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Research on social psychology has revealed the existence of an affective mechanism in a human group, which is the group members spread their emotions to one another, the emotions of the group members form the group emotion, and the group emotion as a powerful force shapes the group members’ emotions. Current group emotion recognition methods focus on how the emotions of the group members form the group-level emotion but rarely take into account how the group emotion feeds back to the group members instantaneously. This paper proposes a new graph convolutional network architecture to characterize this unique affective mechanism for group emotion recognition. We regard the group members as the nodes of the graph and introduce a pseudo node into the graph to represent the role of the group. This paper uses graph convolutional networks to model the emotional interactions within the group from a static image and constructs an effective emotional representation at the group level for recognition. Experiment results on three widely used datasets for group emotion recognition show that our proposed method achieved superior performance in terms of recognition accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The implementation of Deep Learning (DL) Prediction techniques for Human Age Prediction (HAP) has been widely researched and studied to prevent, treat, and extend life expectancy. While most algorithms rely on facial images, MRI scans, and DNA methylation for training and testing, they are seldom implemented due to a lack of significant validation and evaluation in real-world scenarios, low performance, and technical challenges. To address these issues, this paper proposes the Data, Classification Technique, Prediction, and View (DCPV) taxonomy, which outlines the primary components required to implement and validate a deep learning model for predicting human age. By providing a common baseline for end-users and researchers, this taxonomy offers a clearer view of the constituents of deep learning prediction approaches, enabling the development of similar systems in the health domain. In contrast to existing machine learning methods, the proposed taxonomy emphasizes the value of deep learning practices based on performance, accuracy, and efficiency in predicting human age. To validate the DCPV taxonomy, the study examines 31 state-of-the-art research journal articles within the HAP system domain, assessing the taxonomy's performance, accuracy, robustness, and model comparisons. [ABSTRACT FROM AUTHOR]

Head pose estimation is an especially challenging task due to the complexity nonlinear mapping from 2D feature space to 3D pose space. To address the above issue, this paper presents a novel and efficient head pose estimation framework based on particle swarm optimized contrastive learning and multimodal entangled graph convolution network. Firstly, a new network, the region and difference‐aware feature pyramid network (RD‐FPN), is proposed for 2D keypoints detection to alleviate the background interference and enhance the feature expressiveness. Then, particle swarm optimized contrastive learning is constructed to alternatively match 2D and 3D keypoints, which takes the multimodal keypoints matching accuracy as the optimization objective, while considering the similarity of cross‐modal positive and negative sample pairs from contrastive learning as a local contrastive constraint. Finally, multimodal entangled graph convolution network is designed to enhance the ability of establishing geometric relationships between keypoints and head pose angles based on second‐order bilinear attention, in which point‐edge attention is introduced to improve the representation of geometric features between multimodal keypoints. Compared with other methods, the average error of our method is reduced by 8.23%, indicating the accuracy, generalization, and efficiency of our method on the 300W‐LP, AFLW2000, BIWI datasets. [ABSTRACT FROM AUTHOR]

Mechanomyography (MMG) is a low-frequency signal emitted during muscle contraction; it can overcome the inherently unreliable defects of electromyography (EMG) and electroencephalography (EEG). For MMG-based movement pattern recognition, this paper proposes an innovative kernel extreme learning machine (KELM) based on the chaotic particle swarm optimization (CPSO), namely CPSO–KELM. By using CPSO–KELM in MMG-based movement pattern recognition, the classification accuracy of upper-limb movement has been improved, and the results can be better applied to the control of passive rehabilitation training of the upper-limb exoskeleton, which can provide help for the upper extremity rehabilitation of stroke patients. In this paper, MMG which is used for pattern recognition research, is collected by accelerometers when the subjects performed seven types of upper-limb rehabilitation movements. After filtering and segmentation, six time-domain features are extracted for the MMG of each channel, then kernel principal component analysis (KPCA) and principal component analysis (PCA) are used to reduce the feature dimensions. By using different classifiers to build classification models, the average recognition accuracies of movement classification under different processing methods are obtained; it is found that for most classifiers, the recognition rate of MMG after KPCA dimensionality reduction is better than that of PCA, and the overall recognition rate of upper-limb movements using the CPSO–KELM classifier can reach 97.1%, which is better than support vector machine (SVM), back-propagation neural network (BPNN), linear discriminant algorithm (LDA) and other MMG common classifiers in recognition accuracy. Moreover, the experimental analysis shows that compared with genetic algorithm (GA) and particle swarm optimization (PSO), CPSO has faster convergence and smaller training error, and the final recognition accuracy proves that the performance of CPSO–KELM is better than those of GA–KELM and PSO–KELM. [ABSTRACT FROM AUTHOR]

Identity recognition technology is a type of technology that realizes identity verification based on certain biological characteristics. After entering the Internet era, this technology has become a popular research direction in the computer field. In this paper, the image of the tooth print is used as the biological feature to carry out the research on the identification algorithm. This paper adopts the target detection algorithm based on neural network to detect a single tooth imprint area of the target, build a target detection network. The experimental results show that the method has a good segmentation effect on the target area, and the accuracy rate is 91.66%. According to the contour features of the collected tooth print images, a set of tooth pore area ratio feature extraction methods are designed. To objectively evaluate the recognition and classification method, the support vector machine is used as the final classifier. The recognition accuracy rate is 94.09%, and the verification accuracy rate is 94.09%. The test accuracy rate is 91.46%, and the classification effect is excellent. This paper has made a lot of breakthroughs and obvious progress based on the previous research on the tooth impression model. [ABSTRACT FROM AUTHOR]

