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Advances in Machine Learning for Sensing and Condition Monitoring

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Article Chemistry, Analytical

A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

Daoguang Yang et al.

Summary: This paper utilizes the advantages of artificial intelligence algorithms and deep learning algorithms in rotating machinery fault diagnosis. By processing the signals in different ways, a feature extraction model is proposed, and a fuzzy fusion strategy is used to analyze the importance of classifiers and explore the interaction index.

SENSORS (2022)

Article Chemistry, Multidisciplinary

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Monica Tiboni et al.

Summary: This article reviews the monitoring of vibrations in rotating machinery and its applications in diagnostics. Specific patterns extracted from vibration signals are found to be effective in diagnosing abnormal functioning states. The study analyzes different methodologies used in signal measurements, pre-processing and processing, feature selection, and fault diagnosis. It also identifies research trends and innovations in vibration-based condition monitoring, providing insights for future ideas.

APPLIED SCIENCES-BASEL (2022)

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Wearable ultraviolet sensor based on convolutional neural network image processing method

Yan Chen et al.

Summary: This study proposes a wearable UV sensor based on image processing, using photochromic material and PDMS, for real-time UV monitoring and daily solar protection. The introduction of convolutional neural network image processing method successfully quantifies UV intensity and significantly reduces the impact of ambient light. The sensor demonstrates a high detection limit and recognition rate, with a fast CNN test time. Additionally, a mobile convolutional neural network-based UV intensity recognition APP is designed for practical application.

SENSORS AND ACTUATORS A-PHYSICAL (2022)

Article Biochemical Research Methods

DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites

Jidong Zhang et al.

Summary: Addressing the challenging task of analyzing RNA-binding proteins (RBPs) binding sites using an efficient computational approach, DeepPN is introduced as a deep parallel neural network using a combination of convolutional neural network (CNN) and graph convolutional network (GCN). DeepPN discriminates RBP binding sites based on learnable representation of RNA sequences, solely utilizing sequence data without the need for other structural data, such as secondary or tertiary structures of RNA. Evaluated on 24 datasets, DeepPN shows performance comparable to other state-of-the-art methods in predicting RBPs binding sites.

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A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation

Yongyi Chen et al.

Summary: This article proposes a novel cuffless blood pressure estimation framework using the Receptive Field Parallel Attention Shrinkage Network (RFPASN) and blood pressure range constraint to improve the accuracy of blood pressure prediction. By utilizing a multi-scale large receptive field convolution module and a parallel mixed domain attention module, this method can capture the long-term dynamics in the photoplethysmography (PPG) signal and enhance the discriminability and robustness of features. Experimental results on the MIMIC-II database demonstrate the promising performance of the proposed method in blood pressure prediction.

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

Baoping Jiang et al.

Summary: This paper studies the passivity-based sliding mode control for nonlinear systems and its application to dock cranes. By establishing a mathematical model, designing a sliding mode dynamic system and an adaptive control law, and developing easy-checking conditions for analysis, the validity of the proposed method is confirmed.

SENSORS (2022)

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A Graph Neural Network Based Deep Learning Predictor for Spatio-Temporal Group Solar Irradiance Forecasting

Xuan Jiao et al.

Summary: This article proposes a novel deep learning architecture for solar irradiance forecasting. It improves the accuracy and reliability of predictions and is applicable to distributed PV systems.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Article Chemistry, Analytical

Identification of gas mixtures via sensor array combining with neural networks

Jifeng Chu et al.

Summary: In this study, a sensor array was used to detect various gas mixtures, with techniques such as convolutional neural networks being effective in improving identification accuracy, and the impact of humidity on the sensor array was successfully addressed.

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Pantelis Linardatos et al.

Summary: Recent advances in artificial intelligence have led to widespread industrial adoption, with machine learning systems demonstrating superhuman performance. However, the complexity of these systems has made them difficult to explain, hindering their application in sensitive domains. Therefore, there is a renewed interest in the field of explainable artificial intelligence.

ENTROPY (2021)

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Sensors selection for tool failure detection during machining processes: A simple accurate classification model

Mohamed Abubakr et al.

