Shaped charge penetration into high-and ultrahigh-strength Steel-Fiber reactive powder concrete targets

Experiments on shaped charge penetration into high and ultrahigh strength steel-fiber reactive powder concrete(RPC) targets were performed in this paper.Results show that the variation of penetration depth and crater diameter with concrete strength is different from that of shaped charge penetration into normal strength concrete(NSC).The crater diameter of RPC is smaller than that of NSC penetrated by the shaped charge.The jet particles are strongly disturbed and hardly reach the crater bottom because they pass through the narrow channel formed by jet penetration into the RPC.The effects of radial drift velocity and gap effects of jet particles for a shaped charge penetration into RFC target are discussed.Moreover,a theoretical model is presented to describe the penetration of shaped charge into RPC target.As the concrete strength increases,the penetration resistance increases and the entrance crater diameter decreases.Given the drift velocity and narrow crater channel,the low-velocity jet particles can hardly reach the crater bottom to increase the penetration depth.Moreover,the narrow channel has a stronger interference to the jet particles with increasing concrete strength;hence,the gap effects must be considered.The drift velocity and gap effects,which are the same as penetration resistance,also have significant effects during the process of shaped charge penetration into ultrahigh-strength concrete,The crater profiles are calculated through a theoretical model,and the results are in good agreement with the experiments.

A NOVEL SIMULTANEOUS NOISE AND INPUT VSWR MATCHING TECHNIQUE FOR BROADBAND LNA

The Simultaneous Noise and Input Voltage Standing Wave Ratio (VSWR) Matching (SNIM) condition for Low Noise Amplifier (LNA), in principle, can only be satisfied at a single fre-quency. In this paper, by analyzing the fundamental limitations of the narrowband SNIM technique for the broadband application, the authors present a broadband SNIM LNA systematic design technique. The designed LNA guided by the proposed methodology achieves 10 dB power gain with a low Noise Figure of 0.53 dB. Meanwhile, it provides wonderful input matching of 27 dB across the fre-quency range of 3～5 GHz. Therefore, broadband SNIM is realized.

NUMERICAL SIMULATION FOR FORMED PROJECTILE OF DEPLETED URANIUM ALLOY

The numerical simulation for forming projectile of depleted uranium alloy with the SPH (Smooth Particle Hydrodynamic) algorithm was presented. In the computations the artificial pressures of detonation were used, i.e., the spatial distribution and time distribution were given artificially. To describe the deformed behaviors of the depleted uranium alloy under high pressure and high strain rate, the Johnson_Cook model of materials was introduced. From the numerical simulation the formed projectile velocity, projectile geometry and the minimum of the height of detonation are obtained.

Development and Analysis of an Electrically Actuated Lower Extremity Assistive Exoskeleton

An electrically actuated lower extremity exoskeleton is developed, in which only the knee joint is actuated actively while other joints linked by elastic elements are actuated passively. This paper describes the critical design criteria and presents the process of design and calculation of the actuation system. A flexible physical Human-Robot-Interaction （pHR1） measurement device is designed and applied to detect the human movement, which comprises two force sensors and two gasbags attached to the inner surface of the connection cuff. An online adaptive pHRI minimization control strategy is proposed and implemented to drive the robotic exoskelcton system to follow the motion trajectory of human limb. The measured pHRI information is fused by the Variance Weighted Average （VWA） method. The Mean Square Values （MSV） of pHRI and control torque are utilized to evaluate the performance of the exoskeleton. To improve the comfort level and reduce energy consumption, the gravity compensation is taken into consideration when the control law is designed. Finally, practical experiments are performed on healthy users. Experimental results show that the proposed system can assist people to walk and the outlined control strategy is valid and effective.

A Novel Gait Pattern Recognition Method Based on LSTM-CNN for Lower Limb Exoskeleton

This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exoskeleton to collect motion information,which is used for LSTM-CNN input.This article considers five common gait patterns,including walking,going up stairs,going down stairs,sitting down,and standing up.In the LSTM-CNN model,the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features.To optimize the deep neural network structure proposed in this paper,some hyperparameter selection experiments were carried out.In addition,to verify the superiority of the proposed recognition method,the method is compared with several common methods such as LSTM,CNN and SVM.The results show that the average recognition accuracy can reach 97.78%,which has a good recognition eff ect.Finally,according to the experimental results of gait pattern switching,the proposed method can identify the switching gait pattern in time,which shows that it has good real-time performance.

Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system,which conducts natural and close cooperation with the human by recognizing human locomotion timely.Requiring subject-specific training is the main challenge of the existing approaches,and most methods have the problem of insufficient recognition.This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition(LMR)method based on GA-CNN for a lower limb exoskeleton system.The LMR method is a combination of Convolutional Neural Networks(CNN)and Genetic Algorithm(GA)-based multi-sensor information selection.To improve network performance,the hyper-parameters are optimized by Bayesian optimization.An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method.Twelve locomotion modes,which composed an integral locomotion system for the daily application of the exoskeleton,can be recognized by the proposed method.According to a series of experiments,the recognizer shows strong comprehensive abilities including high accuracy,low delay,and sufficient adaption to different subjects.

Extended State Observer based Attitude Control of a Bird-like Flapping-wing Flying Robot

The attitude control system of a flapping-wing flying robot plays an important role in the precise orientation and tracking of the robot.In this paper,the modeling of a bird-like micro flapping-wing system is introduced,and the design of a sliding mode controller based on an Extended State Observer(ESO)is described.The main design difficulties are the control law and the adaptive law for the attitude control system.To address this problem,a sliding mode adaptive extended state observer algorithm is proposed.Firstly,a new extended state approximation method is used to estimate the final output as a disturbance state.Then,a sliding mode observer with good robustness to the model approximation error and external disturbance is used to estimate the system state.Compared with traditional algorithms,this method is not only suitable for more general cases,but also effectively reduces the influence of the approximation error and interference.Next,the simulation and experiment example is given to illustrate the implementation process.The results show that the algorithm can effectively estimate the state of the attitude control system of the flapping-wing flying robot,and further guarantee the robustness of the model regarding error and external disturbance.