Dual-actuator-type active noise control in vibro-acoustic systems with openings.

Openings in plate structures are essential in various engineering applications, particularly in vibro-acoustic systems where airflow is required. This paper investigates noise control in vibro-acoustic systems with noise barriers incorporating structural openings, focusing on active noise control and Active Structural Acoustic Control (ASAC). It also introduces a novel approach, Dual-Actuator-Type Active Noise Control (DATANC), which combines loudspeakers and inertial actuators into the same barrier to address the challenges of noise reduction.

An improved multi-strategy equilibrium optimizer for surface marine vehicle path planning.

To address the limitations of the standard equilibrium optimizer (EO) in terms of insufficient optimization capability, multiple strategies are proposed to enhance its performance. These include a reverse equilibrium state pool, a non-uniform equilibrium state selection strategy, and an equilibrium state mutation strategy. The reverse equilibrium state pool is introduced to encourage candidate solutions with poorer positions to search in a wider search space, under such considerations the global search ability of the improved EO can be enhanced.

Multimodal prediction of catheter ablation outcomes in patients with persistent atrial fibrillation.

The recurrence of atrial fibrillation (AF) following catheter ablation is a common complication in patients with persistent atrial fibrillation (psAF), increasing the risk of stroke and heart failure thereafter. Given the multifactorial nature of post-ablation AF, clinical predictions of successful ablation often suffer from poor accuracy and lack robustness. This paper proposes a multimodal prediction model for post-ablation AF, which extracts complex features from multidimensional data, including electrocardiogram (ECG) images, cellular characteristics, intraoperative and demographic information of patients.

Exploring temporal information dynamics in Spiking Neural Networks: Fast Temporal Efficient Training.

Background: Spiking Neural Networks (SNNs) hold significant potential in brain simulation and temporal data processing. While recent research has focused on developing neuron models and leveraging temporal dynamics to enhance performance, there is a lack of explicit studies on neuromorphic datasets. This research aims to address this question by exploring temporal information dynamics in SNNs.

Driving style classification and recognition methods for connected vehicle control in intelligent transportation systems: A review.

Advancements in intelligent vehicle technology have spurred extensive research into the impact of driving style (DS) on intelligent transportation systems (ITS), aiming to enhance vehicle safety, comfort, and energy efficiency. Accurate DS identification is pivotal for accelerating ITS adoption, especially in regions where its implementation is still in its infancy. This paper investigates the role of DS recognition methods, particularly clustering and classification techniques, in influencing connected vehicle control and optimizing speed planning within ITS.

ShadowGAN-Former: Reweighting self-attention based on mask for shadow removal.

Shadow removal remains a challenging visual task aimed at restoring the original brightness of shadow regions in images. Many existing methods overlook the implicit clues within non-shadow regions, leading to inconsistencies in the color, texture, and illumination of the reconstructed shadow-free images. To address these issues, we propose an efficient hybrid model of Transformer and Generative Adversarial Network (GAN), named ShadowGAN-Former, which utilizes information from non-shadow regions to assist in shadow removal.

Deep reinforcement learning and fuzzy logic controller codesign for energy management of hydrogen fuel cell powered electric vehicles.

Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration.

Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection.

Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation.

Rehabilitation and reinforcement of cracked pavements using crack sealers and composite patches.

Cracking is a significant concern for pavements and should be appropriately treated during road, highway, and runway rehabilitation. This study investigates the behavior of asphaltic materials under tensile and shear loading modes in intact, fractured, and repaired conditions. With this aim, several methods and materials are utilized for repairs, such as poring adhesive into the crack (using bitumen, neat epoxy resin, and polymer concrete adhesives) and patching the crack with textile (by glass fiber and epoxy resin or bitumen).

Planetary gearbox fault classification based on tooth root strain and GAF pseudo images.

Traditional signal processing methods based on acceleration signals can determine whether a fault has occurred in a planetary gearbox. However, acceleration signals are severely affected by interference, causing difficulties in fault identification. This study proposes a gear fault classification method based on root strain and pseudo images.

A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis.

Graph convolutional networks (GCNs) as the emerging neural networks have shown great success in Prognostics and Health Management because they can not only extract node features but can also mine relationship between nodes in the graph data. However, the most existing GCNs-based methods are still limited by graph quality, variable working conditions, and limited data, making them difficult to obtain remarkable performance. Therefore, it is proposed in this paper a two stage importance-aware subgraph convolutional network based on multi-source sensors named ISGCN to address the above-mentioned limitations.

On the fatigue life of dental implants: Numerical and experimental investigation on configuration effect.

