Analysis-based Methods Research Articles

Driving Fatigue Detection Based on Hybrid Electroencephalography and Eye Tracking.

EEG-based unimodal method has demonstrated significant success in the detection of driving fatigue. Nonetheless, data from a single modality might be not sufficient to optimize fatigue detection due to incomplete information. To address this limitation and enhance the performance of driving fatigue detection, a novel multimodal architecture combining hybrid electroencephalograph (EEG) and eye tracking data was proposed in this work. Specifically, the EEG and eye tracking data were separately input into encoders, generating two one-dimensional (1D) features. Subsequently, these 1D features were fed into a cross-modal predictive alignment module to improve fusion efficiency and two 1D attention modules to enhance feature representation. Furthermore, the fused features were recognized by a linear classifier. To evaluate the effectiveness of the proposed multimodal method, comprehensive validation tasks were conducted, including intra-session, cross-session, and cross-subject evaluations. In the intra-session task, the proposed architecture achieves an exceptional average accuracy of 99.93%. Moreover, in the cross-session task, our method demonstrates an average accuracy of 88.67%, surpassing the performance of EEG-only approach by 8.52%, eye tracking-only method by 5.92%, multimodal deep canonical correlation analysis (DCCA) technique by 0.42%, and multimodal deep generalized canonical correlation analysis (DGCCA) approach by 0.84%. Similarly, in the cross-subject task, the proposed approach achieves an average accuracy of 78.19%, outperforming EEG-only method by 5.87%, eye tracking-only approach by 4.21%, DCCA method by 0.55%, and DGCCA approach by 0.44%. The experimental results conclusively illustrate the superior effectiveness of the proposed method compared to both single modality approaches and canonical correlation analysis-based multimodal methods.

Technical analysis of basketball players' shooting movements through video images

BackgroundWith the increase in basketball competition intensity, video image analysis-based methods have been increasingly applied to improve the technical level of basketball players. MethodsTaking the example of one-handed set shoulder shooting (the basketball player spreads legs apart at shoulder width, holds the ball over the right shoulder, extends the right hand along with lower body movement, and releases the ball softly using fingers), this study compared the technical differences between 20 level 2 basketball players (level 2 group) and 20 basketball specialized students (specialized group) by capturing video images using a high-speed camera and conducting independent sample t-tests. ResultsThe level 2 group had a significantly higher shooting percentage than the specialized group (p=0.005). Additionally, the level 2 group had shorter dribbling time and longer shooting time compared to the specialized group (p=0.032; p=0.021). The amplitude of variation in the knee, hip, and elbow joints was significantly greater than that of the specialized group (p=0.008/0.016/0.031). The flexion angles of the index finger and middle finger after striking in the level 2 group were significantly higher than those of the specialized group (p=0.021/0.036). The level 2 group also had a smaller striking angle and a larger striking height and speed than the specialized group (p=0.001; p=0.005/0.006). ConclusionAthletes of different levels show differences in phase duration and joint angles when performing one-handed set shoulder shooting, providing a basis for athletes' training.

A periodic-modulation-oriented noise resistant correlation method for industrial fault diagnostics of rotating machinery under the circumstances of limited system signal availability

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method.

A fault isolation strategy for industrial processes using outlier-degree-based variable contributions

In industrial process monitoring, it is always a challenging and practical problem to analyze the causes of the system fault by isolating true fault variables from vast amounts of process data. However, the phenomenon of smearing effect occurs by using the traditional contribution analysis-based isolation methods since the defined isolation indices of different variables affect each other. In this paper, a new fault isolation method is proposed based on local outlier factor and improved k-nearest neighbor rule aiming to improve the isolation accuracy. Firstly, the nearest neighbors of each sample are obtained along the direction of a specific variable. Based on the nearest neighbors, the outlier-degree value of the variable is calculated and regarded as the contribution of the variable. Then, the contribution of the variable in all samples are obtained in the same way, among which the maximum one is selected as the isolation threshold value of this variable. During the online monitoring, the contribution of the variable in the newly collected sample is calculated in real time. Once the contribution is greater than the threshold, the variable is judged to be the dominant factor causing the system fault. Two cases on numerical example and Tennessee Eastman process are conducted to evaluate the effectiveness of the proposed method.

Quantification of Microglial Engulfment of Synaptic Material Using Flow Cytometry.

