Experimental Evaluation on Haptic Feedback Accuracy by Using Two Self-Made Haptic Devices and One Additional Interface in Robotic Teleoperation

The goal of haptic feedback in robotic teleoperation is to enable users to accurately feel the interaction force measured at the slave side and precisely understand what is happening in the slave environment. The accuracy of the feedback force describing the error between the actual feedback force felt by a user at the master side and the measured interaction force at the slave side is the key performance indicator for haptic display in robotic teleoperation. In this paper, we evaluate the haptic feedback accuracy in robotic teleoperation via experimental method. A special interface iHandle and two haptic devices, iGrasp-T and iGrasp-R, designed for robotic teleoperation are developed for experimental evaluation. The device iHandle integrates a high-performance force sensor and a micro attitude and heading reference system which can be used to identify human upper limb motor abilities, such as posture maintenance and force application. When a user is asked to grasp the iHandle and maintain a fixed position and posture, the fluctuation value of hand posture is measured to be between 2 and 8 degrees. Based on the experimental results, human hand tremble as input noise sensed by the haptic device is found to be a major reason that results in the noise of output force from haptic device if the spring-damping model is used to render feedback force. Therefore, haptic rendering algorithms should be independent of hand motion information to avoid input noise from human hand to the haptic control loop in teleoperation. Moreover, the iHandle can be fixed at the end effector of haptic devices; iGrasp-T or iGrasp-R, to measure the output force/torque from iGrasp-T or iGrasp-Rand to the user. Experimental results show that the accuracy of the output force from haptic device iGrasp-T is approximately 0.92 N, and using the force sensor in the iHandle can compensate for the output force inaccuracy of device iGrasp-T to 0.1 N. Using a force sensor as the feedback link to form a closed-loop feedback force control system is an effective way to improve the accuracy of feedback force and guarantee high-fidelity of feedback forces at the master side in robotic teleoperation.

A Collision Relationship-Based Driving Behavior Decision-Making Method for an Intelligent Land Vehicle at a Disorderly Intersection via DRQN

An intelligent land vehicle utilizes onboard sensors to acquire observed states at a disorderly intersection. However, partial observation of the environment occurs due to sensor noise. This causes decision failure easily. A collision relationship-based driving behavior decision-making method via deep recurrent Q network (CR-DRQN) is proposed for intelligent land vehicles. First, the collision relationship between the intelligent land vehicle and surrounding vehicles is designed as the input. The collision relationship is extracted from the observed states with the sensor noise. This avoids a CR-DRQN dimension explosion and speeds up the network training. Then, DRQN is utilized to attenuate the impact of the input noise and achieve driving behavior decision-making. Finally, some comparative experiments are conducted to verify the effectiveness of the proposed method. CR-DRQN maintains a high decision success rate at a disorderly intersection with partially observable states. In addition, the proposed method is outstanding in the aspects of safety, the ability of collision risk prediction, and comfort.

An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing

In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).

Text Data Mining Algorithm Combining CNN and DBM Models

The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To solve this drawback, a text data mining algorithm based on convolutional neural network (CNN) model and deep Boltzmann machines (DBM) model is proposed in this paper. This method combines the CNN and DBM models with good feature extraction to realize the double feature extraction. It can realize the tag tree by constructing the tag tree and design the effective hierarchical network to achieve classification. At the same time, the model can suppress the input noise on the classification. Experimental results show that the improved algorithm achieves good classification results in special text data mining.

High Dynamic Range Photocurrent Sensory Circuit with a Multi-Transistor Background Light Cancellation Loop for Photoplethysmography Sensing

In this article, we present a new photocurrent sensory circuit with a three-transistor background light cancellation. We describe our innovative photocurrent sensor-based blood pressure measuring device using a resistor-based current-to-voltage converter with a background light cancellation (BLC) loop. The photocurrent sensor is implemented using 0.35 μm standard CMOS technology and has zero average power consumption. The post-layout simulation for the photocurrent sensor shows a 1.3 MΩ transimpedance gain, a referred input noise current of 11 pA, and can reject a DC photocurrent up to 200 μA. This high DC rejection has been achieved due to the newly proposed multi-transistor BLC loop integrated with the sensor.

