High Precision Performance Research Articles

Video Surveillance Object Forgery Detection using PDCL Network with Residual-based Steganalysis Feature

Video surveillance has various applications in various fields and industries. However, the rapid development of video processing technology has made video surveillance information susceptible to multiple malicious attacks. At present, the state-of-the-art methods, including the latest deep learning techniques, cannot get satisfactory results when addressing video surveillance object forgery detection (VSOFD) due to the following limitations: (i) lack of VSOFD-specific features for effective processing and (ii) lack of effective deep network architecture designed explicitly for VSOFD. This paper proposes a new detection scheme to alleviate these limitations. The proposed approach first extracted VSOFD-specific features via residual-based steganalysis feature (RSF) from the spatial-temporal-frequent domain. Key clues of video frames can be more effectively learned from RSF, instead of raw frame images. Then, the RSF feature is used to form the residual-based steganography feature vector group (RSFVG), which serves as the input of our following network. Finally, a new VSOFD-specific deep network architecture called parallel-DenseNet-concatenated-LSTM (PDCL) network is designed, which includes two improved CNN and RNN modules. The improved CNN module fuses and processes the coarse-to-fine feature extraction and simultaneously preserves the frame independence in video frames. The improved RNN module learns the correlation features between the adjacent frames to identify forgery frames. Experimental results show that the proposed scheme using the PDCL network with RSF can achieve high performance in test error, precision, recall, and F1 scores in our newly constructed dataset (SYSU-OBJFORG + newly generated forgery video clips). Compared to existing SOTA methods, our framework achieves the best F1 score of 90.33%, which is greatly improved by nearly 8%.

Determination of echinococcosis IgG antibodies using magnetic bead-based chemiluminescence immunoassay

Echinococcosis is a kind of parasitic disease shared by humans and animals. The aim of this study was to establish a new detection method for echinococcosis screening using magnetic bead-based chemiluminescence immunoassay (CLIA). A magnetic bead-based CLIA to determine anti-echinococcosis IgG antibodies was optimized and established. The sensitivity, accuracy, precision and recovery rate were evaluated using the national reference serum, and the reference interval, specificity and comparison assays were performed using the clinical negative/positive echinococcosis serum samples. This study established a new CLIA to determine anti-echinococcosis IgG antibodies. The sensitivity of this CLIA method was higher than that of the registered ELISA kit and the national standard, the conformance rate of the negative/positive references was 100% (8/8), the CVs of the sensitivity reference were all below 5%, and the CVs of the precision reference were 5.7%. There was no obvious cross-reactivity with the common parasitic disease-positive serum and serum interferents. Clinical sample testing found that the cutoff value of this CLIA was 5537.15 (RLU), and there was no significant difference between the CLIA method and the registered ELISA kit. This study established a fully automated CLIA method with high sensitivity, specificity, accuracy, precision, recovery rate, and satisfactory clinical testing performance, which may provide a new choice for echinococcosis screening.

Model-less prediction filter for adaptive adjustment process noise.

In this study, a filtering scheme suitable for high-precision sensors was proposed to extract high-precision sensor information. According to the principle of Kalman gain based on data fusion, a model-less prediction filter with minimum gain measurement noise compensation and process noise posteriori constraint adjustment was developed. In comparison to various Kalman filter methods, the proposed algorithm demonstrated better accuracy in the steady state. The high precision performance and effectiveness of the model-less prediction filter were verified under a digitally controlled linear power supply.

Batch-Based Learning Consensus of Multiagent Systems With Faded Neighborhood Information.

This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It is therefore of great significance to investigate how to ensure the precise consensus tracking to a given reference leader using heavily contaminated information. To this end, a novel distributed learning consensus scheme is proposed, which consists of a classic distributed control structure, a preliminary correction mechanism, and a separated design of learning gain and regulation matrix. The influence of biased and unbiased randomness is discussed in detail according to the convergence rate and consensus performance. The iterationwise asymptotic consensus tracking is strictly established for linear MAS first to demonstrate the inherent principles for the effectiveness of the proposed scheme. Then, the results are extended to nonlinear systems with nonidentical initialization condition and diverse gain design. The obtained results show that the distributed learning consensus scheme can achieve high-precision tracking performance for an MAS under unreliable communications. The theoretical results are verified by two illustrative simulations.