The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security. [ABSTRACT FROM AUTHOR]

Although low‐rank representation (LRR)‐based subspace learning has been widely applied for feature extraction in computer vision, how to enhance the discriminability of the low‐dimensional features extracted by LRR based subspace learning methods is still a problem that needs to be further investigated. Therefore, this paper proposes a novel low‐rank preserving embedding regression (LRPER) method by integrating LRR, linear regression, and projection learning into a unified framework. In LRPER, LRR can reveal the underlying structure information to strengthen the robustness of projection learning. The robust metric L2,1‐norm is employed to measure the low‐rank reconstruction error and regression loss for moulding the noise and occlusions. An embedding regression is proposed to make full use of the prior information for improving the discriminability of the learned projection. In addition, an alternative iteration algorithm is designed to optimise the proposed model, and the computational complexity of the optimisation algorithm is briefly analysed. The convergence of the optimisation algorithm is theoretically and numerically studied. At last, extensive experiments on four types of image datasets are carried out to demonstrate the effectiveness of LRPER, and the experimental results demonstrate that LRPER performs better than some state‐of‐the‐art feature extraction methods. [ABSTRACT FROM AUTHOR]

3D object recognition and pattern recognition are active and fast-growing research areas in the field of computer vision. It is mandatory to define the pattern class, feature extraction, design classifiers, clustering, and selection of test datasets and evaluate performance for any pattern recognition system. The pattern recognition system recognizes the object, so it is required to extract the features in such a way that it will be suitable for a particular recognition method. Features can be retrieved either locally or globally. The object recognition technique is divided into two parts: the local feature extraction method and the global feature extraction method. Many researchers have done admirable work in the field of local and global feature extraction. Local feature-based techniques are more suitable for the real-world scene. The Global feature-based methods are more suitable for retrieving the 3D model & identifying the object's shape when the object's geometric structure is fragile. A lot of research has been done on pattern recognition in the last 50 years. Still, no single technique can be used for all types of applications, such as bioinformatics, data mining, speech recognition, remote sensing, multimedia applications, text detection, localization, etc. The main agenda of this paper is to summarize the 3D object recognition methodologies. This paper provides a complete study of 3D object recognition based on local and global feature-based methods and different techniques of pattern recognition. We have tried to summarize the results of different technologies and the future scope of this paper's particular technique. We enlisted the accessible online 3D database and their attributes, evaluation parameters of the 3D datasets. This paper will immensely help the researchers to Identify the research gap and limitations in pattern recognition and object recognition so that the researchers will be motivated to do something new in this field. [ABSTRACT FROM AUTHOR]

In the process of reconstruction and expansion of gas pipeline, it is easy to destroy in-service gas pipeline and cause safety accidents. In order to realize the detection of in-service pipelines, based on the characteristics of low sound pressure level and easy attenuation of acoustic signals of gas pipelines, the detection and identification method of gas pipelines based on acoustic signal feature analysis was studied by using Hilebert–Huang transform algorithm and optimized Back Propagation (BP) neural network. This method takes the gas pipeline flow noise signals obtained by numerical simulation and experimental verification as the research object, and the underwater acoustic signals are collected for comparative analysis. Empirical Mode Decomposition (EMD) was used to decompose the two signals, and the time-domain waveform of Intrinsic Mode Functions (IMF) component was obtained, and the characteristic parameters of peak value and peak frequency were determined. The energy characteristic parameters of Hilbert marginal spectrum were calculated, and the characteristic database of gas pipeline flow noise signal was obtained. The optimized BP neural network was used for pattern recognition. The results show that the identification rate of gas pipeline acoustic signal can reach 97.5% by using this method, which verifies the effectiveness of the gas pipeline detection and identification method in this paper. [ABSTRACT FROM AUTHOR]

High-speed train is a large-scale electromechanical coupling equipment with a complex structure, where the coupling is interlaced between various system components, and the excitation sources are complex and diverse. Therefore, reliability has become the top priority for the safe operation of high-speed trains. As the operating mileage of high-speed trains increases, various key systems experience various degrees of performance degradation and damage failures. Moreover, it is accompanied by the influence of external environmental high interference noise and weak early fault information. Thus, those factors are serious challenges for the condition monitoring and fault diagnosis of high-speed trains. Therefore, this paper summarizes the research progress and theoretical results of the fault detection, fault isolation, and fault diagnosis methods of the key systems of high-speed trains. Finally, the paper summarizes the applicability of the main methods, discusses the challenges and opportunities of condition monitoring and fault diagnosis of high-speed trains, and looks forward to improving its diagnosis level. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification. OBJECTIVES: In Computer Vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation. CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and image processing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process. METHODS, RESULTS, AND CONCLUSION: In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of image processing and the results based on the impact; neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