Microglia play a pivotal role in synaptic refinement in the brain. Analysis of microglial engulfment of synapses is essential for comprehending this process; however, currently available methods for identifying microglial engulfment of synapses, such as immunohistochemistry (IHC) and imaging, are laborious and time-intensive. To address this challenge, herein we present in vitro and in vivo* assays that allow fast and high-throughput quantification of microglial engulfment of synapses using flow cytometry. In the in vivo* approach, we performed intracellular vGLUT1 staining following fresh cell isolation from adult mouse brains to quantify engulfment of vGLUT1+ synapses by microglia. In the in vitro synaptosome engulfment assay, we used freshly isolated cells from the adult mouse brain to quantify the engulfment of pHrodoRed-labeled synaptosomes by microglia. These protocols together provide a time-efficient approach to quantifying microglial engulfment of synapses and represent promising alternatives to labor-intensive image analysis-based methods. By streamlining the analysis, these assays can contribute to a better understanding of the role of microglia in synaptic refinement in different disease models.

Joint multi-omics discriminant analysis with consistent representation learning using PANDA.

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.

The classification of SSVEP-BCI based on ear-EEG via RandOm Convolutional KErnel Transform with Morlet wavelet

During the advantages of shorter training and higher information throughput, steady-state visual evoked potential (SSVEP) is widely used in brain–computer interface (BCI) research. Recently, collecting EEG signals from the ear area (ear-EEG) has gained increasing attention because it is more comfortable and convenient than scalp electrodes. The ear-EEG-based BCI system based on ear electrodes has weaker signals and more noise components because the electrodes are located far away from the top of the head. In this study, the RandOm Convolutional KErnel Transform (ROCKET) algorithm integrated with the Morlet wavelet transform (Morlet-ROCKET) was proposed to solve this issue. This study compared the performence of Morlet-ROCKET with two established methods: canonical correlation analysis-based (FBCCA) and Transformer methods. The proposed Morlet-ROCKET model demonstrated superior performance across multiple measures, including increased classification accuracy in 1 s, 3 s, and 4 s time windows and higher area under the curve (AUC) values in receiver operating characteristic (ROC) analysis. The analysis result proved that with efficient data processing algorithms, ear-EEG-based BCI systems can also have good performance, and providing support for the popularization of BCI.

Deep learning-based control framework for dynamic contact processes in humanoid grasping.

Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible grasping points on both the contact surface of the object and the table surface. To mimic human-like grasping movements, an underactuated anthropomorphic hand is utilized, which is designed based on human hand data. The utilization of hand gestures, rather than controlling each motor separately, has significantly decreased the control dimensionality. Additionally, a deep learning framework is used to select gestures and grasp actions. Our methodology, proven both in simulation and on real robot, exceeds the performance of static analysis-based methods, as measured by the standard grasp metric Q1. It expands the range of objects the system can handle, effectively grasping thin items such as cards on tables, a task beyond the capabilities of previous methodologies.

Study on Condition Monitoring of Pitch Bearings Based on Stress Measurement

Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. To address this issue, a stress-based pitch bearing monitoring method is proposed in this paper. First, finite element analysis is conducted to establish the relationship between the maximum surface stress on the outer race of the pitch bearing and the presence of cracks. This relationship allows the identification of cracks on the outer race and an assessment of their severity based on the value of the maximum surface stress. Second, the outer race of the pitch bearing is divided into several segments, and a singularity detection technique is employed to locate the position of cracks on the outer race based on the stresses measured from the segments. To verify the proposed method, a wind turbine pitch bearing test rig was developed in a laboratory. Experimental results have shown that the proposed method can effectively and accurately identify and locate cracks on the outer race of the bearing, thereby demonstrating its great potential as a reliable approach for monitoring the condition of wind turbine pitch bearings.

CrossFuzz: Cross-contract fuzzing for smart contract vulnerability detection

Context:Smart contracts are computer programs that run on a blockchain. As the functions implemented by smart contracts become increasingly complex, the number of cross-contract interactions within them also rises. Consequently, the combinatorial explosion of transaction sequences poses a significant challenge for smart contract security vulnerability detection. Existing static analysis-based methods for detecting cross-contract vulnerabilities suffer from high false-positive rates and cannot generate test cases, while fuzz testing-based methods exhibit low code coverage and may not accurately detect security vulnerabilities. Objective:The goal of this paper is to address the above limitations and efficiently detect cross-contract vulnerabilities. To achieve this goal, we present CrossFuzz, a fuzz testing-based method for detecting cross-contract vulnerabilities. Method:First, CrossFuzz generates parameters of constructors by tracing data propagation paths. Then, it collects inter-contract data flow information. Finally, CrossFuzz optimizes mutation strategies for transaction sequences based on inter-contract data flow information to improve the performance of fuzz testing. Results:We implemented CrossFuzz, which is an extension of ConFuzzius, and conducted experiments on a real-world dataset containing 396 smart contracts. The results show that CrossFuzz outperforms xFuzz, a fuzz testing-based tool optimized for cross-contract vulnerability detection, with a 10.58% increase in bytecode coverage. Furthermore, CrossFuzz detects 1.82 times more security vulnerabilities than ConFuzzius. Conclusion:Our method utilizes data flow information to optimize mutation strategies. It significantly improves the efficiency of fuzz testing for detecting cross-contract vulnerabilities.