Tool Wear Mechanism and Grinding Performance for Different Cooling-Lubrication Modes in Grinding of Nickel-Based Superalloys.

Tool wear introduced during grinding nickel-based superalloys was identified as a significant factor affecting the production quality of aero-engine industries concerning high service performance and high precision. Moreover, uncertainties derived from the various cooling-lubrication modes used in grinding operations complicated the assessment of grinding preformation. Therefore, this work investigated the tool wear mechanisms in grinding nickel-based superalloys that adopted five cooling-lubrication modes and investigated how the wear behaviors affected grinding performance. Results showed that chip-deposits covered some areas on the tool surface under dry grinding and accelerated the tool failure, which produced the highest values of tangential force, 7.46 N, and normal force, 14.1 N. Wedge-shape fractures induced by indentation fatigue were found to be the predominant wear mechanism when grinding nickel-based superalloys under flood cooling mode. The application of minimum quantity lubrication-palm oil (MQL-PO), MQL-multilayer graphene (MQL-MG), and MQL-Al2O3 nanoparticles (MQL-Al2O3) formed lubricity oil-film on the tool surface, which improved the capacity of lubrication in the tool-workpiece contact zone and provided 37%, 30%, and 52% higher coefficient of friction than dry mode, respectively. The results of this study demonstrate that lubricated oil-film produced by MQL modes reduces the possibility of fractures of cubic boron nitride (CBN) grits to some extent.

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objective of simultaneously minimizing the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multiobjective optimization model. Then, the improved multiobjective sparrow search algorithm (IMOSSA) is developed to find the optimal solution. Finally, we conduct computational experiments, including a comparison between IMOSSA and the nondominated sorting genetic algorithm II (NSGA-II), Jaya and the mixed integer linear programming (MILP) solver of CPLEX. The results demonstrate that IMOSSA has high precision, good convergence and excellent performance in solving the GFJSP in low-carbon manufacturing systems.

Modeling and dynamic analysis of a robotic arm with pneumatic artificial muscle by transfer matrix method

Pneumatic artificial muscle (PAM) has attracted significant attention owing to its safe and compliant physical properties in industrial manipulators, along with advanced service technologies. Since PAM is prone to large deformation problems, actuation control, and dynamic identification is required to perceive considering precise and fast computation. This article presents a methodology for accurate dynamic modeling and analysis of a planar robotic arm in the presence of large deformations caused by the artificial muscle. This novel planar robotic arm with pneumatic actuation and spring loaded in an antagonistic form is designed and fabricated. This article deals with the displacement analysis of a new type of PAM-actuated mechanism for large deformation. The aim is to find an accurate mathematical model of this robotic arm with motion nonlinearity. Since vibration can be generated in the articulating operations according to the dynamic behavior of PAM, a transfer-matrix method is proposed to explore the effect of flexibility undergoing planar flexural deformation. An explanation of the procedure through presenting its general formulation is employed to experiment. The discrete method is analyzed beside the rich datasets generated for actuator identification in the preliminary experimental study. The transfer matrices are determined considering the flexibilities whereas results are verified using MSC.ADAMS©. Experimental validation and numerical modeling confirm that the presented modeling methods help to improve the performance of the pneumatic muscle drives in high-precision manipulators. This study leaves room for further development of this algorithmic framework with a more complicated mechanism with soft pneumatic network actuators for various designs.

Robust high-precision tracking control for a class of nonlinear piezoelectric micropositioning systems with time-varying uncertainties

Piezoelectric micropositioning systems (PMSs) have been widely utilized in the high-precision manipulation applications, but are also subjected to undesired nonlinearities, like hysteresis, and parameter uncertainties. To solve this problem, this paper proposes a new robust sliding mode control scheme for a class of nonlinear PMSs with time-varying uncertainties. Different from the conventional sliding mode control (SMC), the proposed controller further combines the Fourier series-based function estimation technique, fuzzy logic system and adaptive learning algorithm to realize online estimation and compensation of system time-varying uncertainties without their boundary information. The adaptive laws of Fourier coefficients and fuzzy adjustable parameters are obtained via the Lyapunov stability theory. Compared with the existing SMC methods, the proposed control effectively eliminates the control chattering problem, and guarantees the convergence of the tracking error in finite time in the presence of time-varying uncertainties. Theoretical analysis and numerical simulation results show that the proposed control strategy can meet the high-speed, high-precision robust tracking performance requirements of PMSs for micro/nano-manipulation applications.

