Soft Saturation Research Articles

Comparison of Implant Precision with Robots, Navigation, or Static Guides

Comparison of Implant Precision with Robots, Navigation, or Static Guides

Reactor power control system design and dynamic simulation for load maneuvers over full range and entire lifetime of an integrated supercritical CO2 reactor plant

Due to advantage of high efficiency, low carbon, and flexible operation, the integrated supercritical CO2 reactor plant has promising prospect in energy supply. This paper aims to clarify performance of the reactor plant and establish a reactor power control strategy through numerical modeling. Firstly, performance on separated components is evaluated to provide guidance for control strategy development. Secondly, influence of control methods, controller schemes, and saturation models on control strategy are discussed. Thirdly, dynamic response of reactor plant with proposed control strategy is confirmed via load change transients. Results show that passive control method is infeasible at EOL and load demand below 50 %FP, and active control method must be considered. The proposed control strategy adopts coupled temperature-channel and power-channel scheme and soft saturation model. It ensures safe operation of the plant over full load range in whole lifetime. These results can provide helpful guidance on part-load operation for this system.

Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm

Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.

Microstructure evolution, stress state and electromagnetic performance of Fe-based nanocrystalline alloy induced by stress-annealing

Stress-induced anisotropy has been applied for FeSiBCuNb nanocrystalline alloy ribbons in transformers with large direct current (DC) component. However, the physical mechanism of anisotropy induced by stress annealing during crystallization remains controversial. Here, a systematic study was conducted on the microstructure, residual stress state, dynamic domain structure and properties of the stress-annealed FeSiBCuNb nanocrystalline ribbons. Present results demonstrate that, the increase of the applied stress greatly increases the residual tensile stress and induces lattice distortion, which acts as pinning site during magnetization, resulting in enhanced domain-wall energy and narrower wall width. Meanwhile, stress annealing promotes the flattening of hysteresis loops and decreases the effective permeability, which improves the DC-bias characteristics of the ribbons, thereby promoting the transition from “hard saturation” to “soft saturation” behavior. Also, stress-induced grain refinement is beneficial to obtaining high hardness. Owing to the stress-induced anisotropy, the stress-annealed sample with applied stress of 113 MPa exhibits excellent combined properties, including low constant effective permeability of 438 at wide frequency range (1 kHz–8 MHz), high DC-bias capability of 74% (30 Oe), low power loss of 49 kW/m3 (100 mT, 100 kHz), and high hardness of 644 HV. This discovery provides an insightful understanding of stress-induced anisotropy mechanism on the high-performance Fe-based nanocrystalline alloys.

Human–Robot Cooperation Control Strategy Design Based on Trajectory Deformation Algorithm and Dynamic Movement Primitives for Lower Limb Rehabilitation Robots

Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI.

Atomic Layer Deposition of ScF3 and ScxAlyFz Thin Films.

In this paper, we present an ALD process for ScF3 using Sc(thd)3 and NH4F as precursors. This is the first material made by ALD that has a negative thermal expansion over a wide-temperature range. Crystalline films were obtained at the deposition temperatures of 250-375 °C, with a growth per cycle (GPC) increasing along the deposition temperature from 0.16 to 0.23 Å. Saturation of the GPC with respect to precursor pulses and purges was studied at 300 °C. Saturation was achieved with Sc(thd)3, whereas soft saturation was achieved with NH4F. The thickness of the films grows linearly with the number of applied ALD cycles. The F/Sc ratio is 2.9:3.1 as measured by ToF-ERDA. The main impurity is hydrogen with a maximum content of 3.0 at %. Also carbon and oxygen impurities were found in the films with maximum contents of 0.5 and 1.6 at %. The ScF3 process was also combined with an ALD AlF3 process to deposit ScxAlyFz films. In the AlF3 process, AlCl3 and NH4F were used as precursors. It was possible to modify the thermal expansion properties of ScF3 by Al3+ addition. The ScF3 films shrink upon annealing, whereas the ScxAlyFz films show thermal expansion, as measured with HTXRD. The thermal expansion becomes more pronounced as the Al content in the film is increased.

