Inverse Kinematics Analysis of a 7-DOF Space Manipulator for Trajectory Design

To solve the inverse kinematics problem for redundant degrees of freedom（DOFs）manipulators has been and still continues to be quite challenging in the field of robotics.Aiming at trajectory planning for a 7-DOF space manipulator system,joint rotation trajectories are obtained from predetermined motion trajectories and poses of the end effector in Cartesian space based on the proposed generalized inverse kinematics method.A minimum norm method is employed to choose the best trajectory among available trajectories.Numerical simulations with the7-DOF manipulator show that the proposed method can achieve the planned trajectory and pose under the circumstances of minimum angular velocities.Moreover,trajectory results from the proposed kinematics model and inverse kinematics method has the advantages of simple modelling,low computation cost,easy to solve and plan trajectory conveniently.The smooth and continuous joint rotation functions obtained from the proposed method are suitable for practical engineering applications.

An approach to wide-field imaging of linear rail ground-based SAR in high squint multi-angle mode

Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. The practical applications drive us to make improvements on the conventional linear rail GB-SAR system in order to achieve larger field imaging. First, a turntable is utilized to support the rotational movement of the radar.Next, a series of high-squint scanning is performed with multiple squint angles. Further, the high squint modulation phase of the echo data is eliminated. Then, a new multi-angle imaging method is performed in the wave number domain to expand the field of view. Simulation and real experiments verify the effectiveness of this method.

A Straightforward Direct Traction Boundary Integral Method for Two-Dimensional Crack Problems Simulation of Linear Elastic Materials

This paper presents a direct traction boundary integral equation method(DTBIEM)for two-dimensional crack problems of materials.The traction boundary integral equation was collocated on both the external boundary and either side of the crack surfaces.The displacements and tractions were used as unknowns on the external boundary,while the relative crack opening displacement(RCOD)was chosen as unknowns on either side of crack surfaces to keep the single-domain merit.Only one side of the crack surfaces was concerned and needed to be discretized,thus the proposed method resulted in a smaller system of algebraic equations compared with the dual boundary element method(DBEM).A new set of crack-tip shape functions was constructed to represent the strain field singularity exactly,and the SIFs were evaluated by the extrapolation of the RCOD.Numerical examples for both straight and curved cracks are given to validate the accuracy and efficiency of the presented method.

Multi-Feature Fusion-Based Instantaneous Energy Consumption Estimation for Electric Buses

Dear Editor, This letter presents a multi-feature fusion-based method for estimating the instantaneous energy consumption of electric buses. More specifically, to improve the accuracy of instantaneous energy consumption estimation of electric buses, we propose a new energy consumption estimation method based on random forest regression(RFR) with multi-feature fusion. The multi-feature includes driving behavior, vehicle status, and external environment.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

Experimental study on evolution law for particle breakage during coal and gas outburst

Coal and gas outburst is a dynamic phenomenon in underground mining engineering that is often accompanied by the throwing and breakage of large amounts of coal.To study the crushing effect and its evolution during outbursts,coal samples with different initial particle sizes were evaluated using a coal and gas outburst testing device.Three basic particle sizes,5–10 mesh,10–40 mesh,and 40–80 mesh,as well as some mixed particle size coal samples were used in tests.The coal particles were pre-compacted at a pressure of 4 MPa before the tests.The vertical ground stress(4 MPa)and the horizontal ground stress(2.4 MPa)were initially simulated by the hydraulic system and maintained throughout.During the tests,the samples were first placed in a vacuum for 3 h,and the coal was filled with gas(CH4)for an adsorption time of approximately 5 h.Finally,the gas valve was shut off and the coal and gas outburst was induced by quickly opening the outburst hole.The coal particles that were thrown out by the outburst test device were collected and screened based on the particle size.The results show the following.(1)Smaller particle sizes have a worse crushing effect than larger sizes.Furthermore,the well-graded coal particles are weakly broken during the outburst process.(2)As the number of repeated tests increases,the relative breakage index grows;however,the increment of growth decreases after each test,showing that further fragmentation becomes increasingly difficult.

