查看更多>>摘要：Real-time proprioception presents a significant challenge for soft robots due to their infinite degrees of freedom and intrinsic compliance.Previous studies mostly focused on specific sensors and actuators.There is still a lack of generalizable technologies for integrating soft sensing elements into soft actuators and mapping sensor signals to proprioception parameters.To tackle this problem,we employed multi-material 3D printing technology to fabricate sensorized soft-bending actuators(SBAs)using plain and conductive thermoplastic polyurethane(TPU)filaments.We designed various geometric shapes for the sensors and investigated their strain-resistive performance during deformation.To address the nonlinear time-variant behavior of the sensors during dynamic modeling,we adopted a data-driven approach using different deep neural networks to learn the relationship between sensor signals and system states.A series of experiments in various actuation scenarios were conducted,and the results demonstrated the effectiveness of this approach.The sensing and shape prediction steps can run in real-time at a frequency of 50 Hz on a consumer-level computer.Additionally,a method is proposed to enhance the robustness of the learning models using data augmentation to handle unexpected sensor failures.All the methods are efficient,not only for in-plane 2D shape estimation but also for out-of-plane 3D shape estimation.The aim of this study is to introduce a methodology for the proprioception of soft pneumatic actuators,including manufacturing and sensing modeling,that can be generalized to other soft robots.
查看更多>>摘要：Surface roughness is one of the most critical attributes of machined components,especially those used in high-performance systems.Online surface roughness monitoring offers advancements comparable to post-process inspection methods,reducing inspection time and costs and concurrently reducing the likelihood of defects.Currently,online monitoring approaches for surface roughness are constrained by several limitations,including the reliance on handcrafted feature extraction,which necessitates the involvement of human experts and entails time-consuming processes.Moreover,the prediction models trained under one set of cutting conditions exhibit poor performance when applied to different experimental settings.To address these challenges,this work presents a novel deep-learning-assisted online surface roughness monitoring method for ultraprecision fly cutting of copper workpieces under different cutting conditions.Tooltip acceleration signals were acquired during each cutting experiment to develop two datasets,and no handcrafted features were extracted.Five deep learning models were developed and evaluated using standard performance metrics.A convolutional neural network stacked on a long short-term memory network outperformed all other network models,yielding exceptional results,including a mean absolute percentage error as low as 1.51％and an R2 value of 96.6％.Furthermore,the robustness of the proposed model was assessed via a validation cohort analysis using experimental data obtained using cutting parameters different from those previously employed.The performance of the model remained consistent and commendable under varied conditions,asserting its applicability in real-world scenarios.
查看更多>>摘要：The energy harvesting technology for the ubiquitous natural wind enables a desirable solution to the issue of distributed sensors in the bridge environmental sensing Internet of Things(IoT)system being restricted to conventional energy supply.In this work,a self-powered system based on a compact galloping piezoelectric-triboelectric energy harvester(GPTEH)is developed to achieve efficient wind energy harvesting.The GPTEH is constructed on the prototype of a cantilever structure with piezoelectric macro-fiber composite(MFC)sheets and a rectangular bluff body with triboelectric nanogenerators(TENGs).Through a special swing-type structural design with iron blocks inside the bluff body,the GPTEH exhibits preferable aerodynamic behavior and excellent energy conversion efficiency,compared to conventional cantilever kind of piezoelectric wind energy harvester(PWEH).The GPTEH also demonstrates the capability of high output power density(PEH of 23.65 W m-2 and TENG of 1.59 Wm-2),superior response wind speed(about 0.5 ms-1),and excellent long-term stability(over 14000 cyclic tests).Furthermore,a power management system is developed to efficiently utilize the output energy from GPTEH to power the sensors and wirelessly transmit environmental data to the terminals.The proposed GPTEH-powered system exhibits a great potential for the bridge environmental monitoring and IoT technologies.
查看更多>>摘要：The surface quality of a corrugated plate directly determines the heat transfer property of the thermal power mechanical apparatus.Traditional detection methods are impractical for real-world production,being slow and destructive.In contrast,the point laser displacement sensor,employing the optical triangle method,emerges as a promising device for assessing parts with variable curvature and highly reflective surfaces.Despite its benefits,high-density sampling by an innate frequency introduces challenges such as data redundancy and a poor signal-to-noise ratio,potentially affecting the efficiency and precision of subsequent data processing.To address these challenges,adjustable frequency data sampling has been developed for this sensor,allowing adaptive sampling for corrugated plate digitization.The process begins with surface digitization to extract discrete points,which are transformed into intersection curves using the B-spline fitting technique.Subsequently,dominant points are identified,considering multigeometric constraints for curvature and arch height.Finally,the sampling signal is adjusted based on the distribution information of dominant points.Comparative results indicate that the proposed method effectively minimizes redundant sampling without compromising the accurate capture of essential geometric features.
查看更多>>摘要：Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoen-coder(Attentive VAE).Explicitly,a multi-criterion feature selection(McFS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the McFS algorithm and the Attentive VAE is verified by ablation experiments,respectively.