Research on Printmaking Image Classification and Creation Based on Convolutional Neural Network

As an important form of expression in modern civilization art, printmaking has a rich variety of types and a prominent sense of artistic hierarchy. Therefore, printmaking is highly favored around the world due to its unique artistic characteristics. Classifying print types through image feature elements will improve people’s understanding of print creation. Convolutional neural networks (CNNs) have good application effects in the field of image classification, so CNN is used for printmaking analysis. Considering that the classification effect of the traditional convolutional neural image classification model is easily affected by the activation function, the T-ReLU activation function is introduced. By utilizing adjustable parameters to enhance the soft saturation characteristics of the model and avoid gradient vanishing, a T-ReLU convolutional model is constructed. A better convolutional image classification model is proposed based on the T-ReLU convolutional model, taking into account the issue of subpar multi-level feature fusion in deep convolutional image classification models. Utilize normalization to analyze visual input, an eleven-layer convolutional network with residual units in the convolutional layer, and cascading thinking to fuse convolutional network defects. The performance test results showed that in the data test of different styles of artificial prints, the GT-ReLU model can obtain the best image classification accuracy, and the image classification accuracy rate is 0.978. The GT-ReLU model maintains a classification accuracy above 94.4% in the multi-dataset test classification performance test, which is higher than that of other image classification models. For the use of visual processing technology in the field of classifying prints, the research content provides good reference value.

Three-Phase Four-Wire Inverter for Grid Emulator Under Wide Inductance Variation to Evaluate the Performance of Distributed Generator

This paper presents a three-phase four-wire inverter to generate voltage sources under wide filter inductance variation. The voltage sources with distortion and unbalance are to emulate grid voltages. The distortion includes voltage sags, voltage swells, and harmonic components, and the unbalance includes voltage and phase-angle differences. With a conventional PI control and direct digital control (DDC) only, the voltage distortion cannot fulfill precisely because of the improper controls. While with the proposed modified direct digital control (MDDC) and harmonic-angle adjustment algorithm, the soft core saturation can be taken into account and the harmonic voltage distortion can be accurately emulated, respectively. Moreover, the proposed control scheme can achieve fast transient response. The control law is derived and the inverter with the control to generate grid voltage is described in detail. Simulated and experimental results from 10 kW prototype have verified the analyses and discussions

Research on neural network algorithm in artificial intelligence recognition

With the rapid development of computer application technology, computer artificial intelligence recognition technology has also made rapid progress. Artificial intelligence was first represented by LISP language, machine theorem proof, etc., and then the intelligent medical treatment and neural network appeared, and now it realizes the popularization of AI. The purpose of this article is to explore the application of neural network algorithms in artificial intelligence recognition. Based on the classic convolutional neural network with LeNet structure, this paper constructs a reasonable convolutional neural network model by deepening and expanding, and uses it in face recognition to conduct face recognition research. LeNet is the origin of CNN, and LeNet is mainly used to identify and classify handwritten characters. This paper uses three networks on the YaleB face database for face recognition, and compares the pooling method of the maximum pooling and average pooling, the misclassification rate of the ReLU incentive function and the tanh incentive function on the three networks.YaleB is a very complete face database, divided into five subsets, ten people, and 64 images per person, including changes in illumination, expressions, and gestures. The tanh function has what is an odd function and has a soft saturation property, while the relu function has a hard saturation property. Analyze whether it can solve the problems caused by illumination factors and occlusion factors in traditional face recognition. Finally, the recognition effect is compared with the traditional face recognition method, and it is concluded that the effect of the traditional face recognition method is not as high as the misclassification rate of 0.0356 based on the convolutional neural network. The recognition method works well. Maximum pooling is also known as maximum reduced pixel sampling, and is more based on the input element map than average pooling.Through the optimization of the algorithm, the face recognition rate is improved, which helps the application and development of artificial intelligence.

