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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Journal of Shanghai Jiaotong University (Science)
Journal Prestige (SJR): 0.143
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1995-8188 - ISSN (Online) 1007-1172
Published by Springer-Verlag Homepage  [2469 journals]
  • Inverse Kinematics Analysis of a 6-DOF Manipulator Using Spherical
           Geometry Method

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      Abstract: Abstract A 6-DOF cooperative manipulator is used for human spinal deformity detection. In order to ensure the scanning quality of spinal deformity and improve the solution rate and speed of inverse motion solution of the manipulator, an inverse kinematics analytical method based on spherical geometry is proposed in this paper. We take the AUBO-i5 collaborative manipulator as the research object, which combines the rapidity of analytical solution with the flexibility of spherical solution. In the Robot Operating System, the simulation experiment solves the inverse kinematics of 10 000 sets of randomly generated postures. The success rate and time-consuming of the solution are calculated. Compared with the two commonly used inverse kinematics solving algorithms, TRAC-IK and KDL, this method has obvious advantages in terms of success rate and average time-consuming.
      PubDate: 2022-10-01
       
  • High Precision Temperature Measurement for Microfluidic Chip Applications

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      Abstract: Abstract Biochemical reaction in microfluidic chip is sensitive to temperature. Temperature precise control in a small size device requires the temperature measurement with high measurement precision. Traditional temperature measurement method usually measures the voltage drop of the thermistor, which is excited by a constant current source. This method requires the constant current source with high precision and stability. The output of the constant current source is influenced by environmental factors, resulting in a larger measurement error. To solve this problem, a proportion method, a two-layer filtering algorithm, and a power management technique were applied to improve the temperature measurement precision. The proportion method can reduce the low frequency fluctuation error. The two-layer filtering algorithm can reduce the high frequency fluctuation error furtherly. The power management technique used can improve the system stability. Through testing the temperature measurement system built, the experimental results show that the fluctuation error can be significantly decreased from 0.5 ◦C to 0.2◦C.
      PubDate: 2022-10-01
       
  • Affective Preferences Mining Approach with Applications in Process Control

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      Abstract: Abstract Traditional industrial process control activities relevant to multi-objective optimization problems, such as proportional integral derivative (PID) parameter tuning and operational optimizations, always demand for process knowledge and human operators’ experiences during human-computer interactions. However, the impact of human operators’ preferences on human-computer interactions has been rarely highlighted ever since. In response to this problem, a novel multilayer cognitive affective computing model based on human personalities and pleasure-arousal-dominance (PAD) emotional space states is established in this paper. Therein, affective preferences are employed to update the affective computing model during human-machine interactions. Accordingly, we propose affective parameters mining strategies based on genetic algorithms (GAs), which are responsible for gradually grasping human operators’ operational preferences in the process control activities. Two routine process control tasks, including PID controller tuning for coupling loops and operational optimization for batch beer fermenter processes, are carried out to illustrate the effectiveness of the contributions, leading to the satisfactory results.
      PubDate: 2022-10-01
       
  • A Novel Method Based on Node’s Correlation to Evaluate Important
           Nodes in Complex Networks

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      Abstract: Abstract Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability. Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation, showing that the interaction between nodes has an influence on the importance of nodes. In this paper, a novel method based on node’s distribution and global influence in complex networks is proposed. The nodes in the complex networks are classified according to the distance matrix, then the correlation coefficient between pairs of nodes is calculated. From the whole perspective in the network, the global similarity centrality (GSC) is proposed based on the relevance and the shortest distance between any two nodes. The efficiency, accuracy, and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes. Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.
      PubDate: 2022-10-01
       
  • Path Planning and Optimization of Humanoid Manipulator in Cartesian Space

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      Abstract: Abstract To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction, an improved bi-directional rapidly-exploring random tree algorithm based on greedy growth strategy in 3D space is proposed. The workspace of manipulator established based on Monte Carlo method is used as the sampling space of the rapidly-exploring random tree, and the opposite expanding greedy growth strategy is added in the random tree expansion process to improve the path planning efficiency. Then the generated path is reversely optimized to shorten the length of the planned path, and the optimized path is interpolated and pose searched in Cartesian space to form a collision-free optimized path suitable for humanoid manipulator motion. Finally, the validity and reliability of the algorithm are verified in an intelligent elderly care service scenario based on Walker2, a large humanoid service robot.
      PubDate: 2022-10-01
       
