Intelligent Sensing and Control of Road Construction Robot Scenes Based on Road Construction

Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.

Illumination Adaptive Identification Algorithm of a Reconfigurable Modular Robot

Reconfigurable modular robots feature high mobility due to their unconstrained connection manners.Inspired by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which could reassemble into various configurations through the compound joint motion.Moreover,an illumination adaptive modular robot identification(IAMRI)algorithm is proposed for CRMR.At first,an adaptive threshold is applied to detect oriented FAST features in the robot image.Then,the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method.After matching features,an improved random sample consensus algorithm is employed to eliminate the mismatched features.Finally,the reconfigurable robot module is identified effectively through the perspective transformation.Compared with ORB,MA,Y-ORB,and S-ORB algorithms,the IAMRI algorithm has an improvement of over 11.6%in feature uniformity,and 13.7%in the comprehensive indicator,respectively.The IAMRI algorithm limits the relative error within 2.5 pixels,efficiently completing the CRMR identification under complex environmental changes.

B-Spline Wavelet on Interval Finite Element Method for Static and Vibration Analysis of Stiffened Flexible Thin Plate

A new wavelet finite element method(WFEM)is constructed in this paper and two elements for bending and free vibration problems of a stiffened plate are analyzed.By means of generalized potential energy function and virtual work principle,the formulations of the bending and free vibration problems of the stiffened plate are derived separately.Then,the scaling functions of the B-spline wavelet on the interval(BSWI)are introduced to discrete the solving field variables instead of conventional polynomial interpolation.Finally,the corresponding two problems can be resolved following the traditional finite element frame.There are some advantages of the constructed elements in structural analysis.Due to the excellent features of the wavelet,such as multi-scale and localization characteristics,and the excellent numerical approximation property of the BSWI,the precise and efficient analysis can be achieved.Besides,transformation matrix is used to translate the meaningless wavelet coefficients into physical space,thus the resolving process is simplified.In order to verify the superiority of the constructed method in stiffened plate analysis,several numerical examples are given in the end.

A Smart Wearable Ring Device for Sensing Hand Tremor of Parkinson’s Patients

Parkinson’s disease(PD)is a very common neurodegenerative disease that occurs mostly in the elderly.There are many main clinical manifestations of PD,such as tremor,bradykinesia,muscle rigidity,etc.Based on the current research on PD,the accurate and convenient detection of early symptoms is the key to detect PD.With the development of microelectronic and sensor technology,it is much easier to measure the barely noticeable tremor in just one hand for the early detection of Parkinson’s disease.In this paper,we present a smart wearable device for detecting hand tremor,in which MPU6050(MIDI Processing Unit)consisting of a 3-axis gyroscope and a 3-axis accelerometer is used to collect acceleration and angular velocity of fingers.By analyzing the time of specific finger movements,we successfully recognized the tremor signals with high accuracy.Meanwhile,with Bluetooth 4.0(Bluetooth Low Energy,BLE)and networking terminal ability,tremor data can be transferred to a monitoring device in real time with extremely lowenergy consumption.The experimental results have shown that the proposed device(smart ring)is convenient for long-term tremor detection which is vital for early detection and treatment for Parkinson’s disease.

A Virtual Puncture Surgery System Based on Multi-Layer Soft Tissue and Force Mesh

Puncture is a common operation in surgery,which involves all kinds of tissue materials with different geometry and mechanical properties.As a new cross-disciplinary research area,Virtual Surgery(VS)makes simulation of soft tissue in puncture operation possible in virtual environment.In this paper,we introduce a VS-based puncture system composed by three-layer soft tissue,simulated with spherical harmonic function(SHF),which is covered with a force mesh,constructed by mass spring model(MSM).The two models are combined together with a parameter of SHF named surface radius,which provides MSM with real-time deformation data needed in force calculation.Meanwhile,force calculation,divided into the surface spring force and the puncture damping force,makes the force presentation better accord to the corresponding tissue characteristics.Moreover,a deformation resumption algorithm is leveraged to simulate the resumption phenomenon of the broken tissue surface.In evaluation experiment,several residents are invited to grades our model along with other four mainstream soft tissue models in terms of 7 different indicators.After the evaluation,the scores are analyzed by a comprehensive weighted grading method.Experiment results show that the proposed model has better performance during puncture operation than other models,and can well simulate surface resumption phenomenon when tissue surface is broken.

