Space–time diversity (STD) has been widely applied in underwater acoustic (UWA) communication due to its exceptional anti-multipath performance. However, underwater noise can seriously affect the processing results of STD. The conventional filt...
The transition from single-device characterization to array-level simulation remains a critical challenge in the development of three-terminal synaptic transistors for neuromorphic computing, as most existing simulation studies either extract paramet...
When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with t...
Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability t...
Addressing the issues of prolonged training times and low recognition rates in large model applications, this paper proposes a weight training method based on entropy gain for weight initialization and dynamic adjustment of the learning rate using th...
The resilience and stability of industrial supply chains are the core pillars of the sustainable development of the global economy, and also an important foundation for achieving the United Nations Sustainable Development Goals of “Responsible...
Accurate estimation of Behind-the-meter Photovoltaic (BTM PV) generation is essential for load forecasting and grid planning. Most distributed PV systems are installed behind customer meters, making their output unobservable. Disaggregating PV output...
Federated learning, as a distributed machine learning framework, aims to protect data privacy while addressing the issue of data silos by collaboratively training models across multiple clients. However, a significant challenge to federated learning...
In the field of engineering, the advancement of automated scoring systems for laboratory reports has been significantly hampered by three persistent challenges: scarcity of high-quality annotated data, high domain-specific complexity, and insufficien...
Particle swarm optimization (PSO) is an optimizing method that is based on the theory of swarm intelligence. PSO is an effective algorithm that is used to search in a parallel manner compared to other methods. However, PSO has a tendency towards loca...
The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS,...
An improved adaptive particle swarm algorithm with guiding strategy (GSAPSO) was proposed, and it was applied to solve the reactive power optimization (RPO). Four kinds of particles containing the main particles, double central particles, cooperative...
Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data...
Owing to the growing interest in environmental problems worldwide, it is essential to schedule power generation considering the effects of pollutants. To address this, we propose an optimal approach that solves the combined economic emission dispatch...
An evolutionary knowledge update framework—self-training with confidence-gated pseudo-labels, elastic weight consolidation, and a rollback rule—was implemented for intracranial region segmentation in Time-of-Flight MR Angiography (TOF-MRA...
In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly signi...
In this paper, a novel approach involving a fractional recurrent neural network (RNN) is proposed to achieve the observer-based synchronization of a cancer cell model. According to the properties of recurrent neural networks, our proposed framework s...
The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a pro...
Accurate remaining useful life (RUL) prediction of rolling bearings plays a critical role in predictive maintenance. However, existing methods face challenges in extracting and fusing multi-source spatiotemporal features, addressing distribution diff...
Background/Objectives: Food cravings involve a strong drive to consume palatable foods irrespective of nutritional status. Importantly, cravings contribute substantially to the obesity epidemic. Managing hunger alone is insufficient for weight manage...
A well-balanced metabolism means a lower risk for metabolism-related neuropsychiatric disorders. Leptin is a secretory adipokine involved in the central control of appetite that appears to play a role in the etiology of feeding-related disorders. Add...
We propose a limited-memory quasi-Newton method using the bad Broyden update and apply it to the nonlinear equations that must be solved to determine the effective Fermi momentum in the weighted density approximation for the exchange energy density f...
Accurate dynamic characterization of wellbore multiphase flow is fundamental for production optimization and real-time control in oil and gas wells. Addressing technical constraints of existing dynamic fitting methods, this study proposes a novel dyn...
Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, the neuron units and weights are shared at each time step to reduce computational costs, limiting...
It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model struc...
This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial veh...
Cloud manufacturing is a current trend in traditional manufacturing enterprises. In this environment, manufacturing resources and manufacturing capabilities are allocated to corresponding services through appropriate scheduling, while research on the...
In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO
To date, diffusion weighted imaging (DWI) is included in routine magnetic resonance imaging (MRI) protocols for several cancers. The real additive role of DWI lies in the “functional” information obtained by probing the free diffusivity of water mole...
This paper addresses the depth regulation of an inspection-class remotely operated vehicle whose hydrodynamic model is uncertain and whose environment applies a persistent, non-vanishing wave disturbance. A neural-network learning term added to a pro...
More accurate diagnosis of brain disorders can be achieved by properly analyzing structural changes in the brain. For the quantification of change in brain structure, the segmentation task is crucial. Recently, generative adversarial networks (GAN) h...
To address the issue that existing penalty weight update strategies in the augmented Lagrangian multiplier method are disconnected from the optimality conditions and remain relatively sensitive to initial parameters, this paper proposes a complementa...
Federated Learning (FL) systems are increasingly vulnerable to data poisoning attacks, in which malicious clients attempt to manipulate their training data in order to compromise the corresponding machine learning model. Existing detection techniques...
Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a r...
Here, we report the current status of the bioactive peptides isolated and characterized from mushrooms during the last 20 years, considering ‘peptide’ a succession from to 2 to 100 amino acid residues. According to this accepted biochemic...
Timely status updates are critical in remote control systems such as autonomous driving and the industrial Internet of Things, where timeliness requirements are usually context dependent. Accordingly, the Urgency of Information (UoI) has been propose...
Low-rank adaptation (LoRA) fine-tunes a large model with a low-rank update over frozen weights. The update travels as a separate adapter file that is easy to copy, alter, and re-upload; so, a hub may have to decide whether a disputed file derives fro...
Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenge...
This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective...
Existing edge computing architectures do not support the updating of neural network models, nor are they optimized for storing, updating, and transmitting different neural network models to a large number of IoT devices. In this paper, a cloud-edge s...
Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the...
This paper presents a novel hardware architecture for principal component analysis. The architecture is based on the Generalized Hebbian Algorithm (GHA) because of its simplicity and effectiveness. The architecture is separated into three portions: t...
The self-organizing map (SOM), which is a type of artificial neural network (ANN), was formulated as an optimal control problem. Its objective function is to minimize the mean quantization error, and the state equation is the weight updating equation...
Velocity updates have been proven to be important for constraining motion-sensor-based dead-reckoning (DR) solutions in indoor unmanned aerial vehicle (UAV) applications. The forward velocity from a mass flow sensor and the lateral and vertical non-h...
Equilibrium propagation (EP) has been proposed recently as a new neural network training algorithm based on a local learning concept, where only local information is used to calculate the weight update of the neural network. Despite the advantages of...
Federated learning enables distributed clients to collaboratively train a global model without sharing their private data. However, Byzantine robustness becomes more challenging under heterogeneous client availability, since Byzantine clients with hi...
This study proposes an improved vector-weighted averaging algorithm (IDEINFO) for the optimization of different problems. The original vector-weighted optimization algorithm (INFO) uses weighted averaging for entity structures and uses three core pro...
This work studies the asymptotic tracking control problem of a vertical take-off and landing (VTOL) aircraft with unknown dynamics and external disturbances. The unknown nonlinear dynamics of the VTOL aircraft are approximated via the introduction of...
A synaptic device that contains weight information between two neurons is one of the essential components in a neuromorphic system, which needs highly linear and symmetric characteristics of weight update. In this study, a charge trap flash (CTF) mem...
The amount by which the artificial neural network weights are updated during the training process is called the learning rate. More precisely, the learning rate is an adjustable parameter used in training neural networks in which small values, often...
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