This paper presents an approach for identifying fingerprints through the extraction of geometric and statistical features of characteristic Minutiae. The proposed approach is in accordance with statistical features to extract important points from the skeleton of a fingerprint's image. Through the addition of geometric features as a kind of preprocessing to this approach, the images are divided into distinct regions. In this approach, statistical parameters like min, max, mean and standard deviation are applied in order to compute the general abstract of the features. Another achievement in this article is the presentation of a similarity measure in identification tools which is only used for methods based on matching patterns. The Optimized version of the proposed method achieved near zero EER percentage in some of the datasets. [ABSTRACT FROM AUTHOR]

The design of fusion engines is a subject of great importance in a variety of fields. In this paper, we focus on the problem of linear fusion at the feature level for multiple signal matrices with noises, with the features being extremal eigenvectors. When given multiple similarity matrices, the objective is to find an estimate of the latent signal eigenspace. The concentration result for the inner product of features from different matrix samples is developed, utilizing the random matrix theory. Based on of the theoretical results, we proposed an efficient algorithm, EigFuse, to solve the constrained data-driven optimization problem with different level of noises. Our method is of high efficiency by comparing it with state-of-the-art baseline approaches with multiple noise levels. Comprehensive experiments on several synthetic as well as real-life networks demonstrate our method’s superior performance. [ABSTRACT FROM AUTHOR]

Spatial failure patterns in wafer defect maps are very useful for root cause analysis, which is essential for yield optimization in the manufacturing process. Especially, the scratch defect type is the most challenging pattern to recognize because the position, shape, size and curvature vary widely from one scratch to another. Discontinuity points within scratches also contribute to the low recognition rate, and such points are often hidden defective dies that become reliability threat. Previous studies on defect pattern identification show that the recognition rate for scratches is among the lowest in all patterns even if the overall accuracy is high. In this paper, we propose a novel scratch pattern recognition method. In this paper, image processing techniques are applied to recognize scratch patterns in wafer maps. The Hough transform is first employed to identify line segments, from which scratches can be reconstructed. In contrast to previous machine-learning based methods, there is no need to train a complicated prediction model so that the computation time is small. The method is validated by using wafer maps from six real products and public data. Experimental results show that the proposed method achieves high recall, precision and accuracy in all cases. [ABSTRACT FROM AUTHOR]

The study on pattern recognition (PR) is one of the fundamental steps for prosthetic hand control to improve the life quality of amputees. However, achieving a proper and functional PR has challenges due to the limitations of bio-signals. To this end, this paper proposes a novel hybrid approach to PR of finger movements and grasping gestures simultaneously based on mechanomyography (MMG) and force-myography (FMG) signals. In this method, first, time-domain features, time-frequency-domain features, and acoustic features are extracted from signal obtained by novel hybrid MMG-FMG sensor; second, analysis of variance (ANOVA) is used to select the best features separately, and then sequential forward feature selection (SFFS) is used to select the optimal combination of the selected features by ANOVA; and finally, the selected features are classified by neural network (ANN). To evaluate the performance of the multimode PR method by hybrid MMG-FMG signals for the accurate simultaneous classification of finger motions and grasping gestures, twelve motion patterns, including seven finger motions and five grasping gestures, from twelve participants including six transracial amputees and six healthy, are tested in the experiments. The real-time experimental results show that the hybrid MMG-FMG signals lead to high resolution, with an average classification accuracy of 91.44% ±0.77 for amputees and 92.19% ±1.12 for healthies. The results illustrate that the proposed approach reduces the difference accuracy between healthy subjects and amputees in classification. The outcomes of this study have great potential to perform onto amputees with different muscle conditions. [ABSTRACT FROM AUTHOR]

Wafer bin maps contain vital information that helps semiconductor manufacturers to identify the root causes and defect pattern failures in wafers. Conventional manual inspection techniques in inspecting these failures are labour intensive and cause prolonged production cycle time. Therefore, automatic inspection techniques can solve this problem. This paper proposes a deep learning approach based on deep convolutional generative adversarial network (DCGAN) and a new Capsule Network (WaferCaps). DCGAN was used to upsample the original dataset and therefore increase the data used for training and balance the classes at the same time. While WaferCaps was proposed to classify the defect patterns according to eight classes. The performance of our proposed DCGAN and WaferCaps was compared with different deep learning models such as the original Capsule Network (CapsNet), CNN, and MLP. In all of our experiment, WM-811K dataset was used for the data upsampling and training. The proposed approach has shown an effective performance in generating new synthetic data and classify them with training accuracy of 99.59%, validation accuracy of 97.53% and test accuracy of 91.4%. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements. [ABSTRACT FROM AUTHOR]

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Representation of wall facade in 3D scene is important for scene understanding and reconstruction. Doors and windows are essential elements of wall facade, which enrich the models of indoor buildings. In order to reconstruct the wall facade with detailed wall opening (doors and windows) information from point cloud, an improved automatic reconstruction approach is proposed in this paper. Specifically, we first detect the occlusion area on the wall by ray-tracing method. Then, the interested wall façade is segmented from scenes, and the contour and feature lines of the wall surface are extracted through the analysis of spatial occupancy. Third, the wall point cloud is transformed into a 2D cell complex. The wall surface is finally reconstructed through the wall opening identification based on graph cut. Experimental results demonstrate that our method has better performance in detail description, feature line extraction and opening area detection of the scene point cloud than existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability. [ABSTRACT FROM AUTHOR]

Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry. [ABSTRACT FROM AUTHOR]

Pattern recognition of linear feature sets, such as river networks, road networks, and contour clusters, is essential in cartography and geographic information science. Previous studies have investigated many methods to identify the patterns of linear feature sets; the key to each of these studies is to generate a reasonable and computable representation for each set. However, most existing methods are only designed for a specific task or data type and cannot provide a general solution for formalizing linear feature sets owing to their complex geometric characteristics, spatial relations and distributions. In addition, some methods require human involvement to specify characteristics, choose parameters, and determine the weights of different measures. To reduce human intervention and improve adaptability to various feature types, this paper proposes a novel deep learning architecture for learning the representations of linear feature sets. The presented model accepts vector data directly without extra data conversion and feature extraction. After generating local, neighborhood, and global representations of inputs, the representations are aggregated accordingly to perform pattern recognition tasks, including classification and segmentation. In the experiments, building groups classification and road interchanges segmentation achieved accuracies of 98% and 89%, respectively, indicating the model's effectiveness and adaptability. [ABSTRACT FROM AUTHOR]

Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique part of eye which can never be altered, and it remains the same throughout the life of an individual. We have proposed the improved iris recognition system including image registration as a main step as well as the edge detection method for feature extraction. The PCA-based method is also proposed as an independent iris recognition method based on a similarity score. Experiments conducted using our own developed database demonstrate that the first proposed system reduced the computation time to 6.56 sec, and it improved the accuracy to 99.73, while the PCA-based method has less accuracy than this system does. [ABSTRACT FROM AUTHOR]

In recent times, pattern recognition of communication modulation signals has gained significant attention in several application areas such as military, civilian field, etc. It becomes essential to design a safe and robust feature extraction (FE) approach to efficiently identify the various signal modulation types in a complex platform. Several works have derived new techniques to extract the feature parameters namely instant features, fractal features, and so on. In addition, machine learning (ML) and deep learning (DL) approaches can be commonly employed for modulation signal classification. In this view, this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks (CSM-FFDNN). The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals. The proposed CSM-FFDNN model involves two major processes namely FE and classification. The proposed model uses Sevcik Fractal Dimension (SFD) technique to extract the fractal features from the digital modulated signals. Besides, the extracted features are fed into the DNN model for modulation signal classification. To improve the classification performance of the DNN model, a barnacles mating optimizer (BMO) is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised. A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model. The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters. [ABSTRACT FROM AUTHOR]

The aim of this paper is to clear up the problem of the connection between the 3D geometric moments invariants and the invariant theory, considering a problem of describing of the 3D geometric moments invariants as a problem of the classical invariant theory. Using the remarkable fact that the complex groups SO(3,C) and SL(2,C) are locally isomorphic, we reduced the problem of deriving 3D geometric moments invariants to the well-known problem of the classical invariant theory. We give a precise statement of the 3D geometric invariant moments computation, introducing the notions of the algebras of simultaneous 3D geometric moment invariants, and prove that they are isomorphic to the algebras of joint SL(2,C)-invariants of several binary forms. To simplify the calculating of the invariants we proceed from an action of Lie group SO(3,C) to equivalent action of the complex Lie algebra sl2. The author hopes that the results will be useful to the researchers in the fields of image analysis and pattern recognition. [ABSTRACT FROM AUTHOR]

Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are obtained for testing by experiments and simulation. Compared with 1-D CNN and 2-D CNN under the same specification, the proposed method can mine more potential local features of discharge pulses by fusing the time-frequency features of PD pulses in different dimensions, and improves the recognition accuracy to 95.8%. [ABSTRACT FROM AUTHOR]

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen's Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices. [ABSTRACT FROM AUTHOR]

Driving cycles have a great influence on vehicles’ fuel economy, control performance and drivability. In this paper, vehicle speed is considered and twelve statistical features for driving pattern recognition are selected to obtain the characteristics of driving cycles. To extract the statistical features online from the speed distribution information, the sampling and updating windows are set and the number of features is reduced. Moreover, since the structure and parameters of neural network are crucial to the classifier, the structure and parameters of neural network, the sampling and updating window size, the feature subset selection are simultaneously optimized by genetic algorithm (GA) to improve the classifying accuracy and simplify neural network structure. The hybrid encoding/decoding and the structure operator are designed to optimize the whole neural network classifier. Four typical driving patterns, i.e., congested urban road, flowing urban road, suburban and highway, are selected based on multiple driving cycles. Simulation results show that the classifiers with GA optimized features have more powerful classification capability than principle component analysis (PCA) and kernel PCA (KPCA) based on k-nearest neighbor, support vector machine neural network classifiers and KPCA Convolutional neural network classifier. The proposed classifier obtains the satisfying classification accuracy with faster real-time classifying speed. [ABSTRACT FROM AUTHOR]