Hot spots enriched plasmonic nanostructure-induced random lasing of quantum dots thin film

Here,a plasmon-enhanced random laser was achieved by incorporating gold nanostars（NS） into disordered polymer and Cd Se/Zn S quantum dots（QDs） gain medium films,in which the surface plasmon resonance of gold NS can greatly enhance the scattering cross section and bring a large gain volume.The random distribution of gold NS in the gain medium film formed a laser-mode resonator.Under a single-pulse pumping,the scattering center of gold NS-based random laser exhibits enhanced performance of a lasing threshold of 0.8 m J/cm^2 and a full width as narrow as 6 nm at half maximum.By utilizing the local enhancement characteristic of the electric field at the sharp apexes of the gold NS,the emission intensity of the random laser was increased.In addition,the gold NS showed higher thermal stability than the silver nanoparticles,withstanding high temperature heating up to 200？C.The results of metal nanostructures with enriched hot spots and excellent temperature stability have tremendous potential applications in the fields of biological identification,medical diagnostics,lighting,and display devices.

Design and Implementation of PLC-Based Autonomous Construction System of Unmanned Vibratory Roller

The vibratory roller is a piece of vital construction machinery in the field of road construction.The unmanned vibratory roller efficiently utilizes the automated driving technology in the vehicle engineering field,which is innovative for the unmanned road construction.This paper develops and implements the autonomous construction system for the unmanned vibratory roller.Not only does the roller have the function of remote-controlled driving,but it also has the capability of autonomous road construction.The overall system design uses the Programmable Logic Controller(PLC)as the kernel controller.It establishes the communication network through multiple Input/Output(I/O)modules,Recommended Standard 232(RS232)serial port,Controller Area Network(CAN)bus,and wireless networks to control the roller vehicle completely.The locating information is obtained through the Global Navigation Satellite System(GNSS)satellite navigation equipment group to support the process of autonomous construction.According to the experimental results,the autonomous construction system can finally enable the roller to perform driving operations and construction independently,which was a significant step forward in engineering application.

On Dual-Mode Driving Control Method for a Novel Unmanned Tractor With High Safety and Reliability

Due to the non-standardization and complexity of the farmland environment,it is always a huge challenge for tractors to achieve fully autonomy(work at Self-driving mode)all the time in agricultural industry.Whereas,when tractors work in the Tele-driving(or Remote driving)mode,the operators are prone to fatigue because they need to concentrate for long periods of time.In response to these,a dual-mode control strategy was proposed to integrate the advantages of both approaches,i.e.,by combing Self-driving at most of the time with Tele-driving under special(complex and hazardous)conditions through switching control method.First,the state switcher was proposed,which is used for smooth switching the driving modes according to different working states of a tractor.Then,the state switching control law and the corresponding subsystem tracking controllers were designed.Finally,the effectiveness and superiority of the dualmode control method were evaluated via actual experimental testing of a tractor whose results show that the proposed control method can switch smoothly,stably,and efficiently between the two driving modes automatically.The average control accuracy has been improved by 20%and 15%respectively,compared to the conventional Tele-driving control and Self-driving control with low-precision navigation.In conclusion,the proposed dualmode control method can not only satisfy the operation in the complex and changeable farmland environment,but also free drivers from high-intensity and fatiguing work.This provides a perfect application solution and theoretical support for the intelligentization of unmanned farm agricultural machinery with high safety and reliability.

