Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is crucial in the field of industrial intelligence. However, existing generalized models face challenges in ...battery life prediction due to the presence of prevalent noise and limited degradation data. To address this issue, we proposed a spatiotemporally integrated RUL prediction model, namely, the DAE-MSCNN-LSTM model, which leverages the combination of multiscale convolutional neural network (MSCNN) and long-short-term memory network (LSTM). This approach can be used to effectively extract feature information from limited available data. Moreover, the model incorporates a denoising autoencoder (DAE) to refine the raw data by mitigating noise and handling outliers. Subsequently, the processed data are fed into parallel MSCNN and LSTM networks, allowing us to capture both spatial and temporal information. The fused features from these networks are subsequently input into a multilayer perceptron (MLP) for RUL prediction. Additionally, we introduced a unified learning framework to address data denoising and model prediction simultaneously. Finally, the optimal hyperparameters were determined using the grid search algorithm. Through extensive experiments on the NASA and CALCE datasets, the superiority of our proposed LIB RUL prediction for model is demonstrated.
•Accurate prediction of the remaining useful life of lithium-ion batteries is critical for industrial intelligence.•Addressing noise as well as insufficient amount of degraded data.•The MAE and RMSE of the model in the NASA dataset are 0.0325 and 0.0429.•The MAE and RMSE of the model in the CALCE dataset are 0.0245 and 0.0319.•Helps maintenance staff develop accurate and efficient maintenance strategies.
Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a ...novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome.
Online social networks (OSNs) are vital to people's daily lives. They offer free services that allow people to connect and interact with family and friends, post comments and images, express views on ...sports and politics, and influence other users on OSN. The significant risks to OSN security are malicious SMBs, and numerous studies have been done to identify them. This article, a Systematic Literature Review (SLR), aims to determine best practices in SMB recognition. This SLR covers research published between 2008 and 2022. As a result of this study, we classified OSN profiles into real, verified, and fake accounts. The malicious SMBs types are SMBs, spam bots, Sybil and cyborgs, stegobots, political bots, and game bots. We also proposed a classification of SMBs detection techniques, which are ML-based, DL-based, graph-based, anomaly-based, topic-modeling-based, DNA-inspired, genetic-based, and hybrid approaches. Additionally, our study revealed that most public datasets used for SMB detection only include some types of SMB, dependent on Twittersphere, include limited data, and needed to be more extensive, up-to-date and accurate. We studied challenges of SMB detection, like data labeling, features engineering, imbalanced data, etc. Finally, we proposed the opening axis for future works.
Accurate detection and localization of mechanical discontinuities are essential for industries dependent on natural, synthetic and composite materials, e.g. construction, aerospace, oil and gas, ...ceramics, metal, and geothermal industries, to name a few. In this study, a physics-informed machine learning workflow is developed for detecting and locating single, linear mechanical discontinuity in homogeneous 2D material by processing the full-waveforms recorded during multi-point compressional/shear transmission measurements. This work is based on fundamental aspects of simulation of wave propagation, signal processing, feature engineering, and data-driven model evaluation. k-Wave simulator is implemented to model the compressional and shear wave transmission through the 2D numerical model of a material containing single mechanical discontinuity. For a specific source-sensor configuration, the newly developed data-driven workflow can detect and locate the mechanical discontinuity with an accuracy higher than 0.9 in terms of coefficient of determination. AdaBoost regressor with k-Nearest Neighbor as a base estimator significantly outperforms all other models. In terms of sensitivity to noise, k-Nearest Neighbor is the most robust to both gaussian and uniform distributed noise.
As the importance of personal data privacy increases, traffic encryption has become an important topic in network communication. In the field of network security and management, the development of ...encrypted traffic classification technology has drawn attention to deep learning methods. For raw encrypted traffic, deep learning models can realize end-to-end classification with high accuracy. However, deep learning methods do not explain which part of the encrypted traffic is critical to classification, and that will limit their application in some cyber security scenarios that demand high interpretability. The approach proposed in this paper features a novel feature engineering method named “BITization” to determine the encoding method of features and a sliding window technique to explore which bytes contribute the most to the classification. The accuracy of classical machine learning methods in encrypted traffic is improved by at least 14.1% through the proposed feature engineering approach. In the experiments, the enhanced methods achieve a 98.6% average accuracy and a 98.5% average F1-score on the ISCX-VPN-Service, Cross-Platform-IOS, Cross-Platform-Android, and USTC-TFC2016 datasets, from which we believe a state-of-the-art performance is achieved.