Controlling magnetic configuration in soft–hard bilayers probed by polarized neutron reflectometry

Hard/soft magnetic bilayer thin films have been widely used in data storage technologies and permanent magnet applications. The magnetic configuration and response to temperatures and magnetic fields in these heterostructures are considered to be highly dependent on the interfacial coupling. However, the intrinsic properties of each of the layers, such as the saturation magnetization and layer thickness, also strongly influence the magnetic configuration. Changing these parameters provides an effective method to tailor magnetic properties in composite magnets. Here, we use polarized neutron reflectometry (PNR) to experimentally probe the interfacial magnetic configurations in the hard/soft bilayer thin films: L10-FePt/A1-FePt, [Co/Pd]/CoPd, [Co/Pt]/FeNi, and L10-FePt/Fe, all of which have a perpendicular magnetic anisotropy in the hard layer. These films were designed with different soft and hard layer thicknesses (tsoft and thard) and saturation magnetization (Mssoft and Mshard). The influences of an in-plane magnetic field (Hip) and temperature (T) are also studied using a L10-FePt/A1-FePt bilayer sample. Comparing the PNR results to the micromagnetic simulations reveals that the interfacial magnetic configuration is highly dependent on tsoft, Mssoft, and the external factors (Hip and T) and has a relatively weak dependence on thard and Mshard. Key among these results, for thin tsoft, the hard and soft layers are rigidly coupled in the out-of-plane direction and then undergo a transition to relax in-plane. This transition can be delayed to larger tsoft by decreasing Mssoft. Understanding the influence of these parameters on the magnetic configuration is critical to designing functional composite magnets for applications.

Design of nonlinear segmentation activation functions for object detection

The existing activation functions ReLU, Tanh, and Mish have problems such as "neuronal death", offset, and poor robustness. Aiming at these problems, the XExp activation function is proposed by combining the advantages of ReLU, Swish, and Mish functions, and the problem of negative half-axis neuronal death is optimized by using the nonlinearity of non-RELU family functions and the non-zero characteristics of negative half-axis functions, and the soft saturation of negative semi-axis is retained. By designing the position of the origin of the function, the problem of positive half-axis offset in the Swish and Mish functions are solved. In terms of convergence speed, the MNIST dataset achieved 93.87% training accuracy during the first batch training on the newly proposed activation function XExp function, which was more than 85% higher in convergence speed compared with the Relu function; In terms of model convergence stability, compared with the accuracy of the Relu function, the XExp function can still achieve 98.05% accuracy when the number of convolutional layers is increased to 25 layers. The two data sets of CIFAR-10 and CIFAR-100 verify their versatility and practicality in the field of object detection.

Robust nonlinear attitude tracking control of an underactuated spacecraft under saturation and time-varying uncertainties

The three-axis attitude tracking control of an underactuated spacecraft in the large-angle maneuver was investigated in this study. As a major contribution of this paper, a robust controller was developed to achieve the perfect attitude tracking of the underactuated spacecraft with consideration of saturation and uncertainties. Interestingly, due to non-singularity of this design, it could relax the burden of limiting the initial condition of the quaternions. The stability analysis of the developed controller could be guaranteed by Lyapunov method as shown here. Overall, the simulation results indicate that the proposed controller has robustness against saturation, external perturbations, inertia uncertainties, and internal disturbances of actuators. As result, the controller was asymptotically stable under the combination of the soft saturation and the perturbations so that attitude parameters converged to the desired path within the 230 s. In this case, the saturation level was consumed 0.08 N.m. Also, it was still asymptotic stable under the hard saturation whose level is equal to 0.01 Nm, 12.5% of the soft saturation level.

Robust backstepping attitude tracking control of an underactuated spacecraft with saturation and time-variant perturbations

This study investigated associations of attitude tracking control of an underactuated spacecraft with consideration of saturation and perturbations. A nonsingular attitude tracking control was proposed which did not need limiting initial conditions of the quaternions. The controller was analyzed based on Lyapunov criteria and LaSalle’s invariance theorem in the large-angle maneuver. In order to control, the complete kinematic and dynamic model of the underactuated spacecraft was reconstructed. According to simulation results, our controller has excellent robustness against the hard saturation, external disturbances, time-varying inertia uncertainties, and internal disturbances of actuators. As result, we found that the attitude controller was asymptotically stable under the soft saturation and the perturbations so that quaternions and angular velocity converged to the desired path within the 80 s. Also, it was still asymptotic stable under the hard saturation whose level is equal to 0.035 Nm, 3.5% of the soft saturation level. In this case, errors of quaternions and angular velocity were converged to the origin within the 150 s. Finally, the closed-loop system was verified by Adams-MATLAB co-simulation. The maximum verification errors for quaternions were less than 19%, while the maximum verification errors for angular velocity were less than 13.5%.