Detection and Analysis of Strong Ionospheric Scintillation in Equatorial Region

To detect the occurrence of ionospheric scintillation in the equatorial region,a coherent/non-coherent integration method is adopted on the accumulation of intermediate frequency(IF)signal and local code,in the process of signal acquisition based on software receiver.The processes of polynomial fitting and sixth-order Butterworth filtering are introduced to detrend the tracking results.Combining with ionospheric scintillation detection algorithm and preset thresholds,signal acquisition and tracking,scintillation detection,positioning solution are realized under the influence of strong ionospheric scintillation.Under the condition that the preset threshold of amplitude and carrier phase scintillation indices are set to 0.5 and 0.15,and the percentage of scin-tillation occurrence is 50%,respectively,PRN 12 and 31 affected by strong amplitude scintillation are detected effectively.Results show that the positioning errors in the horizontal direction are below 5m approximately.The software receiver holds performances of accurate acquisition,tracking and positioning on the strong ionospheric scintillation conditions,which can provide important basis and helpful guidance for relevant research on ionospheric scintillation,space weather and receiver design with high performance.

LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

GNSS gyroscopes:determination of angular velocity and acceleration with very high-rate GNSS

Although global navigation satellite systems(GNSS)have been routinely applied to determine attitudes,there exists no literature on determining angular velocity and/or angular acceleration from GNSS.Motivated by the invention of computerized accelerometers of the correspondence author and following the success of accurately recovering translational velocity and acceleration waveforms from very high-rate GNSS precise positioning by Xu and his collaborators in 2021,we propose the concept of GNSS gyroscopes and reconstruct angular velocity and acceleration from very high-rate GNSS attitudes by applying regularization under the criterion of minimum mean squared errors.The major results from the experiments can be summarized in the following:(i)angular velocity and acceleration waveforms computed by applying the difference methods to high-rate GNSS attitudes are too noisy and can be physically not meaningful and numerically incorrect.The same can be said about inertial measurement unit(IMU)attitudes,if IMU gyros are not of very high accuracy;(ii)regularization is successfully applied to reconstruct the high-rate angular velocity and acceleration waveforms from 50 Hz GNSS attitudes and significantly outperforms the difference methods,validating the proposed concept of GNSS gyroscopes.By comparing the angular velocity and acceleration results by using the difference methods and regularization,we find that the peak values of angular velocity and acceleration by regularization are much smaller by a maximum factor of 1.57 in the angular velocity to a maximum factor of 8662.53 times in the angular acceleration in the case of high-rate GNSS,and by a maximum factor of 1.26 in the angular velocity to a maximum factor of 2819.85 times in the angular acceleration in the case of IMU,respectively;and(iii)the IMU attitudes apparently lead to better regularized angular velocity and acceleration waveforms than the high-rate GNSS attitudes,which can well be explained by the fact that the former is of better accuracy than the latter.As a result,to suppress the significant amplification of noise in GNSS attitudes,larger regularization parameters have to be chosen for the high-rate GNSS attitudes,resulting in smaller peak angular accelerations by a maximum factor of 37.55 percent in the angular velocity to a maximum factor of 6.20 times in the angular acceleration in comparison of the corresponding IMU results.Nevertheless,the regularized angular acceleration waveforms for both GNSS and IMU look more or less similar in pattern or waveform shape.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks （DBN） is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing work- ing status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine （RBM）, and is trained using layer-by- layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierar- chical representations, which are suitable for fault classi- fication, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

Design and simulation for large parafoil fix line object homing algorithm

Traditional parafoil homing usually uses a point as object. As the mobility of parafoil is limited by its glide ratio and wind, in some cases when the parafoil scatter area is large, or the glide ratio of parafoil is small, the deviation of its landing point to object point will be arduous to control. Accordingly, during these situations, when parafoil is used in recovery of spacecraft or satellite, the landing area of parafoil can be set as a rectangle, and the object of parafoil can be set as a line segment. The thesis of this work is designing an algorithm for parafoil homing using line segment as object. The algorithm of wind velocity and direction calculation in different flying segments was also investigated. The algorithm designed navigates the parafoil to land into the predestined area and largely reduce the probability of recovery loads falling to unwanted area to damage houses and people.