  • Multilevel Disparity Reconstruction Network for Real-Time Stereo Matching

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      Abstract: Abstract Recently, stereo matching algorithms based on end-to-end convolutional neural networks achieve excellent performance far exceeding traditional algorithms. Current state-of-the-art stereo matching networks mostly rely on full cost volume and 3D convolutions to regress dense disparity maps. These modules are computationally complex and high consumption of memory, and difficult to deploy in real-time applications. To overcome this problem, we propose multilevel disparity reconstruction network, MDRNet, a lightweight stereo matching network without any 3D convolutions. We use stacked residual pyramids to gradually reconstruct disparity maps from low-level resolution to full-level resolution, replacing common 3D computation and optimization convolutions. Our approach achieves a competitive performance compared with other algorithms on stereo benchmarks and real-time inference at 30 frames per second with 4×104 resolutions.
      PubDate: 2022-10-01
       
  • Effects of Elastic Joints on Performances of a Close-Chained Rod Rolling
           Robot

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      Abstract: Abstract In rolling experiments, the performances of spider-like robot are limited greatly by its motors’ driving ability; meanwhile, the ground reaction forces are so great that they damaged the rods. In this paper, we solve above problems both mechanically and by control. Firstly, we design the parameters of the central pattern generator (CPG) network based on the kinematics of the robot to enable a smooth rolling trajectory. And we also analyze the kinematic rolling and dynamic rolling briefly. Secondly, we add torsion springs to the passive joints of the spider-like robot aiming to make use of its energy storage capacity to compensate the insufficient torque. The simulation results show that the optimized CPG control parameters can reduce the fluctuation of the mass center and the ground reaction forces. The torsion spring can reduce the peak torque requirements of the actuated joints by 50%.
      PubDate: 2022-10-01
       
  • Wear Detection System for Elevator Traction Sheave

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      Abstract: Abstract In order to achieve accurate non-contact measurement of the mechanical wear of the elevator traction sheave grooves, a wear detection system based on machine vision was developed. The industrial camera was fixed through a special fixture, and the images were collected by aligning each groove. In this paper, target groove is extracted based on normalized correlation coefficient matching. Corner points are extracted to describe the contour of the traction wheel groove. The inflection point of the wheel groove boundary is determined by straight boundary fitting, and the amount of rope groove wear is calculated by geometric knowledge. Based on the physical model structure, a mathematical model is established to eliminate the unavoidable occlusion error. The experimental results show that this system can carry out an accurate quantitative analysis and realize an accurate measurement of the wear of the traction sheave rope groove with convenience and accuracy.
      PubDate: 2022-10-01
       
  • Event-Triggered Control of Positive Semi-Markovian Jump Systems
           Without/with Input Saturation

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      Abstract: Abstract This paper is concerned with the event-triggered control of positive semi-Markovian jump systems without/with input saturation. The considered systems are subject to a stochastic semi-Markovian process whose sojourn time is dependent on a non-exponential distribution. First, an event-triggering condition is introduced in a linear form for the systems. A class of event-triggered feedback controllers is proposed using matrix decomposition technique. By using a stochastic co-positive Lyapunov function, the systems’ positivity and stability are guaranteed. Then, the obtained results are developed for the systems with input saturation. A cone set is chosen as the attraction domain and the corresponding attraction domain gain matrix is designed in terms of standard linear programming approach. Finally, two numerical examples are provided to verify the validity and effectiveness of the presented theoretical findings.
      PubDate: 2022-10-01
       
  • Improved Spatial Registration Algorithm for Sensors on Multiple Mobile
           Platforms

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      Abstract: Abstract This paper focuses on the spatial registration algorithm under the earth-center earth-fixed (ECEF) coordinate system for multiple mobile platforms. The sensor measurement biases are discussed with the attitude information of the platform into consideration. First, the biased measurement model is constructed. Besides, the maximum likelihood registration (MLR) algorithm is discussed to simultaneously estimate the measurement biases and the target state. Finally, an improved online MLR (IMLR) algorithm is proposed through a sliding window of adaptive size. Simulation results demonstrate that the proposed IMLR algorithm effectively improves the realtime ability of the system and can approach similar estimation accuracy to the conventional MLR algorithm.
      PubDate: 2022-10-01
       