Model Predictive Control for Electrical Drive Systems-An Overview

This paper reviews the classification and application of the model predictive control(MPC)in electrical drive systems.Main attention is drawn to the discrete form of MPC,i.e.finite control set model predictive control(FCS-MPC),which outputs directly the switching states of power converters.To show the diversity and simple realization with various control performances of the strategy,in this paper,several different FCS-MPCs with their working mechanisms are introduced.Comparison of FCS-MPC with conventional control strategies for electric drives is presented.Furthermore,extensive control issues,e.g.encoderless control and disturbance observation are also included in this work.Finally,the trend of research hot topics on MPC is discussed.

State of health estimation for lithium-ion batteries in real-world electric vehicles

The state of health(SOH) plays a significant role in the mileage and safety of an electric vehicle(EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for SOH estimation. However, most of these proposed methods cannot be applied to real-world EVs. Here, we present a method for SOH estimation based on realworld EV data. A battery-aging evaluation health index(HI) with a strong correlation to the SOH is retrieved from battery-aging data and then modified with thermal factors to depict the former SOH. Afterward, a local weighted linear-regression algorithm is used to qualitatively characterize the declining trend of the HI, which eliminates the local HI fluctuation caused by data noise.Subsequently, a series of features-of-interest(FOIs) is extracted according to the battery consistency, cell-voltage extrema, and cumulative mileage, and validated using the grey relational analysis. Finally, a battery-degradation model is built using the extreme gradient-boosting algorithm with the selected FOIs. The experimental results from real-world data indicate that the proposed method has high estimation accuracy and generalization, and the maximum error is around 2% for batteries in realworld EVs.

Optical Wireless Communications

The optical spectrum can serve as a good spectrum resource for wideband wireless communications. The advantages of optical wireless communications （OWC） mainly lie in two aspects： the potential large transmission bandwidth due to the high-frequency carrier, and thecommunication security due to no radio-frequency radiation. Thus OWC can be applied in the seenarios where the radio silence is required or the radio frequency radiation may cause explosions, for example in the battle field or some special ar- eas in the storehouses.

Regional Emigration—China's New Approach to Geo-Disaster Mitigation

INTRODUCTION In southern Shaanxi,China,the government is implementing a“pre-emptive”migration away from remote mountains and contiguous areas with extreme poverty(Figure 1).The direct aim is to eradicate extreme poverty and protect the ecological integrity in mountainous areas.However,this policy can not only reduce human damage to natural protected areas and wildlife habitats,but more importantly,bid farewell to the areas where geo-disasters are frequent,the risk of farmers'exposure to disasters is eliminated.

Defect detection and repair algorithm for structures generated by topology optimization based on 3D hierarchical fully convolutional network

Customized 3D-printed structural parts are widely used in surgical robotics.To satisfy the mechanical properties and kinematic functions of these structural parts,a topology optimization technique is adopted to obtain the optimal structural layout while meeting the constraints and objectives.However,topology optimization currently faces some practical challenges that must be addressed,such as ensuring that structures do not have significant defects when converted to additive manufacturing models.To address this problem,we designed a 3D hierarchical fully convolutional network(FCN)to predict the precise position of the defective structures.Based on the prediction results,an effective repair strategy is adopted to repair the defective structure.A series of experiments is conducted to demonstrate the effectiveness of our approach.Compared to the 2D fully convolutional network and the rule-based detection method,our approach can accurately capture most defect structures and achieve 89.88%precision and 95.59%recall.Furthermore,we investigate the impact of different ways to increase the receptive field of our model,as well as the trade-off between different defect-repairing strategies.The results of the experiment demonstrate that the hierarchical structure,which increases the receptive field,can substantially improve the defect detection performance.To the best of our knowledge,this paper is the first to investigate 3D defect prediction and repair for topology optimization in conjunction with deep learning algorithms,providing practical tools and new perspectives for the subsequent development of topology optimization techniques.