Myoelectric pattern recognition is widely used to control upper limb prostheses. However, the non-stationary characteristics of electromyography (EMG) signals, caused by physiological changes (e.g. muscle fatigue) or non-physiological changes (e.g. the electrode- skin impedance), hinder the use of prostheses and deteriorate the performance of the myoelectric control system. In this paper, a set of robust features is proposed to be integrated with adaptive learning techniques in order to improve the myoelectric performance. Four types of features are proposed, namely the H, HI, AI, and AIH features. The H features correspond to the histogram-oriented gradient (HOG) algorithm of the High-Density surface Electromyography map (HD-sEMG map). On the other hand, the HI features are generated by combining the H features and the intensity feature that is evaluated from the HD-sEMG map. AI is the intensity features calculated from the segmented HD-sEMG maps constructed in the individual channel. Finally, AIH features are obtained by combining the H features and AI features. Offline and online adaptive tests are conducted to evaluate the proposed features. The results show that employing the proposed AI features with an adaptive classifier improves the classification accuracy from 91.58% to 97.2% in online classification setups. Results also show that the AI features are more robust against noise than TD features. The average classification accuracy is reduced by 0.7% when additive White Gaussian noise is applied, in comparison to 5.8% reduction in accuracy when TD features is used. The results confirm the robustness of the proposed features extracted from the HD-sEMG map. [ABSTRACT FROM AUTHOR]

How to learn informative representations from Electromyography (EMG) signals is of vital importance for myoelectric control systems. Traditionally, hand-crafted features are extracted from individual EMG channels and combined together for pattern recognition. The spatial topological information between different channels can also be informative, which is seldom considered. This paper presents a radically novel approach to extract spatial structural information within diverse EMG channels based on the symmetric positive definite (SPD) manifold. The object is to learn non-Euclidean representations inside EMG signals for myoelectric pattern recognition. The performance is compared with two classical feature sets using accuracy and F1-score. The algorithm is tested on eleven gestures collected from ten subjects, and the best accuracy reaches 84.85%±5.15% with an improvement of 4.04%~20.25%, which outperforms the contrast method, and reaches a significant improvement with the Wilcoxon signed-rank test. Eleven gestures from three public databases involving Ninapro DB2, DB4, and DB5 are also evaluated, and better performance is observed. Furthermore, the computational cost is less than the contrast method, making it more suitable for low-cost systems. It shows the effectiveness of the presented approach and contributes a new way for myoelectric pattern recognition. [ABSTRACT FROM AUTHOR]

Recent one-stage object detectors follow a per-pixel prediction approach that predicts both the object category scores and boundary positions from every single grid location. However, the most suitable positions for inferring different targets, i.e., the object category and boundaries, are generally different. Predicting all these targets from the same grid location thus may lead to sub-optimal results. In this paper, we analyze the suitable inference positions for object category and boundaries, and propose a prediction-target-decoupled detector named PDNet to establish a more flexible detection paradigm. Our PDNet with the prediction decoupling mechanism encodes different targets separately in different locations. A learnable prediction collection module is devised with two sets of dynamic points, i.e., dynamic boundary points and semantic points, to collect and aggregate the predictions from the favorable regions for localization and classification. We adopt a two-step strategy to learn these dynamic point positions, where the prior positions are estimated for different targets first, and the network further predicts residual offsets to the positions with better perceptions of the object properties. Extensive experiments on the MS COCO benchmark demonstrate the effectiveness and efficiency of our method. With a single ResNeXt- $64{\times }4\text{d}$ -101-DCN as the backbone, our detector achieves 50.1 AP with single-scale testing, which outperforms the state-of-the-art methods by an appreciable margin under the same experimental settings. Moreover, our detector is highly efficient as a one-stage framework. Our code is public at https://github.com/yangli18/PDNet . [ABSTRACT FROM AUTHOR]

Image texture is an important part of many types of images, for example medical images. Texture Analysis is the technique that uses measurable features to categorize complex textures. The main goal is to extract discriminative features that are used in different pattern recognition applications and texture categorization. This paper investigates the extraction of most discriminative features for different texture images from the “Colored Brodatz” dataset using two types of image contrast measures, as well as using the statistical moments on five bands (red, green, blue, grey, and black). The Euclidean distance measure is used in the matching step to check the similarity degree. The proposed method was tested on 112 classes of textures. The achieved results showed that the proposed method is accurate and fast concerning classification accuracy with low computational complexity. The achieved Recognition Rate (RR) was 100%. [ABSTRACT FROM AUTHOR]