A Real-time Cutting Model Based on Finite Element and Order Reduction

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in real-time.Therefore,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time performance.The proposed model uses the finite element model to construct a deformation model of the virtual lung.Meanwhile,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation computation.In addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual lung.Then,the Bezier curve is adopted to draw the incision outline after the virtual lung has been cut.Finally,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual lung.Experimental results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

Fuzzy Adaptive Control of Delayed High Order Nonlinear Systems

This paper deals with the problem of tracking control for a class of high order nonlinear systems with input delay. The unknown continuous functions of the system are estimated by fuzzy logic systems （FLS）. A state conversion method is introduced to eliminate the delayed input item. By means of the backstepping algorithm, the property of semi-globally uniformly ultimately bounded （SGUUB） of the closed-loop system is achieved. The stability of the closed-loop system is proved according to Lyapunov second theorem on stability. The tracking error is proved to be bounded which ultimately converges to an adequately small compact set. Finally, a computer simulation example of high order nonlinear systems is presented, which illustrates the effectiveness of the control scheme.

Real-time haptic model for soft tissue deformation in surgery simulation

The modelling and simulation of deformable objects is a challenging topic in the field of haptic rendering between human and virtual environment.In this paper,a novel and efficient layered rhombus-chain-connected haptic deformation model based on physics is proposed for an excellent haptic rendering.During the modelling,the accumulation of relative displacements in every chain structure unit in each layer is equal to the deformation on the virtual object surface,and the resultant force of corresponding springs is equivalent to the external force.The layered rhombus-chain-connected model is convenient and fast to calculate,and can satisfy real-time requirement due to its simple nature.Simulation experiments in virtual human liver based on the proposed model are conducted,and the results demonstrate that our model provides stable and realistic haptic feeling in real time.Meanwhile,the display result is vivid.

The torsion model of soft tissue in virtual surgery

In order to solve the problem of real-time soft tissue torsion simulation in virtual surgeries,a torsion model based on coil spring is proposed to actualize real-time interactions and applications in virtual surgeries. The proposed model is composed of several connected coil springs in series. The sum of torsion deformation on every coil is equivalent to the soft tissue surface deformation. The calculation of the model is simple because the method for calculating the torsion deformation for each coil spring is the same. The virtual surgery simulation system is established on PHANTOM OMNI haptic device based on the Open GL 3 D graphic interface and VC + + software,and it is used to simulate the torsion deformation of virtual legs and arms. Experimental results show that the proposed model can effectively simulate the torsion deformation of soft tissue while being of real-time performance and simplicity,which can well meet requirements of virtual operation simulations.

RF characterization of InP double heterojunction bipolar transistors on a flexible substrate under bending conditions

This letter presents the fabrication of InP double heterojunction bipolar transistors(DHBTs)on a 3-inch flexible substrate with various thickness values of the benzocyclobutene(BCB)adhesive bonding layer,the corresponding thermal resistance of the InP DHBT on flexible substrate is also measured and calculated.InP DHBT on a flexible substrate with 100 nm BCB obtains cut-off frequency f_(T)=358 GHz and maximum oscillation frequency f_(MAX)=530 GHz.Moreover,the frequency performance of the InP DHBT on flexible substrates at different bending radii are compared.It is shown that the bending strain has little effect on the frequency characteristics(less than 8.5%),and these bending tests prove that InP DHBT has feasible flexibility.

Pattern Recognition of Motor Imagery EEG using Wavelet Transform

Brain-computer interface (BCI) provides new communication and control channels that do not depend on the brain’s normal output of peripheral nerves and muscles. In this paper, we report on results of developing a single trial online motor imagery feature extraction method for BCI. The wavelet coefficients and autoregressive parameter model was used to extraction the features from the motor imagery EEG and the linear discriminant analysis based on mahalanobis distance was utilized to classify the pattern of left and right hand movement imagery. The performance was tested by the Graz dataset for BCI competition 2003 and satisfactory results are obtained with an error rate as low as 10.0%.