Performance Prediction of Electric Motors via Deep Learning

When designing electric motors, many types of performances (electrical and mechanical characteristics) must be predicted with good accuracy. In general, these performances are determined based on complex theoretical calculations, but theoretical calculations include various assumptions. Therefore, it is difficult to eliminate prediction errors when predicting performance, and it is necessary to improve accuracy by referring actual test data. Recently, with the digitalization of the manufacturing process, a large amount of actual data has been converted into a database, and it is expected to be put to effective use. Here, a neural network that predicts various performances of electric motors using a large amount of actual data as a training dataset, is constructed to achieve uniform and high-precision performance prediction via deep learning. Its practical use for actual design work is verified in this study.

Flexible iontronic sensors with high-precision and high-sensitivity detection for pressure and temperature

Flexible sensor with high precision and sensitivity in detecting pressure and temperature is highly desired. Herein, we design a flexible iontronic sensor by sandwiching a sandpaper-like ionogel layer between two silver nanowires-loaded polymeric nanofiber films. The obtained sensor can serve as a flexible pressure sensor with high precision (1.18 Pa), high sensitivity (106.01 kPa−1), wide measuring range (0–77 kPa), fast response/recovery time (16 ms/25 ms), and good stability over 2000 cycles because of the continuous change in the contract between electrode and dielectric layer under pressure and the formation of electric double layer between them. Owing to these excellent merits in pressure sense, the as-prepared sensor can real-time monitor tiny pressure caused by pulse, facial movements, and swallowing. More interestingly, this sensor can also serve as a temperature sensor, which has outstanding integrated performance of high precision of 0.02 °C, high sensitivity of 28.1% °C−1 (20–40 °C), wide operating window from −40 °C to 80 °C, superb repeatability and reliability, attributing to the monotonous relationship between the dielectric constant of TPU/IL ionogel and temperature. The designed sensor has bright prospects in flexible electronics and electronic skins for monitoring human health and human motion.

A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping

The development of high-throughput field phenotyping, which uses modern detection technologies and advanced data processing algorithms, could increase productivity and make in-field phenotypic evaluation more efficient by collecting large amounts of data with no or minimal human assistance. Moreover, high-throughput plant phenotyping systems are also very effective in selecting crops and characterizing germplasm for drought tolerance and disease resistance by using spectral sensor data in combination with machine learning. In this study, an affordable high-throughput phenotyping platform (phenomobile) aims to obtain solutions at reasonable prices for all the components that make up it and the many data collected. The goal of the practical innovation in field phenotyping is to implement high-performance precision phenotyping under real-world conditions at accessible costs, making real-time data analysis techniques more user-friendly. This work aims to test the ability of a phenotyping prototype system constituted by an electric phenomobile integrated with a MAIA multispectral camera for real in-field plant characterization. This was done by acquiring spectral signatures of F1 hybrid Elisir (Olter Sementi) tomato plants and calculating their vegetation indexes. This work allowed to collect, in real time, a great number of field data about, for example, the morphological traits of crops, plant physiological activities, plant diseases, fruit maturity, and plant water stress.

Design and Implementation of Finite-Time Control for Speed Tracking of Permanent Magnet Synchronous Motors

This letter investigates real-time implementation of a finite-time control for permanent magnet synchronous motors in the presence of external load disturbance. Firstly, an integral terminal sliding manifold is designed to achieve fast speed, high precision performance, and to enhance the quality of currents by reducing the total harmonic distortion. Indeed, the proposed surface manifold ensures a finite-time convergence of the speed state variable. Secondly, a switching control scheme is added for the system control to force the state systems to converge to their desired values in the presence of load disturbance. Finite-time stability is proved based on Lyapunov theory. Finally, the effectiveness of the designed controller is validated by carrying out real-time experimental studies using eZdspTM F28335 board. According to the experimental results, the proposed controller is easy to implement, improves tracking accuracy, reduces the chattering issues and ensures robustness against external load disturbance.