Wafer map defect patterns provide valuable information for root cause analysis and yield learning. Previous studies show that supervised machine learning (ML) methods can achieve good defect pattern recognition rate. However, the effectiveness of these methods rely on accurately labeled samples, which leads to two problems. First, oftentimes there are mislabeled data, which may prevent an accurate model from being established. Secondly, defect patterns that are not defined a priori will not be recognized. In this paper, we propose a semi-supervised learning method to deal with these problems. Labeled wafer maps are first used to train a prediction model, and questionable samples are excluded in the process. The prediction model is then used to classify unlabeled data. The remaining data that cannot be properly classified are then sent to an unsupervised learning algorithm to extract more defect patterns for enhanced labeling. The proposed approach is validated using the WM-811K database. With the proposed method, we are able to define five new defect pattern types, and the 14 defect types can be recognized with high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose an attention-based instance and semantic segmentation joint approach, termed ABJNet, for addressing the instance and semantic segmentation of 3D point clouds simultaneously. First, a point feature enrichment (PFE) module is used to enrich the segmentation network's input data by indicating the relative importance of each point's neighbors. Then, a more efficient attention pooling operation is designed to establish a novel module for extracting point cloud features. Finally, an efficient attention-based joint segmentation module (ABJS) is proposed for combining semantic features and instance features in order to improve both segmentation tasks. We evaluate the proposed attention-based joint semantic-instance segmentation neural network (ABJNet) on a variety of indoor scene datasets, including S3DIS and ScanNet V2. Experimental results demonstrate that our method outperforms the start-of-the-art method in 3D instance segmentation and significantly outperforms it in 3D semantic segmentation. [ABSTRACT FROM AUTHOR]

Traditional myoelectric pattern recognition (MPR) systems usually perform well under laboratory conditions. However, in practice, unknown motions that do not appear in the training phase will inevitably disturb MPR systems, leading to dramatic performance degradation in motion classification. Generative and discriminative models are two representative ways to solve this problem in the literature. They only learn the patterns of known motions and reject unknown motion samples endowed with different patterns. However, the generative models often perform poorly in categorizing known motions compared with the discriminative models. While in contrast, the discriminative models always perform relatively inferiorly in distinguishing unknown motions. In this paper, a novel myoelectric control method based on the convolutional prototype network (CPN) is proposed. CPN combines the advantage of the generative model and the discriminative model in an end-to-end manner. It constructs multiple prototypes of known motion classes and identifies samples of known/unknown classes using the template (i.e., prototype) matching. The proposed method is evaluated using the high-density surface electromyography (HD-sEMG) data recorded while performing seven known and six unknown hand motions. Compared to the latest deep learning-based study, our proposed method can yield a significantly higher accuracy of 97.87% in identifying unknown motions and reduce training and testing time by more than 80%, indicating that it is a promising solution to realize robust myoelectric control in practice. [ABSTRACT FROM AUTHOR]

Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether analysing leaf patterns can provide sufficient information for further differentiating cultivars. This paper reports our attempt on cultivar recognition from leaves as a general very fine-grained pattern recognition problem, which is not only a challenging research problem but also important for cultivar evaluation, selection and production in agriculture. We propose a novel local R-symmetry co-occurrence method for characterising discriminative local symmetry patterns to distinguish subtle differences among cultivars. Through scalable and moving R-relation radius pairs, we generate a set of radius symmetry co-occurrence matrices (RsCoM)and their measures for describing the local symmetry properties of interior regions. By varying the size of the radius pair, the RsCoM measures local R-symmetry co-occurrence from global/coarse to fine scales. A new two-phase strategy of analysing the distribution of local RsCoM measures is designed to match the multiple scale appearance symmetry pattern distributions of similar cultivar leaf images. We constructed three leaf image databases, SoyCultivar, CottCultivar, and PeanCultivar, for an extensive experimental evaluation on recognition across soybean, cotton and peanut cultivars. Encouraging experimental results of the proposed method in comparison with the state-of-the-art leaf species recognition methods demonstrate the effectiveness of the proposed method for cultivar identification, which may advance the research in leaf recognition from species to cultivar. [ABSTRACT FROM AUTHOR]

With the deployment of radar in versatile scenarios and a wide variety of potential targets, demand for automatic classification of various targets is increasing. The wide variety of radar signatures from physically smaller targets due to lower velocity / radar cross-section thresholds and the increased deployment of radar-based sensors do crowd the radar screen with misinterpreted targets. Micro-Doppler signatures have been widely employed by researchers for the recognition of those targets that exhibit micro-motions. This review article presents the evolution and recent advances in radar micro-Doppler based signature analysis and feature extraction. A review of the micro-Doppler-based target classification techniques along with their applications in defense and commercial sectors, has also been presented. This article provides the first review paper in the open literature that systematically covers the major steps along with the adopted practices in micro-Doppler based target recognition. Moreover, the limitations and future trends in the field are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment). Subsequently, it uses this representation to plot the data. One figure is produced for each time series. In consequence, the time series classification problem is converted into the visual pattern recognition problem. This transformation allows applying a wide range of algorithms for standard pattern recognition – in this domain, there are more options to choose from than in the domain of time series classification. In this paper, we demonstrated the high effectiveness of the new method in a series of experiments on publicly available time series. We compare our results with several state-of-the-art approaches dedicated to time series classification. The new method is robust and stable. It works for time series of differing lengths and is easy to extend and alter. Even with a baseline variant presented in an empirical study in this paper, it achieves a satisfying classification accuracy. Furthermore, the proposed conversion of raw time series into images that are subjected to feature extraction opens the possibility to apply standard clustering algorithms. [ABSTRACT FROM AUTHOR]

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively. [ABSTRACT FROM AUTHOR]