Summary: In this study, a novel approach was proposed to develop an accurate and simple tool condition classification model for early failure detection during machining processes. By preprocessing signals, extracting features, and reducing features based on importance, a high-accuracy classification model was achieved. The results showed that high accuracy classification can be achieved using a single sensor, and ensemble material-dependent models also showed potential for improvement.

CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY (2021)

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Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application

Yongxiang Lei et al.

Summary: The paper presents an effective and efficient soft modeling method (SAE-WELM) for industrial processes, which outperforms existing state-of-the-art methods in capturing deep features with faster speed and higher accuracy.

CONTROL ENGINEERING PRACTICE (2021)

Review Environmental Sciences

Review of Image Classification Algorithms Based on Convolutional Neural Networks

Leiyu Chen et al.

Summary: This article summarizes the application of deep learning in image classification, covering the development of CNNs from their predecessors to the latest network architectures, as well as a comprehensive comparison and analysis of various image classification methods.

REMOTE SENSING (2021)

Article Engineering, Electrical & Electronic

DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks

Kenechi G. Omeke et al.

Summary: This study presents a new clustering algorithm DEKCS to extend the lifetime of WUSNs. The protocol selects the optimal clusterhead based on node position and battery level, and dynamically adjusts energy thresholds to prevent network disconnection. Additionally, the elbow method is applied for scalable network operation.

IEEE SENSORS JOURNAL (2021)

Article Instruments & Instrumentation

Imbalanced data fault diagnosis of hydrogen sensors using deep convolutional generative adversarial network with convolutional neural network

Yongyi Sun et al.

Summary: The study proposed a gas sensor fault diagnosis method based on DCG-CNN, which converts 1D fault signals into 2D grayscale images, enriches data samples using the DCG method, improves fault diagnosis accuracy with the CNN method, and achieves higher fault diagnosis accuracy than traditional methods.

REVIEW OF SCIENTIFIC INSTRUMENTS (2021)

Article Computer Science, Information Systems

An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks

Weimin Wen et al.

Summary: The study introduces an intrusion detection model based on convolutional deep belief networks, which effectively addresses issues of redundancy and energy consumption in intrusion detection. By utilizing unsupervised learning for feature extraction, the model enhances intrusion detection accuracy and reduces false alarm rates.

INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING (2021)

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Human activity recognition with analysis of angles between skeletal joints using a RGB-depth sensor

Omer Faruk Ince et al.

ETRI JOURNAL (2020)

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Distributed robust data clustering in wireless sensor networks using diffusion moth flame optimization

Dinesh Kumar Kotary et al.

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A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare

Md. Zia Uddin et al.

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Adaptive strategy for fault detection, isolation and reconstruction of aircraft actuators and sensors

Muhammad Taimoor et al.

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Ultrafast machine vision with 2D material neural network image sensors

Lukas Mennel et al.

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Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection

M. S. Mustafa et al.

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A Novel Hybrid Deep Neural Network to Predict Pre-impact Fall for Older People Based on Wearable Inertial Sensors

Xiaoqun Yu et al.

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2020)

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Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

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Deep learning in environmental remote sensing: Achievements and challenges

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Remote sensing image captioning via Variational Autoencoder and Reinforcement Learning

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Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm

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Learning to Sense: Deep Learning for Wireless Sensing with Less Training Efforts

Jie Wang et al.

IEEE WIRELESS COMMUNICATIONS (2020)

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A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks

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EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications

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Research on Sewage Monitoring and Water Quality Prediction Based on Wireless Sensors and Support Vector Machines

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Machining sensor data management for operation-level predictive model

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A Quadral-Fuzzy Control Approach to Flight Formation by a Fleet of Unmanned Aerial Vehicles

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LSTM-CNN Architecture for Human Activity Recognition

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Imran Ashraf et al.

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Deep Learning on Multi Sensor Data for Counter UAV Applications-A Systematic Review

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Object Detection With Deep Learning: A Review

Zhong-Qiu Zhao et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)

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A Review on Convolutional Neural Network in Bearing Fault Diagnosis

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Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data

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Predicting tool wear with multi-sensor data using deep belief networks

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Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification

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