A fault localization approach based on multi-system PCA and dynamic SDG: Application in train lifting equipment

The development of the current rail transportation is increasingly rapid, and train maintenance is closely related to the high-efficiency running of rail transportation. Train lifting equipment (TLE) is important for train maintenance, for which stable operation is crucial. TLE fault is characterized by early abnormally weak and capable of propagation. Early anomaly detection and fault diagnosis are the keys to TLE fault maintenance, and this paper proposes a TLE fault localization methodology to address these two problems. For early anomaly detection, a combination of sequence-based and principal component analysis-based methods is utilized to address the problem. For fault diagnosis, the signed directed graph-based method is utilized to ensure the completeness of reasoning results, and the system division mechanism and dynamic division mechanism are utilized to narrow down the scope of faults and accelerate the reasoning process, respectively. The experiments prove that the proposed method can reduce the loss caused by equipment faults and improve the reliability and stability of the equipment.

Assessing pore network heterogeneity across multiple scales to inform CO2 injection models

Geologic heterogeneity is a key feature that must be considered when translations of scaled data are performed. This paper presents the assessment of geologic heterogeneity using a multiscale workflow that includes image analysis-based methods coupled with well log analysis to provide data in which fractals and machine learning methods estimate the carbon dioxide (CO2) storage resource potential of a reservoir. The heterogeneity of rock properties of the complex Bell Creek reservoir in Montana, USA, was explored at the pore scale (∼nm to mm), core scale (∼mm to m), and well scale (∼cm to m). The data used in this study included advanced image analysis of micro-CT (computed tomography) images (pore scale), thin sections (pore scale), plugs and core images (core scale) and well logs (well scale). The micro-CT images were segmented using a U-net segmentation approach into objects of pores and grains. The segmented images were reconstructed into subvolumes of different sizes. Physical properties (porosity and permeability) and fractal dimensions were calculated for the various subvolumes, and Lorenz coefficient (Lc) values, a single parameter to describe the degree of heterogeneity within a pay zone section, were calculated from thin-section images and well logs. Porosity and fractal dimension values were used to estimate the 188-µm threshold of representative elementary volume (REV) in this study. Both the Lc and fractal dimension values were found to be negatively correlated. When these two parameters are combined, it is possible to discern differences in the complex porous networks of the samples analyzed in this study.

ConPep: Prediction of peptide contact maps with pre-trained biological language model and multi-view feature extracting strategy

The accurate prediction of peptide contact maps remains a challenging task due to the difficulty in obtaining the interactive information between residues on short sequences. To address this challenge, we propose ConPep, a deep learning framework designed for predicting the contact map of peptides based on sequences only. To sufficiently incorporate the sequential semantic information between residues in peptide sequences, we use a pre-trained biological language model and transfer prior knowledge from large scale databases. Additionally, to extract and integrate sequential local information and residue-based global correlations, our model incorporates Bidirectional Gated Recurrent Unit and attention mechanisms. They can obtain multi-view features and thus enhance the accuracy and robustness of our prediction. Comparative results on independent tests demonstrate that our proposed method significantly outperforms state-of-the-art methods even with short peptides. Notably, our method exhibits superior performance at the sequence level, suggesting the robust ability of our model compared with the multiple sequence alignment (MSA) analysis-based methods. We expect it can be meaningful research for facilitating the wide use of our method.

Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic

Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used.

Assessment of Heavy Metals Contamination and Potential Ecological Risk in Surface Sediments of the El Bey Wadi, Tunisia