Multivariable Disturbance Observer-Based Finite-Time Sliding Mode Attitude Control for Fixed-Wing UAVs Under Matched and Mismatched Disturbances

In this letter, we propose a multivariable disturbance observer-based finite-time sliding mode attitude control (MDOB-FT-SM-AC) for fixed-wing UAVs in the presence of both matched and mismatched disturbances. Compared with existing sliding mode attitude controllers, the significant improvements of the proposed MDOB-FT-SM-AC are the multivariable control structure, strong robustness, and high precision performance with continuous control input signal. In the proposed MDOB-FT-SM-AC, we first develop multivariable finite-time disturbance observers such that the precise estimation of both matched and mismatched disturbances is ensured. Next, a nonsingular terminal sliding manifold is designed such that the fixed-wing UAV is driven to track its desired attitude command in finite time. We finally present a multivariable super-twisting reaching law such that the finite-time convergence of the sliding variable and its derivative to zero is guaranteed. Attentive finite-time convergence analysis is derived based on the Lyapunov and homogeneity theories. Simulation results are given to illustrate the superiority of the proposed MDOB-FT-SM-AC.

AD-YOLOv5s based UAV detection for low altitude security

UAV (Unmanned Aerial Vehicle) black flight at low altitude could cause serious safety risks. Consequently, it is crucial to detect and manage low altitude small UAVs. The existing methods of low altitude small UAV detection suffer from problems such as high false alarm rate, and poor real-time performance. In order to solve the above problems, we present a novel approach, named AD-YOLOv5s, to achieve low altitude small UAV detection with high precision and high real-time performance. Firstly, the feature enhancement method is used to expand the dataset. We optimize the model feature fusion, the prediction head structure, and the loss function. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, feature enhancement is performed to improve the detection accuracy. Secondly, the ghost module and depthwise separable convolution are used to reduce the number of parameters of the model, and we propose the method of lightweight design of model to improve the detection speed. Compared with the YOLOv5s model, the experiment result shows that our proposed AD-YOLOv5s model improves the value of mAP by 2.2% and the value of Recall by 1.8%, reduces the value of GFLOPs by 29.9% and parameters by 38.8%, and achieves 27.6 FPS when the proposed model deploy on a low-cost edge computing device (jetson nano).

Monitoring of butt weld penetration based on infrared sensing and improved histograms of oriented gradients

In the process of butt welding, achieving real-time monitoring of butt weld penetration is of great significance for improving forming quality. Currently, weld penetration detection methods based on weld pool vision are easily disturbed by arc lights. In this study, an innovative approach for butt weld penetration detection using infrared sensing and improved histograms of oriented gradients (HOG) is proposed. First, an improved HOG algorithm was studied to extract the temperature field distribution features of the welding heat-affected zone (HAZ) around the weld pool. Second, the feasibility of using the improved HOG feature to detect weld penetration was analysed, and a weld penetration detection model was established. Finally, the generalisation performance of the proposed weld penetration detection algorithm was confirmed, and the influence of the location and size of the detection area on the accuracy of the detection algorithm was analysed. The testing results indicate that the proposed detection algorithm exhibits high precision and excellent generalisation performance, and enhances the means of monitoring of butt weld penetration.

An artificial intelligence model based on multi-step feature engineering and deep attention network for optical network performance monitoring

Optical network performance monitoring technology, which can effectively identify physical impairment in the network, is of great significance to ensure network stability and prevent accidents. In order to establish a high-precision performance monitoring framework for optical networks, a three-step feature engineering and deep attention mechanism approach is proposed in the paper. The main modeling process consists of the following three steps: In Step I, the AAH (Asynchronous Amplitude Histogram) method is used to initially extract features from the original data. In Step II, the KPCA (Kernel Principal Component Analysis) method and the Q-learning method are utilized to reduce the feature dimension and transmit the optimized feature to the downstream predictor. In step III, the downstream predictor based on the LSTM (long short-term memory network) and attention mechanism can effectively construct the mapping between features and labels and realize data prediction. After several groups of comparative experiments, the following conclusions can be obtained: (a) Ablation experiments and component comparisons demonstrate that the proposed framework is able to effectively combine feature engineering and predictors, which can get excellent results. (b) The optical network performance detection framework proposed in the paper achieves better results than three SOTA (state-of-the-art) models and fifteen alternative frameworks.

