips can be accepted, while we can't tolerate large slips up to several hundred meters. This paper proposed the GPS filter algorithm, which combined with navigation algorithm such as DR, EKF and iSAM (incremental smoothing and mapping). In order to verify the validity and feasibility of the combination of navigation algorithm and the proposed algorithm, Sea trials were carried out on the high seas of the Yellow Sea. Experimental results and analysis show that the autonomous navigation approach combined GPS filter with navigation algorithm improves the accuracy of navigation compared with conventional method, at the same time iSAM has a higher accuracy than the other two algorithms.