ABSTRACT Sebkhat Soliman included in the Ramsar Convention is a wetland of international importance. This area is of great ecological interest considering the quality and the richness of the flora and the diversity of the avifauna. This site receives a large amount of water through the EL Bey River. In order to assess the levels of contamination, concentrations of six heavy metals, including Cr, Cu, Pb, Ni, Mn, and Zn, were examined in 25 surface sediment samples from El Bey Wadi. These concentrations, which were expressed in mg/kg of dry weight, showed that Pb ranged from 5.52 to 41.66, Zn from 26.94 to 1471.89, Cu from 91.97 to 260.05, Ni from 9.35 to 140.45, Cr from 25. It’s important to note that the highest metal concentrations have been found in site 5 which is frequently exposed to local industrial pollutant sources and in sites that are frequently inundated by river water that is carrying away industrial waste from the cities of Grombalia and Bou-Argoub. The mean EF values for Cu, Mn and Ni were between 3 and 5 in the sediments of the El Bey River, suggesting moderate enrichment, while the mean EF values for Pb, Zn and Cr indicating respectively moderate to severe enrichment and very severe enrichment. CF values for Cr, Cu and Zn were > 6 in sediments taken from the fifth site, which denotes a very high contamination by these metals. At site 5, PLI value (4.09), reflecting that its sediment is heavily polluted. Hierarchical cluster analysis-based complementary statistical methods have demonstrated that Mn is lithogenic and other analyzed metals are anthropogenic.

Automated outlier detection and estimation of missing data

The majority of algorithms used for data imputation are based on latent variable methods. The presence of outliers in process data, however, misleads the latent relations among variables, resulting in an inaccurate estimation of missing values. This article proposes an approach for automatically detecting outliers using T2 and Q contributions and estimating missing data using various general-purpose algorithms while reducing the impact of outliers. The software is validated using biomanufacturing data from the production of a monoclonal antibody produced by Chinese hamster ovary cells in a perfusion bioreactor for five missingness cases including missing completely at random, sensor drop-out, multi-rate, patterned, and censoring. Based on the normalized root mean squared error and the three proposed metrics corresponding to feasibility, plausibility, and rapidity, respectively, matrix completion methods are the most effective, except for the censoring case in which probabilistic principal component analysis-based methods are the most effective.

Bayesian inference of pit corrosion in prestressing strands using Markov Chain Monte Carlo method

Corrosion monitoring has been widely studied to maintain the structural capacity of bridges through direct visual and nondestructive inspections or indirect inverse analysis-based methods. This study proposes a Bayesian inference method for inferring pit corrosion in the prestressing strands of prestressed concrete (PSC) bridges, which is an indirect method for corrosion monitoring. First, the probabilistic relationship between the mechanical properties of the strands and the amount of pit corrosion was defined using Bayes’ rule. Subsequently, a Markov chain Monte Carlo method was introduced to infer the posterior probability, which is a conditional probability distribution of the amount of corrosion given a certain mechanical property. Based on the inference results, probabilistic bounds for the amount of corrosion were derived. The proposed method was applied to two examples: (a) probabilistic corrosion inference of strands based on the tensile test results, and (b) probabilistic corrosion inference of embedded strands in PSC girders based on the bending test results. The inference results demonstrated the applicability of the proposed method.

A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles

The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.

Modeling Variation in Multi-Station Compliant Assembly Using Parametric Space Envelope

Abstract Compliant parts have different characteristics from rigid parts and are used frequently in industries. One of the biggest challenges facing by industries is geometric variation management of these compliant parts which can directly impact product quality and functionality. Existing rigid body-based variation modeling approaches are not suitable for compliant assembly while finite element analysis-based methods have the disadvantages of requiring heavy computation efforts and detailed design information which is unavailable during preliminary design phase. Hence, this paper proposes a novel geometric variation propagation model of multi-station compliant assembly based on parametric space envelope. Three sources of variation: location-led positional variation, assembly deformation-induced variation, and station transition caused variation are analyzed. In this study, geometric variations are modeled indirectly through control points of constructed variation tool. Compared with existing methods where geometric variation is modeled by tracking key feature points on the manufacturing part, the proposed approach brings unique benefits. It can deal with arbitrary complex compliant part, and it provides a unified modeling framework for different types of variation. The method is illustrated and verified through a two-station three parts case study on a multi-station compliant panel assembly. The proposed method provides industries with a new way to manage geometric variation from compliant assembly.

An Android Malware Detection Method Using Multi-Feature and MobileNet

Most of the existing static analysis-based detection methods adopt one or few types of typical static features for avoiding the problem of dimensionality and computational resource consumption. In order to further improve detecting accuracy with reasonable resource consumption, in this paper, a new Android malware detection model based on multiple features with feature selection method and feature vectorization method are proposed. Feature selection method for each type of features reduces the dimensionality of feature set. Weight-based feature vectorization method for API calls, intent and permission is designed to construct feature vector. Co-occurrence matrix-based vectorization method is proposed to vectorize opcode sequence. To demonstrate the effectiveness of our method, we conducted comprehensive experiments with a total of 30,000 samples. Experimental results show that our method outperforms state-of-the-art methods.