Postmastectomy radiotherapy in breast reconstruction:Current controversies and trends

Breast cancer is the most common cancer among women worldwide.Postmastectomy radiotherapy(PMRT)is an essential component of combined therapy for early-stage,high-risk breast cancer.Breast reconstruction(BR)is often considered for patients with breast cancer who have undergone mastectomy.There has been a considerable amount of discussion about the optimal approach to combining PMRT with BR in the treatment of breast cancer.PMRT may increase the risk of complications and prevent good aesthetic results after BR,while BR may increase the complexity of PMRT and the radiation dose to surrounding normal tissues.The purpose of this review is to give a broad overview and summary of the current controversies and trends in PMRT and BR in the context of the most recent literature available.

Recognizing the orbital angular momentum(OAM)of vortex beams from speckle patterns

The orbital angular momentum(OAM)of vortex beams offers a new degree for information encoding,which has been applied to optical communications.OAM measurement is essential for these applications,and has been realized in free space by several methods.However,these methods are inapplicable to estimate the OAM of vortex beams directly from the speckle patterns in the exit end of a multimode fiber(MMF).To tackle this issue,we design a convolutional neural network(CNN)to realize 100%accuracy recognition of two orthogonally polarized OAM modes from speckle patterns.Moreover,we demonstrate that even when the speckle patterns are cropped to only 1/64 of the original patterns,the recognition accuracy of the designed neural network is still higher than 98%.We also study the recognition accuracy of cropped speckles in different areas of speckle patterns to verify the feasibility of OAM recognition after cropping.The results demonstrate that recognizing the OAMs of two orthogonally polarized vortex beams from only a portion of speckle patterns in the exit end of an MMF is feasible,offering the potential to construct a 1×N data transmission scheme.

Fast image reconstruction network in image stitching

Compared with the traditional feature-based image stitching algorithm,the free-view image stitching algorithm based on deep learning has the advantages of fast stitching speed and good effect.However,these algorithms still cannot achieve real-time splicing speed.For the image reconstruction stage,we redesign a new fast image reconstruction network.This network is designed based on ShuffleNet,and the new network structure and loss function will reduce the time required for image reconstruction.In addition,this network can also reduce the performance loss after the network is lightweight.It is proved by experiments that the fast image reconstruction network can realize real-time high-resolution free-view image reconstruction.

Predictive Field-Oriented Control for Electric Drives

Model predictive field-oriented control(PFOC)is a novel control strategy belonging to the class of finite control set model predictive control(FCS-MPC)for electric drive systems.It is a direct control scheme which minimizes the cost function through the convergence of errors between the given and predictive stator currents.In this paper,PFOC is thoroughly presented and analyzed through its dynamics and steady state operations under wide range of speed and varying torque conditions.In light of PFOC’s model based characteristics,its robustness an d influence under model parameters’mismatch is also investigated.Experiments are conducted to show these performance and sensitivities.

Position encoded asymmetrically clipped optical orthogonal frequency division multiplexing in visible light communications

A PE-ACO-OFDM(Position-Encoded Asymmetrically Clipped Optical Orthogonal Frequency Division Multiplexing)signaling scheme for intensity modulation and direct detection is introduced in this paper,where the anti-asymmetry characteristics of ACO-OFDM are exploited to improve the rate of data transmission.This is achieved by reducing the symbol duration of the ACO-OFDM signal,where only the first half of ACO-OFDM is used to transmit the ACO-OFDM data symbol after inverting its negative samples to positive ones.In addition,encoded ACO-OFDM samples are combined with every ACO-OFDM symbol to allow the receiver to identify the position of the inverted samples.Simulation results are introduced,and it is shown that the data rates of PE-ACO-OFDM improve by 33%compared with ACO-OFDM,when a 256-quadrature amplitude modulation scheme is considered as the encoded constellation order.It is also shown that the signal to noise ratio of the proposed PE-ACO-OFDM is higher by almost 1 dB compared with the traditional ACO-OFDM.