BACKGROUND: The presence of foreign materials (FM) not only reduces the commercial value of tobacco and the quality of cigarette products, but also affects the aroma and flavor of cigarettes. Existing tobacco deblending equipment has received little study with respect to homochromatic FM. In the present study, visible‐near infrared (VIS‐NIR) hyperspectral imaging technique combined with chemometrics were used to identify and visualize the homochromatic FM on the surface of thining tobacco. A comparison with conventional vision method was made to analyze the feasibility of the method. The importance of detecting FM in cut tobacco was further demonstrated by first studying the volatile organic compounds produced in cigarette mixed FM smoke and their effects on human health before conducting hyperspectral experiments. RESULTS: The results indicated that solid‐phase microextraction and gas chromatography mass spectrometry could detect volatile organic compounds in mainstream cigarette smoke that were not cigarette components and affected consumer health. Then, spectral features of the samples were extracted from hyperspectral images for building identification models to distinguish FM from cut tobacco. The visual RGB values of cut tobacco and FM were also used for the analysis of the recognition models. The results showed that the accuracy, precision and recall reached 100.00% using the back propagation artificial neural network classification model based on the principal component analysis raw wavelengths. The visualization results based on the optimal model produced clearer localization than conventional computer vision method. CONCLUSION: The present study revealed that the VIS‐NIR hyperspectral imaging technology had advantage in the detection and localization of FM on the surface of thinning tobacco, which provided a foundation for improving the quality and safety of cut tobacco production. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Aerial targets grouping is an important content of air battlefield situation awareness, and intelligent aerial targets grouping is an urgent need for intelligent air combat. Traditional aerial targets grouping methods have problems such as difficulty extracting air target features, difficulty designing formation similarity measures, and lack of data samples. To solve the above problems, this paper proposes a relational network based aerial targets grouping method to achieve end‐to‐end grouping under small sample conditions. Firstly, an adaptive clustering algorithm is proposed to preliminarily cluster the aerial targets in the air battlefield situation to obtain the group to be tested. Then, a representation of aerial target groups is designed, which unifies the multi‐dimensional features of the group's temporal, spatial, and attribute. Next, based on this representation, combining 3D convolutional neural networks (3DCNN) and long short‐term memory (LSTM), a neural network structure that can automatically extract multi‐dimensional features of groups is designed. After that, the relation module is used to learn the distance metric between groups. After training the relation network, the type of group to be tested can be identified, thereby achieving the goal of grouping. A dataset of 30 types of aerial target group patterns was established through simulation. The ablation experiments were conducted to verify the performance of each module, and the experimental results showed that the designed feature extraction module can effectively extract temporal sample features, while the designed relationship module performs better in extracting temporal sample information. By comparing several typical traditional aerial target grouping methods, the proposed method improves the grouping accuracy by 11.2% to 13.4%, achieving an accuracy of 91.1% in the 5‐way 1‐shot task and 95.6% in the 5‐way 5‐shot task. [ABSTRACT FROM AUTHOR]

In the analysis of surface electromyography signals(sEMG), the extraction of suitable features is one of the key factors affecting pattern recognition. The aim of this paper is to propose an improved sEMG feature extraction algorithm based on muscle activity regions. The fusion of muscle activity intensity on the basis of muscle activity regions compensates for the low accuracy of the original features for the recognition of similar movements. In this paper, the sEMG signals of five leg movements were collected, including two similar movements: upstairs and downstairs, standing and sitting. The classification performance of the features before and after the improvement was tested with six classifiers. It proves that the new characteristic, active muscle position and intensity (AMPI), greatly improves the classification accuracy of similar movements. The paper also compares the new features with the traditional eight classification features. The results show that the new features are at the forefront of the classification performance, with a very small difference in classification accuracy of 4.1% compared to the best performing features. This confirms the high practical value of the new features. New features are still based on the mapping relationship between movement patterns and active muscle regions. This provides new ideas for the feature extraction method of sEMG signals. In addition, compared with the traditional features, the new feature still have the ability to reduce the dimension, which provides a more applicable feature extraction method for the application of multi-channel electromyogram(EMG) signals acquisition devices and high-density electrodes.

In pattern recognition tasks, the information from system input is modeled through a series of nonlinear operations, which include but not limited to feature extraction, regression, and classification. Both theoretically and practically, these operations are inevitably subject to internal modeling error and external disturbance, resulting at a performance challenge. Those state-of-the-art methods, e.g. Convolutional Neural Network and Transformer, still display significant instabilities and failures under practical applications, so comes a lack of generalization. Consequently, the more robust pattern recognition methods and related theories still merit a further study. This paper firstly reviews those state-of-the-art technologies in the field. The bottleneck of performances in those latest researches is associated with a lack of disturbance estimation and corresponding compensation. Therefore, the implications of disturbance rejection in pattern recognition field are further discussed from a control point of view. Then, the open problems are summarized. Ultimately, a discussion of the potential solutions, which is related to the application of compensation on features, is given to highlight the future study. Through the systematic review in this paper, the disturbance rejection in pattern recognition is developed into a control problem. Hopefully, more effective control technologies for the compensation on features can be used to improve the robustness of pattern recognition theoretically and practically. • The pattern recognition process is inevitably subject to internal modeling error and external disturbance. • The shortcomings of existing disturbance rejection technologies are summarized. • The bottleneck of performances in those latest researches is associated with a lack of disturbance estimation and corresponding compensation. • The open problems and the potential solutions are summarized to discuss the future study in an innovative control point of view, i.e. a compensation on features. [ABSTRACT FROM AUTHOR]