Beyond the standard quantum limit for parametric amplification of broadband signals

The low-noise amplification of weak microwave signals is crucial for countless protocols in quantum information processing. Quantum mechanics sets an ultimate lower limit of half a photon to the added input noise for phase-preserving amplification of narrowband signals, also known as the standard quantum limit (SQL). This limit, which is equivalent to a maximum quantum efficiency of 0.5, can be overcome by employing nondegenerate parametric amplification of broadband signals. We show that, in principle, a maximum quantum efficiency of unity can be reached. Experimentally, we find a quantum efficiency of 0.69 ± 0.02, well beyond the SQL, by employing a flux-driven Josephson parametric amplifier and broadband thermal signals. We expect that our results allow for fundamental improvements in the detection of ultraweak microwave signals.

Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach

Noise in spiking neurons is commonly modeled by a noisy input current or by generating output spikes stochastically with a voltage-dependent hazard rate (“escape noise”). While input noise lends itself to modeling biophysical noise processes, the phenomenological escape noise is mathematically more tractable. Using the level-crossing theory for differentiable Gaussian processes, we derive an approximate mapping between colored input noise and escape noise in leaky integrate-and-fire neurons. This mapping requires the first-passage-time (FPT) density of an overdamped Brownian particle driven by colored noise with respect to an arbitrarily moving boundary. Starting from the Wiener–Rice series for the FPT density, we apply the second-order decoupling approximation of Stratonovich to the case of moving boundaries and derive a simplified hazard-rate representation that is local in time and numerically efficient. This simplification requires the calculation of the non-stationary auto-correlation function of the level-crossing process: For exponentially correlated input noise (Ornstein–Uhlenbeck process), we obtain an exact formula for the zero-lag auto-correlation as a function of noise parameters, mean membrane potential and its speed, as well as an exponential approximation of the full auto-correlation function. The theory well predicts the FPT and interspike interval densities as well as the population activities obtained from simulations with colored input noise and time-dependent stimulus or boundary. The agreement with simulations is strongly enhanced across the sub- and suprathreshold firing regime compared to a first-order decoupling approximation that neglects correlations between level crossings. The second-order approximation also improves upon a previously proposed theory in the subthreshold regime. Depending on a simplicity-accuracy trade-off, all considered approximations represent useful mappings from colored input noise to escape noise, enabling progress in the theory of neuronal population dynamics.

Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Random noise is unavoidable in seismic data acquisition due to anthropogenic impacts or environmental influences. Therefore, random noise suppression is a fundamental procedure in seismic signal processing. Herein, a deep denoising convolutional autoencoder network based on self-supervised learning was developed herein to attenuate seismic random noise. Unlike conventional methods, our approach did not use synthetic clean data or denoising results as a training label to build the training and test sets. We directly used patches of raw noise data to establish the training set. Subsequently, we designed a robust deep convolutional neural network (CNN), which only depended on the input noise dataset to learn hidden features. The mean square error was then evaluated to establish the cost function. Additionally, tied weights were used to reduce the risk of over-fitting and improve the training speed to tune the network parameters. Finally, we denoised the target work area signals using the trained CNN network. The final denoising result was obtained after patch recombination and inverse operation. Results based on synthetic and real data indicated that the proposed method performs better than other novel denoising methods without loss of signal quality loss.

Control-Oriented Modeling for Nonlinear MIMO Turbofan Engine Based on Equilibrium Manifold Expansion Model

This paper investigates the control-oriented modeling for turbofan engines. The nonlinear equilibrium manifold expansion (EME) model of the multiple input multiple output (MIMO) turbofan engine is established, which can simulate the variation of high-pressure rotor speed, low-pressure rotor speed and pressure ratio of compressor with fuel flow and throat area of the nozzle. Firstly, the definitions and properties of the equilibrium manifold method are presented. Secondly, the steady-state and dynamic two-step identification method of the MIMO EME model is given, and the effects of scheduling variables and input noise on model accuracy are discussed. By selecting specific path, a small amount of dynamic data is used to identify a complete EME model. Thirdly, modeling and simulation at dynamic off-design conditions show that the EME model has model accuracy close to the nonlinear component-level (NCL) model, but the model structure is simpler and the calculation is faster than that. Finally, the linearization results are obtained based on the properties of the EME model, and the stability of the model is proved through the analysis of the eigenvalues, which all have negative real parts. The EME model constructed in this paper can meet the requirements of real-time simulation and control system design.