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- research-articleMarch 2024
Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk
AbstractNaturally inspired designs of training environments for reinforcement learning (RL) often suffer from highly skewed encounter probabilities, with a small subset of experiences being encountered frequently, while extreme experiences remain rare. ...
- research-articleMarch 2024
A hybrid methodology for anomaly detection in Cyber–Physical Systems
AbstractThe rapid adoption of Industry 4.0 has seen Information Technology (IT) networks increasingly merged with Operational Technology (OT) networks, which have traditionally been isolated on air-gapped and fully trusted networks. This increased attack ...
Highlights- Anomaly detection in Cyber–Physical Systems.
- Internet of Things (IoT) Security.
- One-class machine learning.
- research-articleMarch 2024
Adaptive learning control of robot manipulators via incremental hybrid neural network
AbstractA novel hybrid neural network based learning control method is proposed to improve trajectory tracking accuracy for complex robot manipulators in this paper. Firstly, a hybrid neural network is presented to improve the model accuracy and data ...
- research-articleMarch 2024
Finite-time quasi-synchronization of multi-layer heterogeneous networks with distributed hybrid control
AbstractIn this paper, the finite-time quasi-synchronization of a multi-layer heterogeneous network with a leader node is studied. A novel distributed control approach that incorporates impulsive control is introduced to achieve finite-time quasi-...
- research-articleMarch 2024
Robust learning of parsimonious deep neural networks
AbstractWe propose a simultaneous learning and pruning algorithm capable of identifying and eliminating irrelevant structures in a neural network during the early stages of training. Simultaneous learning and pruning presents serious challenges such as ...
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- articleMarch 2024
Topologies in distributed machine learning: Comprehensive survey, recommendations and future directions
AbstractWith the widespread use of distributed machine learning (DML), many IT companies have established networks dedicated to DML. Different communication architectures of DML have different traffic patterns and different requirements on network ...
- research-articleFebruary 2024
Neuromorphic imaging and classification with graph learning
AbstractBio-inspired neuromorphic cameras asynchronously record pixel brightness changes and generate sparse event streams. They can capture dynamic scenes with little motion blur and more details in extreme illumination conditions. Due to the ...
Highlights- Asynchronous event streams of neuromorphic cameras shape synchronous graph representations.
- Graph Transformer effectively learns informative features from event-based graphs.
- Temporal-wise connectivity and learning suffer less from ...
- research-articleFebruary 2024
Improving robustness for vision transformer with a simple dynamic scanning augmentation
AbstractVision Transformer (ViT) has demonstrated promising performance in computer vision tasks, comparable to state-of-the-art neural networks. Yet, this new type of deep neural network architecture is vulnerable to adversarial attacks limiting its ...
- research-articleFebruary 2024
Personalized robotic control via constrained multi-objective reinforcement learning
AbstractReinforcement learning is capable of providing state-of-art performance in end-to-end robotic control tasks. Nevertheless, many real-world control tasks necessitate the balancing of multiple conflicting objectives while simultaneously ensuring ...
Graphical abstractDisplay Omitted
Highlights- A constrained multi-objective reinforcement learning approach is proposed.
- The method is formulated as a constrained multi-objective Markov decision process.
- An evenness metric is designed through Shannon–Wiener diversity index.
- research-articleFebruary 2024
An AER-based spiking convolution neural network system for image classification with low latency and high energy efficiency
AbstractSpiking neural network is used to solve the problems of power consumption and computation requirement of the traditional artificial neural network. This paper proposes an event-driven multilayer spiking convolutional neural network (SCNN) ...
- research-articleFebruary 2024
Analysis of synchronous localization systems for UAVs urban applications
- Javier Díez-González,
- Rubén Ferrero-Guillén,
- Paula Verde,
- Alberto Martínez-Gutiérrez,
- José-Manuel Alija-Pérez,
- Hilde Perez
AbstractUnmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform ...
Highlights- The comparison among different time-based localization architectures for UAVs navigation in deep urban environments.
- A novel analysis for optimally deploying time-based localization systems for drone localization in urban scenarios.
- research-articleFebruary 2024
Event-triggered critic learning impedance control of lower limb exoskeleton robots in interactive environments
AbstractIn this paper, we present an event-triggered critic learning impedance control algorithm for a lower limb rehabilitation exoskeleton robot in an interactive environment, where the control objective is specified by a desired impedance model. In ...
- research-articleFebruary 2024
TFF-CNN: Distributed optical fiber sensing intrusion detection framework based on two-dimensional multi-features
AbstractDistributed fiber optic vibration sensing system has been widely used in safety monitoring with distinct advantages, and the feature extraction and classification methods of fiber optic signals directly determine the real-time performance and ...
- research-articleJanuary 2024
Human–robot collaborative interaction with human perception and action recognition
AbstractThis paper presents a human–robot interaction system (HRIS) that utilizes human perception and action recognition to enable the robot to understand human intentions and flexibly interact with humans. A monocular multi-person three-dimensional (3D)...
- research-articleDecember 2023
Weed mapping in multispectral drone imagery using lightweight vision transformers
AbstractIn precision agriculture, non-invasive remote sensing can be used to observe crops and weeds in visible and non-visible spectra. This paper proposes a novel approach for weed mapping using lightweight Vision Transformers. The method uses a ...
Highlights- A new weed mapping approach is proposed for images captured by a drone.
- The approach uses a lightweight Transformer that performs semantic segmentation.
- New techniques are proposed for transfer learning from RGB in a multispectral ...
- research-articleDecember 2023
Neural network-based sliding mode controllers applied to robot manipulators: A review
AbstractIn recent years, numerous attempts have been made to integrate sliding mode control (SMC) and neural networks (NN) in order to leverage the advantages of both methods while mitigating their respective disadvantages. These endeavors have yielded ...
- research-articleDecember 2023
Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorphic accelerator
AbstractSpiking Neural Networks (SNN) promise extremely low-power and low-latency inference on neuromorphic hardware. Recent studies demonstrate the competitive performance of SNNs compared with Artificial Neural Networks (ANN) in conventional ...
- research-articleDecember 2023
Integrating reinforcement learning with deterministic learning for fault diagnosis of nonlinear systems
AbstractReliable fault diagnosis (FD) is important to ensure safety in nonlinear engineering systems. Modern engineering systems are often subject to unknown complex nonlinearities and varying operation conditions, therefore, one of the main challenges ...
- research-articleDecember 2023
A Hybrid Weight Quantization Strategy for Memristive Neural Networks
AbstractDue to the ability to store data and process information, the memristor-based neuromorphic system has attracted extensive attention. Its efficient parallel computing approach allows it to implement neural networks in hardware. However, due to the ...
Highlights- Obtained the pulse conductance relations from modeling the tantalum oxide memristor.
- A weight mapping rule based on the memristor array is proposed.
- Hybrid quantization schemes are proposed to improve the performance.
- research-articleDecember 2023
NUTS-BSNN: A non-uniform time-step binarized spiking neural network with energy-efficient in-memory computing macro
AbstractThis work introduces a network architecture NUTS-BSNN: A Non-uniform Time-step Binarized Spiking Neural Network. NUTS-BSNN is a fully binarized spiking neural network with all binary weights, including the input and output layers. In the input ...