Dental implants have seen widespread and successful use in recent years. Given their long-term application and the critical role of geometry in determining fracture and fatigue characteristics, fatigue assessments are of utmost importance for implant systems. In this study, nine dental implant system samples were subjected to testing in accordance with ISO 14801 standards.

A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data.

Recently, due to the difficulty of collecting condition data covering all mechanical fault types in industrial scenarios, the fault diagnosis problem under incomplete data is receiving increasing attention where no target prior information can be available. The existing open-set or universal domain adaptation (DA) diagnosis methods typically treat private fault samples in the target as a generalized "unknown" fault class, neglecting their inherent structure. This oversight can lead to confusion in latent feature space representations and difficulties in separating unknown samples.

Fixed-time periodic stabilization of discontinuous reaction-diffusion Cohen-Grossberg neural networks.

This paper aims to study the fixed-time stabilization of a class of delayed discontinuous reaction-diffusion Cohen-Grossberg neural networks. Firstly, by providing some relaxed conditions containing indefinite functions and based on inequality techniques, a new fixed-time stability lemma is given, which can improve the traditional ones. Secondly, based on state-dependent switching laws, the periodic wave solution of the formulated networks is transformed into the periodic solution of ordinary differential system.

Adaptive neural-network-based sliding mode control of switching distributed delay systems with Markov jump parameters.

This paper is devoted to the issue of observer-based adaptive sliding mode control of distributed delay systems with deterministic switching rules and stochastic jumping process, simultaneously, through a neural network approach. Firstly, relying on the designed Lebesgue observer, a sliding mode hyperplane in the integral form is put forward, on which a desired sliding mode dynamic system is derived. Secondly, in consideration of complexity of real transition rates information, a novel adaptive dynamic controller that fits to universal mode information is designed to ensure the existence of sliding motion in finite-time, especially for the case that the mode information is totally unknown.

An adaptive reinforcement learning-based multimodal data fusion framework for human-robot confrontation gaming.

Playing games between humans and robots have become a widespread human-robot confrontation (HRC) application. Although many approaches were proposed to enhance the tracking accuracy by combining different information, the problems of the intelligence degree of the robot and the anti-interference ability of the motion capture system still need to be solved. In this paper, we present an adaptive reinforcement learning (RL) based multimodal data fusion (AdaRL-MDF) framework teaching the robot hand to play Rock-Paper-Scissors (RPS) game with humans.

Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis.

Intelligent fault diagnosis aims to build robust mechanical condition recognition models with limited dataset. At this stage, fault diagnosis faces two practical challenges: (1) the variability of mechanical working conditions makes the collected data distribution inconsistent, which brings about the domain shift; (2) some unpredictable unknown fault modes that do not observe in the training dataset may occur in the testing scenario, leading to a category gap. In order to cope with these two entangled challenges, an open set multi-source domain adaptation approach is developed in this study.

Robot path planning based on artificial potential field with deterministic annealing.

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot's obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination.

Measurement outlier-resistant mobile robot localization using multiple Doppler-azimuth radars under round-robin protocol.

This paper is concerned with the measurement outlier-resistant mobile robot localization problem by using multiple Doppler-azimuth radars under round-robin protocol (R-RP). In the considered robot localization system, multiple Doppler-azimuth radars are equipped on the robot platform to produce the measurement including the Doppler frequency shift and the azimuth. In order to assuage communication link congestion, the R-RP is used.

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health.

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration.

An event-triggered mechanism to observer-based sliding mode control of fractional-order uncertain switched systems.

This paper is dealing with the problem of observer-based event-triggered sliding mode control for fractional-order uncertain switched systems with a positive order less than one. Firstly, a fractional-order state observer is designed, based on which a fractional-order integral sliding surface function is proposed. Then, utilizing the estimated observer error and sliding mode error vectors, an event-triggered condition is constructed to decide whether the current control signal should be updated or not.

Neural network-based event-triggered data-driven control of disturbed nonlinear systems with quantized input.

This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances.

RBF Neural Network Sliding Mode Control for Passification of Nonlinear Time-Varying Delay Systems with Application to Offshore Cranes.

This paper is devoted to studying the passivity-based sliding mode control for nonlinear systems and its application to dock cranes through an adaptive neural network approach, where the system suffers from time-varying delay, external disturbance and unknown nonlinearity. First, relying on the generalized Lagrange formula, the mathematical model for the crane system is established. Second, by virtue of an integral-type sliding surface function and the equivalent control theory, a sliding mode dynamic system can be obtained with a satisfactory dynamic property.