In the long process of iron and steel, the sintering process has the largest amount of flue gas emissions, many types of pollutants and high concentrations. The source control of SO
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and NOx in ...sintering flue gas through digital technology has become a new emission reduction technology. In this study, the BP neural network model (BP-NN) is optimized by using the particle swarm algorithm (PSO) to form the PSO-BPNN model, which effectively improves the characteristics of BP-NN with slow convergence speed and easily falls into local minima, and improves the learning ability and generalization. The test results show that the PSO-BP-NN algorithm not only has fast convergence speed and high prediction accuracy, but also has smaller training and inspection errors. In addition, this model combines process theory and feature engineering selection of parameters, which effectively improves the accuracy of the model and the interpretability of the results based on the linkage of process knowledge, and has certain analytical significance for the source management and post-treatment of sintered flue gas.
Cognitive impairment features in neuropsychiatric conditions and when undiagnosed can have a severe impact on the affected individual's safety and ability to perform daily tasks. Virtual Reality (VR) ...systems are increasingly being explored for the recognition, diagnosis and treatment of cognitive impairment. In this paper, we describe novel VR-derived measures of cognitive performance and show their correspondence with clinically-validated cognitive performance measures. We use an immersive VR environment called VStore where participants complete a simulated supermarket shopping task. People with psychosis (k=26) and non-patient controls (k=128) participated in the study, spanning ages 20-79 years. The individuals were split into two cohorts, a homogeneous non-patient cohort (k=99 non-patient participants) and a heterogeneous cohort (k=26 patients, k=29 non-patient participants). Participants' spatio-temporal behaviour in VStore is used to extract four features, namely, route optimality score, proportional distance score, execution error score, and hesitation score using the Traveling Salesman Problem and explore-exploit decision mathematics. These extracted features are mapped to seven validated cognitive performance scores, via linear regression models. The most statistically important feature is found to be the hesitation score. When combined with the remaining extracted features, the multiple linear regression model resulted in statistically significant results with R2 = 0.369, F-Stat = 7.158, p(F-Stat) = 0.000128.
Parkinson's Disease (PD) is currently the fastest growing neurodegenerative disease. It decreases the quality of life for patients, especially when not diagnosed properly and timely. Accurate ...diagnostic of PD is complicated by the fact that there exist several neurodegenerative diseases with similar motor symptoms, e.g. essential tremor. In this work, we report on a second opinion system based on the video analysis and classification of subjects using machine learning methods including feature extraction, dimensionality reduction and classification. Our approach serves for avoiding a typical misdiagnosis of PD by essential tremor. Consequently, we designed 15 common tasks and recorded the movement video. Video data was collected from 89 subjects at a medical center and labeled by doctors. We first demonstrate classification between the healthy subjects and subjects with PD suspected case followed by the classification between the subjects with true PD and the subjects with essential tremor. We achieved f1 score 0.90 for the first classification and f1 score 0.84 for the second classification. The proposed unobtrusive approach demonstrated its feasibility through a pilot study. It opens up wide vista for differentiating PD patients against other patients and not against a cohort of healthy subjects.
This articleproposes an intelligent algorithm for wind turbine faults identification based on the image model of the dynamic process and the deep convolutional neural network. First, feature ...engineering is designed to generate the image model of a dynamic process. We performed variable refinement and data normalization preprocessing on the dataset. Then the time series data are reconstructed in the Gram angle field to form an image model. Next, an identification algorithm adapted to the image model, called multistream self-fusion net (MuSnet), is proposed. Inside the MuSnet, we use the multistream self-fusion module to replace the original convolution operation in the low-level convolution. This allows the features of heterogeneous information in the image model to be better extracted. Multiple evaluation metrics in the experiment show that the proposed method has advantages of high recognition accuracy, ability to train with fewer samples, speeding up the convergence of learning, and high robustness for wind turbine fault identification.
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