New drug delivery system in ophthalmology: results studying the surface structure of soft contact lenses from various polymer

The development of an ophthalmic therapeutic system includes research on the spatial structure of soft contact lens polymers and the study of the processes of saturation and release of medicinal substances from them. This allows you to determine the methods of saturation of contact lenses with medicinal agents and will open up new opportunities in the treatment of ophthalmological diseases. The purpose of this preliminary fragment of large-scale research was to study the surface structure of soft contact lenses made of various polymers. The following polymers were used in the work: Nelfilcon A, Hilafilcon B, Nezofilcon A, Etafilcon A, Lotrafilcon B. The following pharmaceutical substances were used: Brimonidine Tartrate, Betaxolol Hydrochloride, Pyridoxine Hydrochloride. The surface structure of soft contact lenses was studied using atomic force microscopy. Each material under study has a different surface character, which together with the differences in pore properties determines its individuality. Based on this, it should be assumed that the surface of soft contact lenses affects the possibility of their potential use as a means of delivering drug agent molecules to the eye tissues. In all cases of soft contact saturation, the highest absorption capacity was demonstrated by Hilafilcon B and Etafilcon A with a similar surface.

The influence of soft clay saturation characteristics on unconfined compressive strength in Guangdong-Hong Kong-Macao Greater Bay Area

In order to study the influence of saturated characteristics of soft clay on unconfined compressive strength, the soft clay of Guangdong-Hong Kong-Macao Bay Area is taken as the research object, comparing and contrasting on unconfined compression test of saturated clay and unsaturated clay in laboratory, studying the variation law of unconfined compressive strength and sensitivity of unsaturated and saturated soft clay. The test results show that: 1. During the failure of unsaturated soft clay samples, oblique fractures appear, showing brittle shear failure, while the saturated clay samples appear constant bulging in the middle, and finally a “cross” is broken in the central bulging part, showing plastic shear failure.2. The unconfined compressive strength of unsaturated clay is about 10kPa higher than that of saturated soft clay, and its corresponding sensitivity is also about 0.4 higher. It can be shown that the soil saturation has a certain influence on the soil strength. The difference between saturated soil and unsaturated soil is the existence of gas phase. To be more precise, the existence of gas phase in unsaturated soil, i.e. the existence of suction, makes the soil stronger and presents the brittle shear failure form, while the saturated soil basically presents the plastic shear failure form.

Admittance-Based Controller Design for Physical Human–Robot Interaction in the Constrained Task Space

In this article, an admittance-based controller for physical human–robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners —This work is motivated by the neglect of safety in existing controller design in physical human–robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human–robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: a differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: a learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector.

Profiling and analysis of control rod speed design on core power control for TRIGA reactor

In nuclear reactor TRIGA PUSPATI (RTP) operation, conventional Control Rod Speed Design (cCRSD) uses hard saturation to control rod insertion step as a function of the reactivity insertion rate in the reactor core to ensure the power output will not exceed the limit for the operational safety. The cCRSD which is part of the core power control system produces relatively long settling time during transient and a chattering noise signal during steady-state. Therefore, the current study proposed a new CRSD based on different saturation types such as soft and sigmoid function saturation and changes in the maximum control rod speed limiter value. In this paper, the new CRSD (nCRSD) is designed, profiled, and analyzed based on simulation of an actual Control Rod Drive Mechanism (CRDM) model, common types of saturation models, and reactivity rod worth curve experimental data to predict the best reactivity insertion rate response for operations. The model for core power control is based on mathematical models of the reactor core, the FCA controller, and the CRSD model. The mathematical models of the reactor core are based on a point kinetics model, thermal-hydraulic models, and reactivity models. The nCRSD model is integrated with the Proportional-Integral (PI) type controller and the model of the reactor core. The performance of the nCRSD and cCRSD is compared via computer simulation and experiments. Overall, the results show that the response from the nCRSD offers better results than the cCRSD, which reduces the rise time by up to 52%, the settling time by up to 26%, and the chattering error by up to 25% of the nominal value.

