Explore the vast range of titles available.

In this paper, we propose systematic controller design guidelines to ensure both individual vehicle stability and string stability in cooperative adaptive cruise control (CACC)-based platoon systems, assuming a homogeneous platoon where all vehicles share identical dynamic models. We rigorously demonstrate that the limitation of conventional adaptive cruise control (ACC) in maintaining the target inter-vehicle distance can be effectively overcome by incorporating the desired acceleration of the preceding vehicle as a static feedforward input. Furthermore, by formulating transfer functions in the frequency domain, we analytically derive the conditions required to ensure both individual vehicle stability and string stability of the CACC system. Building on this insight, we propose a practical and theoretically well-founded design guideline for determining the proportional, derivative, and feedforward gains of control input under a constant time gap spacing policy. The proposed guidelines are validated through simulations conducted in a realistic platooning scenario involving multiple vehicles.

This study investigated the relationship between different disfluency types (i.e., repetitions, prolongations, and blocks) and resting state functional connectivity in the feedback (FB) and feedforward (FF) control networks in 20 adults who stutter. Frequency of each disfluency type was coded in speech samples derived from the Stuttering Severity Instrument, and functional connectivity between brain regions of interest was derived from resting state functional magnetic resonance imaging scans. We used LASSO regressions to identify the connections that most strongly predicted each disfluency type. Both repetitions and prolongations were significantly associated with increased connectivity in left ventral motor cortex - right ventral premotor cortex, which is hypothesized to be involved in FB control of speech. In contrast, blocks were significantly associated with reduced connectivity in right anterior cerebellum - left ventral lateral thalamic nucleus and increased connectivity in left presupplementary motor area - left posterior inferior frontal sulcus, both of which are hypothesized to be involved in FF control of speech. Our findings suggest that repetitions and prolongations may be associated with increased reliance on FB-based corrective mechanisms, whereas blocks may be associated with disrupted FF-based initiation mechanisms. These neural underpinnings may correspond to different challenges in terminating or initiating motor commands and underscore the nuanced neurobiological processes underlying speech disfluencies.

Superconducting circuits combined with real-time feed-forward electronics are used to teleport a quantum state between two macroscopic solid-state systems. Efficient teleportation on demand Quantum teleportation is one of the most important elementary protocols in quantum information processing. Previous studies have achieved quantum teleportation, but usually randomly and at low rates. Two groups reporting in this issue of Nature have used contrasting methods to achieve the same aim —more efficient quantum teleportation. Takeda et al . describe the experimental realization of fully deterministic, unconditional quantum teleportation of photonic qubits — an optimum choice for information carrying — with overall transfer fidelities exceeding the classical limit of teleportation. The technique may facilitate the development of large-scale optical quantum networks. Steffen et al . report quantum teleportation in a solid-state system, achieving deterministic quantum teleportation in a chip-based superconducting circuit architecture. They teleport quantum states between two macroscopic systems separated by 6 mm at a rate of 10,000 per second, exceeding other reported implementations. Transmission loss in superconducting waveguides is low, so this system should be scalable to significantly larger distances, a step towards quantum communication at microwave frequencies. Engineered macroscopic quantum systems based on superconducting electronic circuits are attractive for experimentally exploring diverse questions in quantum information science 1 , 2 , 3 . At the current state of the art, quantum bits (qubits) are fabricated, initialized, controlled, read out and coupled to each other in simple circuits. This enables the realization of basic logic gates 4 , the creation of complex entangled states 5 , 6 and the demonstration of algorithms 7 or error correction 8 . Using different variants of low-noise parametric amplifiers 9 , dispersive quantum non-demolition single-shot readout of single-qubit states with high fidelity has enabled continuous 10 and discrete 11 feedback control of single qubits. Here we realize full deterministic quantum teleportation with feed-forward in a chip-based superconducting circuit architecture 12 , 13 , 14 . We use a set of two parametric amplifiers for both joint two-qubit and individual qubit single-shot readout, combined with flexible real-time digital electronics. Our device uses a crossed quantum bus technology that allows us to create complex networks with arbitrary connecting topology in a planar architecture. The deterministic teleportation process succeeds with order unit probability for any input state, as we prepare maximally entangled two-qubit states as a resource and distinguish all Bell states in a single two-qubit measurement with high efficiency and high fidelity. We teleport quantum states between two macroscopic systems separated by 6 mm at a rate of 10 4  s −1 , exceeding other reported implementations. The low transmission loss of superconducting waveguides is likely to enable the range of this and other schemes to be extended to significantly larger distances, enabling tests of non-locality and the realization of elements for quantum communication at microwave frequencies. The demonstrated feed-forward may also find application in error correction schemes.