Automatic Current-Constrained Double Loop ADRC for Electro-Hydrostatic Actuator Based on Singular Perturbation Theory

For an electro-hydrostatic actuator (EHA), the position, speed, and current cascade three-loop control architecture are dominant in existing active disturbance rejection control (ADRC). However, this architecture suffers from many problems, such as severe noise sensitivity of the extended state observer (ESO), excessive complexity of control structure, and too many tuned parameters, which makes the controller not easy to implement in practice. Aiming at the above drawbacks, a novel cascade double-loop ADRC strategy that is automatic current-constrained is proposed, which makes the whole ADRC architecture simplified to the position and the integrated speed–current double-loop architecture. Firstly, for the position control loop, the singular perturbation theory is used to reasonably reduce the order of the model for the position subsystem of EHA. A reduced order ADRC controller (ROADRC) is synthesized, which not only effectively reduces the noise sensitivity of ESO, but also circumvents use of the actuation acceleration information in the controller design process. Secondly, the integrated speed–current ADRC controller is designed by taking the speed and current subsystems of EHA into synthesis, which avoids the problem of excessive current loop bandwidth in conventional three-loop control architecture, and the number of tuned parameters is significantly reduced. Additionally, an uncomplicated and effective automatic current-constrained ADRC controller (CACADRC) is designed to solve the problem in the integrated speed–current ADRC that the current cannot be automatically constrained. Finally, by comparing the three-loop PI controller with the traditional three-loop ADRC, a detailed simulation analysis is carried out to verify the effectiveness and merits of the proposed controller. The simulation results show that the proposed controller not only inherits the advantages of high precision and strong disturbance rejection performance of the conventional ADRC, but can also efficiently decrease the noise sensitivity of ESO and effectively achieve the current-constrained control.

Demonstrate chiral spin currents with nontrivial interactions in superconducting quantum circuit

Quantum many-body systems in which time-reversal symmetry is broken give rise to a wealth of exotic phases, and thus constitute one of the frontiers of modern condensed matter physics. Quantum simulation allows us to better understand many-body systems with huge Hilbert space, where classical simulation is usually inefficient. With superconducting quantum circuit as a platform for quantum simulation, we realize synthetic Abelian gauge fields by using microwave drive and tunable coupling in loop configurations to break the time-reversal symmetry of the system. Based on high-precision manipulation and readout of circuit-QED architecture, we demonstrate the chiral ground spin current of a time-reversal symmetry broken system with nontrivial interactions. Our work is a significant attempt to simulate quantum many-body systems with time-reversal symmetry breaking in multi-qubit superconducting processors.

Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator.

Piezoelectric actuated models are promising high-performance precision positioning devices used for broad applications in the field of precision machines and nano/micro manufacturing. Piezoelectric actuators involve a nonlinear complex hysteresis that may cause degradation in performance. These hysteresis effects of piezoelectric actuators are mathematically represented as a second-order system using the Dahl hysteresis model. In this paper, artificial intelligence-based neurocomputing feedforward and backpropagation networks of the Levenberg-Marquardt method (LMM-NNs) and Bayesian Regularization method (BRM-NNs) are exploited to examine the numerical behavior of the Dahl hysteresis model representing a piezoelectric actuator, and the Adams numerical scheme is used to create datasets for various cases. The generated datasets were used as input target values to the neural network to obtain approximated solutions and optimize the values by using backpropagation neural networks of LMM-NNs and BRM-NNs. The performance analysis of LMM-NNs and BRM-NNs of the Dahl hysteresis model of the piezoelectric actuator is validated through convergence curves and accuracy measures via mean squared error and regression analysis.

A Novel Two-Degree-of-Freedom Gimbal for Dynamic Laser Weeding: Design, Analysis, and Experimentation

Current robotic laser weeding systems mostly rely on serial mechanisms with one or two actuated axes, which can barely meet the requirements of high precision and dynamic performances. In this article, a novel laser weeding gimbal is proposed based on a two-degree-of-freedom 5-revolute rotational parallel manipulator to perform a dynamic intrarow laser weeding operation. Comprehensive analyses consisting of kinematics, workspace, singularity, and dynamics are carried out to evaluate the performance of the proposed design. Finally, experimental tests were conducted both in lab and field environments and the results are provided, in which the positioning accuracy has been evaluated as in average errors of 0.62 mm in position at the distance of 535 mm, and the dynamic weeding efficiency is around 0.72 s/weed with a dwell time of 0.64 s at the tracking speed of 0.1 m/s. The effectiveness of the proposed dynamic intrarow weeding mechanism has been evaluated in a real field trial.