Dry-type insulated transformers stand out for their higher applicability in substations, high-voltage instrumentation systems, and electrical installations. In this machine, the insulation system is constituted of dielectric materials such as epoxy resin and Nomex paper. Some critical issues in the operation of this equipment, such as overload, moisture, or heat, can induce a slow degradation of the physical–chemical properties of the dielectric materials, which can culminate in the total failure of the transformer. However, before the transformer's shutdown, it is common to detect discharge activity in the insulation system. Based on this issue, this work proposes an experimental and comparative analysis between acoustic emission and Hall-effect sensors, aiming at differentiating discharges in epoxy resin and Nomex paper, materials that constitute the insulation of the dry-type insulated transformers. Two signal processing techniques were studied: traditional frequency analysis and discrete wavelet transform. The objective is to develop signal processing techniques to differentiate each type of discharge since different discharges require different maintenance actions. The results obtained indicate that acoustic emission sensors and Hall sensors are promising in differentiating discharge in epoxy resin and Nomex paper. Furthermore, the pattern recognition tools presented by this work, which associated the wavelet levels energies and the energy of the full signals with the average band and the equivalent bandwidth, were effective to perform feature extraction of power transformer condition. [ABSTRACT FROM AUTHOR]

Background: The bearing is an important component of mechanical transmission, and its condition is closely related to the safe operation of the equipment. However, the nonlinear vibration signal of the bearing leads to low accuracy of fault diagnosis because it is difficult to extract bearing characteristics. Method: To solve this problem, a bearing fault diagnosis method based on variational mode decomposition (VMD) fuzzy entropy (FE) and improved deep belief networks (DBN) is proposed. Since the information on bearing characteristics is overlaid by strong noise, VMD is used to process the vibration signal and calculate the FE of the modal components. Then, an improved butterfly optimization algorithm (BOA) with a mixed strategy is proposed, and the improved BOA is applied to optimize the hyper-parameters of the DBN to obtain the optimized DBN model. Finally, the optimized DBN is used as a pattern recognition algorithm for fault diagnosis. Results: The two experimental results show that this method can effectively diagnose bearing faults. The diagnosis rates are 98.33 % and 100 %, respectively, which provide theoretical support for bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a K-order propagation number algorithm–Feature selection–Classification System (KFCS) is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations. [ABSTRACT FROM AUTHOR]

System logs play an important role in software development and system maintenance. Many system software programs continuously generate system logs during software runtimes for failure detection and diagnosis purposes. Currently, the analysis of system log data is mainly a manual process that highly depends on human knowledge and experience. This time-consuming task has become a problem because of the ever-increasing volume of log data. Existing studies have investigated machine learning and deep learning techniques to automate the failure detection task. This paper takes the deep learning approach and proposes two detection structures based on recurrent and convolutional neural networks. More importantly, this paper takes a step further by closely examining the timestamps of log data which existing studies have generally ignored. This study found that time information can be a distinguishing factor between regular and abnormal log sequences. Inspired by this observation, a novel method is proposed to integrate log timestamps in deep learning models using interpolation techniques. The evaluation results show that the log timestamps can significantly improve the performance of failure detection. Cross-comparison of the different models demonstrates that the proposed network structure can successfully utilize the timestamp information. The code is available on GitHub: https://github.com/hfyxin/Ts-models-log-data-analysis.git.

For natural gas pipeline leakage systems based on distributed feedback fiber laser vibration sensors (DFB-FL), a novel neural network model (mCNN-LFLBs) integrating multi-scale convolutional neural networks (mCNN) with local feature extraction blocks (LFLBs) is proposed in this study. To address the problem of feature loss in the traditional signal image transfer process, the one-dimensional original signal is directly used as the input of the model, effectively maintaining the time-domain correlation of the leaked signal. The time-domain correlation of the leakage signal is preserved. To address the problem that traditional pattern recognition algorithms have a weak ability to extract deep features and local features, mCNN is established by this model, which realizes the use of convolution kernels of different scales to extract features of different frequency bands in one-dimensional signals. Additionally, LFLBs are introduced, which enhances the local expression ability of the model, further improves the feature mining quality of the model, and better presents the deep features of leaked signals. Experimental results demonstrate that the mCNN-LFLBs model has a classification accuracy of up to 98.6%, with a testing response time of approximately 10 ms, and outstanding performance in terms of convergence speed. These results highlight the potential practical application of the mCNN-LFLBs model in natural gas leak detection. • This paper proposes a neural network model (mCNN-LFLBs) based on the Distributed Feedback Fiber Laser (DFB-FL) vibration sensor, which combines local feature extraction blocks (LFLBs) with multi-scale convolutional neural networks (mCNN). • By parallelizing three one-dimensional CNNs with different convolutional kernels, a multi-scale structure is created, allowing the model to adaptively learn features of different scales in the signal. The Local Feature Extraction Block (LFLB) further extracts local features. • The model exhibits not only a quicker convergence rate but also a 98.6% average recognition rate and clearer advantages in terms of leaking aperture size judgment accuracy. [ABSTRACT FROM AUTHOR]