  • Target Detection Algorithm Based On Human Judge Mechanism

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      Abstract: Abstract A D-G-YOLOV3 algorithm was proposed to identify and judge recyclables, which introduced a dense feature network to replace the feature pyramid network. The network closely connects and fits the feature maps and simulates human judgment mechanism. A three-stage judgment is made for judgment objects with lower confidence. Based on the judgment of the original image, the second-stage judgment is carried out after the channel contrast is increased. Finally, sampling is performed on the region of interest where the second-stage confidence score wins for the third stage of judgment, and then judgment result is sent to the gated recurrent unit network for final inference. The result shows that through experiments on the same recyclables data set, the algorithm reduces the missed detection rate by 15.54%, and the false detection rate by 0.97%, while improves the accuracy rate by 16.51%.
      PubDate: 2022-10-01
       
  • Game Theory Based Sensor Management in Reducing Target Threat Level
           Assessment Risk

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      Abstract: Abstract Sensor management schemes are calculated to reduce target threat level assessment risk in this paper. Hidden Markov model and risk theory are combined to build the target threat level model firstly. Then the target threat level estimation risk is defined. And the sensor management schemes are optimized with the smallest target threat level assessment risk. What’s more, the game theory is applied to calculate the optimal sensor management scheme. Some simulations are conducted to prove that the proposed sensor management method is effective.
      PubDate: 2022-10-01
       
  • A Class of Distributed Variable Structure Multiple Model Algorithm Based
           on Posterior Information of Information Matrix

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      Abstract: Abstract The tracking of maneuvering targets in radar networking scenarios is studied in this paper. For the interacting multiple model algorithm and the expected-mode augmentation algorithm, the fixed base model set leads to a mismatch between the model set and the target motion mode, which causes the reduction on tracking accuracy. An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed. The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set. Furthermore, combining with the unscented information filtering, and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy, a distributed target tracking algorithm based on the posterior information of the information matrix is proposed. For the problem of filtering divergence while target is leaving radar surveillance area, a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.
      PubDate: 2022-10-01
       
  • Adaptive Human-Robot Collaboration Control Based on Optimal Admittance
           Parameters

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      Abstract: Abstract In order to help the operator perform the human-robot collaboration task and optimize the task performance, an adaptive control method based on optimal admittance parameters is proposed. The overall control structure with the inner loop and outer loop is first established. The tasks of the inner loop and outer loop are robot control and task optimization, respectively. An inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is then proposed, which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator. Subsequently, the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force. The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model. The model includes the unknown dynamics of the operator and the task performance details. To relax the requirement of the system model, the integral reinforcement learning is employed to solve the linear quadratic regulator problem. Besides, an auxiliary force is designed to help the operator complete the specific task better. Compared with the traditional control scheme, the security performance and interaction performance of the human-robot collaboration system are improved. The effectiveness of the proposed method is verified through two numerical simulations. In addition, a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method.
      PubDate: 2022-10-01
       
  • Bending Prediction Method of Multi-Cavity Soft Actuator

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      Abstract: Abstract The multi-cavity soft actuator is assembled from single-cavity soft actuator through a reasonable geometric distribution. It has the characteristic that the pneumatic soft actuator is driven by its own deformation and has more degrees of freedom. Pneumatic soft actuator is widely used as an emerging discipline and its strong compliance has been greatly developed and applied. However, as the most application potential type of soft actuators, there is still a lack of simple and effective deformation prediction methods for studying the spatial deformation of multi-cavity soft actuators. To solve this problem, a vector equation method is proposed based on the analysis of the principle of the space deformation of the two-cavity, three-cavity and four-cavity soft actuators. Furthermore, a nonlinear mathematical model of the air pressure, space position and deformation trajectory of the soft actuator end is established by combining the vector equation method. Finally, the three-channel soft actuator is verified through experiments. The results show that the mathematical model can better predict the space deformation trajectory of the soft actuator, which provides a new research method for studying the space deformation of the multi-channel soft actuator.
      PubDate: 2022-10-01
       
  • Hysteresis Modeling and Compensation for Distal Shaft Deflection of
           Flexible Ureteroscope