Classification of plastic materials based on spectroscopic data is a very crucial task in a variety of applications, including automated recycling, environmental monitoring, quality control in manufacturing, quality control of products, and analysis of complex material properties. These applications demand high precision in identifying and separating plastic types to enhance sustainability and ensure regulatory compliance. In this work, we presented a novel technique Hybridized Attention mechanism-based Feedforward Neural Network (HAttFFNN) to detect three stage Polyethylene Terephthalate (PET) materials. Dataset used in this methodology is basically comprised of 295,327 samples, and contains the parameters like absorbance, wavelengths, references, samples. We collected the spectral data (900-1700 nm) using the Digital Light Processing (DLP) Near-Infrared (NIR) scan Nano Evaluation Module (EVM). We utilized various preprocessing techniques for better and improved detection result, such as Savitzky-Golay filter, interference, Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC). The preprocessed and organized spectral data is provided to the proposed HAttFFNN model for the detection of three stage PET material. To validate the performance of the proposed model, we experimented various State-Of-The-Art (SOTA) models, Multi-Head Neural Network (MHNN), Virtual Geometry Group (VGG16), One-Dimensional Convolutional Neural Network (1D-CNN), Residual Network (ResNet), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The proposed model outperforms state of the art techniques across all metrics including accuracy, precision, recall, F1 score, and specificity with Stage 1 (PET Clear vs PET Hazard) achieving 99.33% accuracy, Stage 2 (PET vs Others) 99.32%, and Stage 3 (PET Coloured vs PET Transparent) 99.28%, along with consistently high precision, recall, and specificity values for each class. These results confirm that our proposed model, HAttFFNN, is able to achieve higher accuracy in spectroscopic classification domain, especially in complex cases such as differentiating between visually and spectrally similar materials (PET Clear vs PET Hazard, PET vs Others and PET Colored vs PET Transparent) where traditional models often fail. Furthermore, the Root Mean Square Error (RMSE) values 0.1408 for Stage 1, 0.1249 for Stage 2, and 0.1403 for Stage 3, further validate the model's low-error performance, reinforcing its effectiveness as a less error-prone approach for spectrometry-based plastic material classification.

With advancements in mobile robot technology, wheel-legged robots have emerged as promising next-generation mobile solutions, reducing design costs and enhancing adaptability in unstructured environments. As underactuated systems, their balance control has become a prominent research focus. Despite there being numerous control approaches, challenges remain. Balance control methods for wheel-legged robots are influenced by hardware characteristics, such as motor friction, which can induce oscillations and hinder dynamic convergence. This paper presents a friction feedforward Linear Quadratic Regulator (LQR) balance control method. Specifically, a basic LQR controller is developed based on the dynamics model of the wheel-legged robot, and a Stribeck friction model is established to characterize motor friction. A constant-speed excitation trajectory is designed to gather data for friction identification, and the Particle Swarm Optimization (PSO) algorithm is applied to determine the optimal friction parameters. The identified friction model is subsequently incorporated as feedforward compensation for the LQR controller’s torque output, resulting in the proposed friction feedforward LQR balance control algorithm. The minimum standard deviation for friction identification is approximately 0.30, and the computed friction model values closely match the actual values, indicating effective and accurate identification results. Balance experiments demonstrate that under diverse conditions—such as flat ground, single-sided bridges, and disturbance scenarios—the convergence performance of the friction feedforward LQR algorithm markedly surpasses that of the baseline LQR, effectively reducing oscillations, accelerating convergence, and improving the robot’s stability and robustness.

Purpose: The purpose of this study was to explore the relationship between feedback and feedforward control of articulation and voice by measuring reflexive and adaptive responses to first formant (F[subscript 1]) and fundamental frequency (f[subscript o]) perturbations. In addition, perception of F[subscript 1] and f[subscript o] perturbation was estimated using passive (listening) and active (speaking) just noticeable difference paradigms to assess the relation of auditory acuity to reflexive and adaptive responses. Method: Twenty healthy women produced single words and sustained vowels while the F[subscript 1] or f[subscript o] of their auditory feedback was suddenly and unpredictably perturbed to assess reflexive responses or gradually and predictably perturbed to assess adaptive responses. Results: Typical speakers' reflexive responses to sudden perturbation of F[subscript 1] were related to their adaptive responses to gradual perturbation of F[subscript 1]. Specifically, speakers with larger reflexive responses to sudden perturbation of F[subscript 1] had larger adaptive responses to gradual perturbation of F[subscript 1]. Furthermore, their reflexive responses to sudden perturbation of F[subscript 1] were associated with their passive auditory acuity to F[subscript 1] such that speakers with better auditory acuity to F[subscript 1] produced larger reflexive responses to sudden perturbations of F[subscript 1]. Typical speakers' adaptive responses to gradual perturbation of F[subscript 1] were not associated with their auditory acuity to F[subscript 1]. Speakers' reflexive and adaptive responses to perturbation of f[subscript o] were not related, nor were their responses related to either measure of auditory acuity to f[subscript o]. Conclusion: These findings indicate that there may be disparate feedback and feedforward control mechanisms for articulatory and vocal error correction based on auditory feedback.