Component-Level Optimization of Hybrid Excitation Synchronous Machines for a Specified Hybridization Ratio Using NSGA-II

In this article, a Hybrid Excitation Synchronous Machine (HESM) is optimally designed for a specified Hybridization Ratio (HR). A new formulation of the design problem is proposed to be tackled by the Non-dominated Sorting Genetic Algorithm II (NSGA-II), while minimizing the material cost. This formulation includes a more comprehensive explanation of the key concept, HR, which considers the soft and hard saturation effects in the HESM design. The HESM model is based on a 3D nonlinear Magnetic Equivalent Circuit (MEC). For faster convergence, the number of design variables is reduced using two statistical analyses, namely Analysis of Level and Analysis of Variance (ANOVA). A HESM is optimally designed for HR = 0.5 and validated by a commercial Finite Element Analysis (FEA) software.

Analysis and Calculation of Current Ripple Considering Inductance Saturation and Its Application to Variable Switching Frequency PWM

The inductance is an important parameter impacting current ripple calculation, usually with the assumption that the designed inductance performs ideally in most of voltage source inverters (VSIs) applications. However, the initial permeability of magnetic material usually descends when the winding current reaches or goes beyond the saturation limit. In such region of saturation, the implemented inductor does not perform as expected in design stages and its value varies with the bias current. In this paper, analysis and calculation of the current ripple are presented for a three-phase VSI with powder cores-based inductors, which reveals the calculation error of current ripple caused by the soft saturation nature of powder cores. Based on the equivalent single-phase model, the influence of inductance saturation on current ripple can be quantitatively evaluated. Then, a comparison of the current ripple calculation between considering and ignoring inductance saturation of implemented inductors is drawn in detail. Furthermore, under the condition of inductance saturation, the output current ripple peak-to-peak value of three phases is used as the constraint for variable switching frequency pulsewidth modulation (VSFPWM) design. A comprehensive method is developed to compensate the effect of inductance saturation in VSFPWM. At last, the effectiveness of the proposed strategy is verified by the detailed simulation and experiment.

Green Synthesis of Nickel–Copper Mixed Ferrite Nanoparticles: Structural, Optical, Magnetic, Electrochemical and Antibacterial Studies

The nickel–copper mixed ferrite nanoparticles [Cu1−xNixFe2O4 (x = 0, 0.5, 1)] NPs were prepared a by combustion method using Aloe barbadensis extract as a green reducing agent. The structural, functional, morphological, optical, magnetic, electrochemical properties of the sample were investigated using x-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy, energy-dispersive x-ray spectroscopy, ultraviolet–visible (UV–Vis) spectrophotometry, photoluminescence, vibrating sample and cyclic voltammetry. The XRD patterns showed that all compositions with a cubic spinel structure and a crystallite size of 52 nm were reduced to 29 nm after nickel addition in copper ferrite and 35.85 nm for nickel ferrite. The UV–Vis absorption spectrum shows that the nickel-substituted copper ferrite band energy (Eg) increases as the crystallite size decreases. Measurements of magnetization obtained at room temperature revealed a soft ferromagnetic behaviour and saturation magnetization, coercivity value increased with the substitution of nickel. The maximum specific capacitance of 114 F g−1 was obtained at the scan rate of 5 mV s−1 for copper–nickel mixed ferrite NPs. A study of antibacterial activity against Escherichia coli, Klebsilla pneumonia, Staphylococcus aureus, and Bacillus subtilis using a well-diffusion method was performed. Nickel substitution on the copper spinel ferrite NPs revealed a major influence on structural, optical, magnetic, electrochemical and magnetic properties of the product obtained.