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      Abstract: Abstract Flexible ureteroscopy (FURS) has been widely used in the diagnosis and treatment of upper urinary tract diseases. The key operation of FURS is that the surgeon manipulates the distal shaft of flexible ureteroscope to a specific target for diagnosis and treatment. However, the hysteresis of flexible ureteroscope may be one of the most important factors that degrade the manipulation accuracy and the surgeon usually spends a long time navigating the distal shaft during surgery. In this study, we obtained hysteresis curves of distal shaft deflection for the flexible ureteroscope through extensive repeated experiments. Then, two methods based on piecewise linear approximation and long short-term memory neural network were employed to model the hysteresis curves. On this basis, we proposed two hysteresis compensation strategies for the distal shaft deflection. Finally, we carried out hysteresis compensation experiments to verify the two proposed compensation strategies. Experimental results showed that the hysteresis compensation strategies can significantly improve position accuracy with mean compensation errors of no more than 5°.
      PubDate: 2022-09-24
       
  • Enhancement of Pinching Grasping Robustness Using a Multi-Structure Soft
           Gripper

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      Abstract: Abstract Recently, soft grippers have garnered considerable interest in various fields, such as medical rehabilitation, due to their high compliance. However, the traditional PneuNet only reliably grasps medium and large objects via enveloping grasping (EG), and cannot realize pinching grasping (PG) to stably grasp small and thin objects as EG requires a large bending angle whereas PG requires a much smaller one. Therefore, we proposed a multi-structure soft gripper (MSSG) with only one vent per finger which combines the PneuNet in the proximal segment with the normal soft pneumatic actuator (NSPA) in the distal segment, allowing PG to be realized without a loss in EG and enhancing the robustness of PG due to the height difference between the distal and proximal segments. Grasping was characterized on the basis of the stability (finger bending angle describes) and robustness (pull-out force describes), and the bending angle and pull-out force of MSSG were analyzed using the finite element method. Furthermore, the grasping performance was validated using experiments, and the results demonstrated that the MSSG with one vent per finger was able to realize PG without a loss in EG and effectively enhance the PG robustness.
      PubDate: 2022-09-24
       
  • Symmetric Nonnegative Matrix Factorization for Vertex Centrality in
           Complex Networks

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      Abstract: Abstract One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading. Most of the current centrality algorithms focus on single feature or manually extract the attributes, which occasionally results in the failure to fully capture the vertex’s importance. A new vertex centrality approach based on symmetric nonnegative matrix factorization (SNMF), called VCSNMF, is proposed in this paper. For highlight the characteristics of a network, the adjacency matrix and the degree matrix are fused to represent original data of the network via a weighted linear combination. First, SNMF automatically extracts the latent characteristics of vertices by factorizing the established original data matrix. Then we prove that each vertex’s composite feature which is constructed with one-dimensional factor matrix can be approximated as the term of eigenvector associated with the spectral radius of the network, otherwise obtained by the factor matrix on the hyperspace. Finally, VCSNMF integrates the composite feature and the topological structure to evaluate the performance of vertices. To verify the effectiveness of the VCSNMF criterion, eight existing centrality approaches are used as comparison measures to rank influential vertices in ten real-world networks. The experimental results assert the superiority of the method.
      PubDate: 2022-09-24
       
  • Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning
           for Noninvasive Prediction of Gleason Grade Groups from MRI

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      Abstract: Abstract The Gleason grade group (GG) is an important basis for assessing the malignancy of prostate cancer, but it requires invasive biopsy to obtain pathology. To noninvasively evaluate GG, an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning. First, a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area. Second, the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area. Finally, in the network training, a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed, which further improves the prediction performance of the network. Experimental results show that the proposed method is better than the traditional network model in predicting GG performance. The quadratic weighted Kappa is 0.471 1 and the positive predictive value for predicting clinically significant cancer is 0.936 9.
      PubDate: 2022-09-24
       
  • Generation Approach of Human-Robot Cooperative Assembly Strategy Based on
           Transfer Learning

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      Abstract: Abstract In current small batch and customized production mode, the products change rapidly and the personal demand increases sharply. Human-robot cooperation combining the advantages of human and robot is an effective way to solve the complex assembly. However, the poor reusability of historical assembly knowledge reduces the adaptability of assembly system to different tasks. For cross-domain strategy transfer, we propose a human-robot cooperative assembly (HRCA) framework which consists of three main modules: expression of HRCA strategy, transferring of HRCA strategy, and adaptive planning of motion path. Based on the analysis of subject capability and component properties, the HRCA strategy suitable for specific tasks is designed. Then the reinforcement learning is established to optimize the parameters of target encoder for feature extraction. After classification and segmentation, the actor-critic model is built to realize the adaptive path planning with progressive neural network. Finally, the proposed framework is verified to adapt to the multi-variety environment, for example, power lithium batteries.
      PubDate: 2022-08-19
       
 
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