Each sub-region of the hippocampus plays a critical computational role in the formation of episodic learning and memory, but studies have yet to show and interpret the individual spiking dynamics of each region and how that information is passed between each subregion. This is in part due to the difficulty in accessing individual communicating axons. Here, we created a novel microfluidic device that facilitates network growth of four separated hippocampal subregions over a micro-electrode array. This device enabled monitoring single axons over two electrodes so direction of spike propagation in interregional communication could be ascertained. In this in vitro hippocampal study, we compared spiking dynamics across two novel four-compartment device architectures: one with four sets of axon tunnels between subregions that excluded the perforant pathway from EC-CA3, and one with five sets of axon tunnels that included the EC-CA3 connection. We found 30–90% faster feed-forward firing rates (shorter interspike intervals) in axons in the five-tunnel model with 35–75% slower bursting dynamics (longer interburst intervals) compared to the four-tunnel model. The CA3-CA1 and CA1-EC axons had more spikes in bursts in the five-tunnel architecture than the four-tunnel counterpart suggesting more structured information transfer. Feedback firing rates were similar between configurations. The faster feed-forward inter-regional spiking in the more natural five-tunnel than the four-tunnel configuration suggests tighter control of spiking and possibly more precise communication between subregions.

The negative high-pass filter feedback of the grid current (NFGCF) can offer active damping for the LCL-type grid-connected inverter. Due to the control delay in digital control systems, this damping can cause the system to exhibit non-minimum phase behavior within specific frequency ranges. This study proposes a joint active damping approach that combines grid current feedback and the point of common coupling (PCC) voltage unit feedforward. The proposed method introduces a dynamic damping region that varies with grid impedance. By developing suitable damping loop control parameters, this region can span the entire frequency range, even exceeding the Nyquist frequency fs/2. The research results demonstrate that the proposed approach enhances robustness against variations in grid impedance and eliminates non-minimum phase behavior. Simulation and experimental outcomes validate the effectiveness of this joint active damping method.

The shift of the world in the past two decades towards renewable energy (RES), due to the continuously decreasing fossil fuel reserves and their bad impact on the environment, has attracted researchers all around the world to improve the efficiency of RES and eliminate problems that arise at the point of common coupling (PCC). Harmonics and un-balance in 3-phase voltages because of dynamic and nonlinear loads cause a lagging power factor due to inductive load, active power losses, and instability at the point of common coupling. This also happens due to a lack of system inertia in micro-grids. Passive filters are used to eliminate harmonics at both the electrical converter’s input and output sides and improve the system’s power factor. A Synchronous Reference Frame (SRF) control method is used to overcome the problem related to grid synchronization. The sine pulse width modulation (SPWM) technique provides gating signals to the switches of the multilevel inverter. A multi-layer feed forward neural network (ML-FFNN) is employed at the output of a system to minimize mean square error (MSE) by removing the errors between target voltages and reference voltages produced at the output of a trained model. Simulations were performed using MATLAB Simulink to highlight the significance of the proposed research study. The simulation results show that our proposed intelligent control scheme used for the suppression of harmonics compensated for reactive power more effectively than the SRF-based control methods. The simulation-based results confirm that the proposed ML-FFNN-based harmonic and reactive power control technique performs 0.752 better in terms of MAE, 0.52 for the case of MSE, and 0.222 when evaluating based on the RMSE.

Cortical circuits process both sensory and motor information in animals performing perceptual tasks. However, it is still unclear how sensory inputs are transformed into motor signals in the cortex to initiate goal-directed actions. In this study, we found that a visual-to-motor inhibitory circuit in the anterior cingulate cortex (ACC) triggers precise action in mice performing visual Go/No-go tasks. Three distinct features of ACC neurons—visual amplitudes of sensory neurons, suppression times of motor neurons and network activity from other neurons—predicted response times of the mice. Moreover, optogenetic activation of visual inputs in the ACC, which drives fast-spiking sensory neurons, prompted task-relevant actions in mice by suppressing ACC motor neurons and disinhibiting downstream striatal neurons. Notably, when mice terminated actions in response to stop signals, both motor neuron and network activity increased. Collectively, our data demonstrate that visual inputs to the frontal cortex trigger gated feedforward inhibition to initiate goal-directed actions. Kim et al. found that visual inputs trigger gated feedforward inhibition of ACC neurons, which disinhibits striatal motor neurons and initiates precise responses in mice performing a visual Go/No-go task.