查看更多>>摘要：The collective formation control problem of a cluster of rotorcraft unmanned aerial vehicles(UAVs)is investigated in this article.The consensus tracking towards formation centroid with following UAVs forming a predefined configuration around the leader is considered as the objective.Unlike prior studies,the information of the central reference trajectory,which is deemed as a virtual leader in the leader-follower topology,is not directly accessible for partial nodes through the communication network.Therefore,a novel distributed formation tracking control scheme is promoted.Besides,a decentralized saturation observer is employed to estimate the reference acceleration signal of the virtual leader.In the absence of linear velocity measurement,two sliding manifolds are proposed by introducing the relative discrepancy terms of position and velocity.Then a smooth saturation operator in the form of a sigmoid function is applied to generate the command force input.Moreover,under the dilemma of constrained capabilities of the airborne sensors equipped on the rotorcrafts,the angular velocity is difficult to acquire.Two cascaded auxiliary attitude error systems are established on each rotorcraft system to synthesize the rotating torque with no need to require the angular velocity measurement.Due to the strong coupling and nonlinearity of the rotorcraft UAV system,the command angular velocity and the derivatives of command input are hard to obtain.Then a continuous nonlinear differentiator is proposed to work with the difficulties in deriving the explicit expression of system derivatives.Thereafter,a detailed stability analysis is conducted progressively on the angular control loop,reference trajectory observer loop,and the position control loop.A simulation scheme for a cluster of four rotorcraft UAVs tracking sinusoidal trajectory are presented and the formation control results are proven advantageous in comparison with the control protocol in previous literature.
查看更多>>摘要：The two-dimensional Logistic memristive hyperchaotic map(2D-LMHM)and the secure hash SHA-512 are the foundations of the unique remote sensing image encryption algorithm(RS-IEA)suggested in this research.The proposed map is formed from the improved Logistic map and the memristor,which has wide phase space and hyperchaotic range and is exceptionally excellent to be utilized in specific applications.The proposed image algorithm uses the permutation-assignment-diffusion structure.Permutation generates two position matrices in a progressive manner to achieve an efficient random exchange of pixel positions,assignment is carried through on the image pixels of the permutated image to entirely remove the original image information,strengthening the relationship between permutation and diffusion,and loop diffusion in two different directions can use subtle changes of pixels to affect the whole plane.The random key and plain-image SHA-512 hash values are used to produce an additional key,which is then utilized to figure out the permutation parameters and the initial value of a chaotic map.The experimental results with the average NPCR=99.6094％(NPCR:number of pixels change rate),average UACI=33.4638％(UACI:unified average changing intensity),100％pass rate of the targets in the test set,the average correlation coefficient is 0.00075,and the local information entropy is 7.9025,which shows that the algorithm is able to defend against a variety of illegal attacks and provide more trustworthy protection than some of the existing state-of-the-art algorithms.
查看更多>>摘要：Most of nonlinear oscillators composed of capacitive and inductive variables can obtain the Hamilton energy by using the Helmholtz theorem when the models are rewritten in equivalent vector forms.The energy functions for biophysical neurons can be obtained by applying scale transformation on the physical field energy in their equivalent neural circuits.Realistic dynamical systems often have exact energy functions,while some mathematical models just suggest generic Lyapunov functions,and the energy function is effective to predict mode transition.In this paper,a memristive oscillator is approached by two kinds of memristor-based nonlinear circuits,and the energy functions are defined to predict the dependence of oscillatory modes on energy level.In absence of capacitive variable for capacitor,the physical time t and charge q are converted into dimensionless variables by using combination of resistance and inductance(L,R),e.g.,τ=t×R/L.Discrete energy function for each memristive map is proposed by applying the similar weights as energy function for the memristive oscillator.For example,energy function for the map is obtained by replacing the variables and parameters of the memristive oscillator with corresponding variables and parameters for the memristive map.The memristive map prefers to keep lower average energy than the memristive oscillator,and chaos is generated in a discrete system with two variables.The scheme is helpful for energy definition in maps,and it provides possible guidance for verifying the reliability of maps by considering the energy characteristic.
查看更多>>摘要：Due to the rapid advancement of technologies,there has been a significant increase in the discharge of industrial wastewater,and freshwater is becoming a scarce resource.Currently,research on solar evaporators is mainly focused on the efficient production of clean water,with less emphasis on the removal of residual pollutants remaining in the original solutions.Through this study,problems,including the difficult recovery of catalyst powder and the difficult removal of floating organic matter are solved by co-depositing low-surface-tension zirconia particles and bismuth tungstate onto the floating layer.Hydrogels and melamine sponges were combined to solve the problem that traditional hydrogels lack mechanical strength.An excellent water-repellent effect can be seen from the contact angle between the liquid globule and canvas/felt,which is greater than 155°.The steam generation rate of the assembled evaporation system is 1.78 kg m-2 h-1,and its purification efficiency for methyl orange and rhodamine B exceeds 99％.This study presents a novel strategy for treating wastewater contaminated with organic dyes,aiming to solve problems including environmental damage,water pollution,and water scarcity.
查看更多>>摘要：Kink oscillations,which are frequently observed in coronal loops and prominences,are often accompanied by extreme-ultraviolet(EUV)waves.However,much more needs to be explored regarding the causal relationships between kink oscillations and EUV waves.In this article,we report the simultaneous detection of kink oscillations and EUV waves that are both associated with an X2.1 flare on 2023 March 03(SOL2023-03-03T17:39).The kink oscillations,which are almost perpendicular to the axes of loop-like structures,are observed in three coronal loops and one prominence.One short loop shows in-phase oscillation within the same period of 5.2 min at three positions.This oscillation could be triggered by the pushing of an expanding loop and interpreted as the standing kink wave.Time lags are found between the kink oscillations of the short loop and two long loops,suggesting that the kink wave travels in different loops.The kink oscillations of one long loop and the prominence are possibly driven by the disturbance of the coronal mass ejection(CME),and that of another long loop might be attributed to the interaction of the EUV wave.The onset time of the kink oscillation of the short loop is nearly same as the beginning of an EUV wave.This fact demonstrates that they are almost simultaneous.The EUV wave is most likely excited by the expanding loop structure and shows two components.The leading component is a fast coronal wave,and the trailing one could be due to the stretching magnetic field lines.
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