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Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an intelligent information decision-level fusion method for target recognition which takes full advantage of the ensemble classifier and Dempster–Shafer (DS) theory is proposed. To deal with the problem that DS produces counterintuitive results when evidence conflicts, a contraction–expansion function is introduced to modify the body of evidence to mitigate conflicts between pieces of evidence. In this method, preprocessing and feature extraction are first performed on the multi-frame dual-band infrared images to obtain the features of the target, which include long-wave radiant intensity, medium–long-wave radiant intensity, temperature, emissivity–area product, micromotion period, and velocity. Then, the radiation intensities are fed to the random convolutional kernel transform (ROCKET) architecture for recognition. For the micromotion period feature, a support vector machine (SVM) classifier is used, and the remaining categories of the features are input into the long short-term memory network (LSTM) for recognition, respectively. The posterior probabilities corresponding to each category, which are the result outputs of each classifier, are constructed using the basic probability assignment (BPA) function of the DS. Finally, the discrimination of the space target category is implemented according to improved DS fusion rules and decision rules. Continuous multi-frame infrared images of six flight scenes are used to evaluate the effectiveness of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method in this paper can reach 93% under the strong noise level (signal-to-noise ratio is 5). Its performance outperforms single-feature recognition and other benchmark algorithms based on DS theory, which demonstrates that the proposed method can effectively enhance the recognition accuracy of space infrared dim targets. [ABSTRACT FROM AUTHOR]

With the rapid development of information transfer technology and the influence of complex electromagnetic environment, the signal components in communication systems are becoming more and more complex, and the spectrum congestion is further aggravated, which seriously affects the performance of communication systems. Single-channel blind source separation technology is an effective solution, but the existing algorithms are deficient in terms of estimation accuracy, time delay and computational complexity. Based on the powerful feature extraction capability and timing signal processing capability of deep learning, this paper proposes an end-to-end time-domain blind separation convolutional network structure, including three parts: encoder, separation module and decoder. The attention mechanism module is also introduced into the encoding layer of the network to enhance the deep feature extraction ability of the network, highlight the influence of important timing features of communication signals, further improve the separation performance and enhance the practicality of the network. The experimental results show that the algorithm has superior performance and can effectively separate the mixed communication signal of various modulation types, and it can also extract own-side communication signals from high-power masked signals. Compared with the existing algorithms, the network in this paper has high separation accuracy and low computational complexity, which breaks the bottleneck of traditional single-channel blind separation algorithms in complex communication electromagnetic environment. [ABSTRACT FROM AUTHOR]

In this paper, a fault feature extraction method based on the combination of computational order tracking (COT) and variational mode decomposition (VMD) is proposed to solve the problem of fault feature extraction in a gearbox under variable speed conditions. First, a speed estimation method based on a forward-backward greedy algorithm and Fourier fitting is proposed to solve the speed estimation problem under variable speed conditions. Then, a feature extraction method based on COT, VMD, and fast kurtometry is proposed. COT is used to calculate the order. After the signal is decomposed by VMD, a fast kurtogram is used to filter the modal components with obvious fault features so as to extract fault features. Finally, combined with the analysis of actual equipment examples, the experimental results show that steady-state filtering can effectively filter the background noise and improve the signal-to-noise ratio. The rotation speed estimated by the forward–backward greedy algorithm and Fourier fitting is very close to the actual speed, which verifies that the method proposed in this paper can effectively solve the problem of fault feature extraction of a gearbox under variable speed conditions. [ABSTRACT FROM AUTHOR]

Automated and accurate detection of power quality (PQ) events is important from the point of view of safety as well as maintaining the reliability of the power transmission and distribution network. However, detection of multiple PQ events in a noisy environment is challenging task. Another important issue is the choice of meaningful features that can directly influence the accuracy of PQ detection. Considering these two aforesaid facts, this paper presents a novel framework for automated classification of PQ signals in a noisy environment employing cross Stockwell Transform (XST). The XST proposed in this paper has better noise suppression capability compared to conventional Stockwell Transform. Here, XST was used to convert 1D PQ signals to 2D time–frequency (T–F) images. To improve the accuracy of PQ detection, an automated feature extraction method employing deep learning is implemented in this work. The noise free T–F images obtained using XST were fed as inputs to several pre-trained convolutional neural networks (CNNs) for deep feature extraction. Transfer learning technique was implemented to reduce the computational cost. The extracted deep features were further undergone selection using one-way analysis of variance test followed by false discovery rate correction. The statistically significant deep features were subsequently fed to three benchmark machine learning classifiers for classification of PQ signals. In addition, tests were also carried out on real-life PQ signals to verify the practicability of the proposed framework. Investigations revealed that the proposed method returned mean accuracy of 99.72% and 96.45% for classification of simulated and real-life PQ signals, respectively. [ABSTRACT FROM AUTHOR]

As a commonly used mode of transportation in people's daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals. [ABSTRACT FROM AUTHOR]

An energy-saving control strategy for wheel loaders is proposed in this paper to address the issue of high energy consumption during their operation. The strategy is based on the intelligent identification of working stages, allowing for staged power matching and resulting in reduced energy consumption. Each work stage of the loader is identified by matching it to the main pump pressure waveform and actuator pilot pressure waveform. Using a sliding time window method, pressure waveforms from each working stage are subjected to feature extraction. A bidirectional long short-term memory neural network (BILSTM) algorithm is then used to establish an intelligent recognition model. Based on work stage identification, an energy-saving control strategy based on power matching is proposed for the shoveling stage of the loader, and the Grey Wolf optimization (GWO)-PID algorithm is utilized for control parameter tuning. Finally, the effectiveness of the energy-saving control strategy based on work stage identification is verified through experiments. The research results indicate that the BILSTM recognition model outperforms other models with a recognition accuracy of 96.1%. The optimal time window width is 0.6 s, and the proposed energy-saving control strategy achieves a fuel-saving rate of 6.81%. This method provides feasibility for reducing energy consumption in construction machinery and achieving energy-saving and carbon-reduction goals. [ABSTRACT FROM AUTHOR]

Lung cancer is an uncontrolled growth of tissue causing a lump in the human lung. If lung cancer can be detected early, it can increase the survival rate. Therefore, a multi-classification approach of lung nodule detection with high computational effectiveness is required. In this paper, a multi-classification approach of lung nodule detection and classification is proposed using artificial intelligence on computed tomography (CT) scan images. Different preprocessing steps are applied for resizing, smoothing, and enhancement of the CT images. Then, two different approaches for feature extraction using VGG16 transfer learning and morphological segmentation are proposed. Morphological segmentation and feature extraction are applied for the segmentation of the region of interest and to extract the distinct features. Finally, the proposed deep learning architecture and seven different machine learning algorithms are applied on the preprocessed data and the extracted features for the classification of lung nodules into three classes: malignant, benign, and normal. It is observed that the stacked ensemble model of deep learning convolutional neural network (CNN) and VGG16 transfer learning models (CNN+VGG16) can achieve 99.55% accuracy using preprocessed data. It is also observed that all the ML algorithms perform with reasonably high accuracy using the low-dimensional morphological features. It is observed from the fivefold cross-validation results that logistic regression performs with 99.36% accuracy in 23.71 s time using the preprocessed data. Whereas, using the morphological features, k-nearest neighbor, and the support vector machine perform with the highest accuracy of 99.76% with very reduced computational time of 0.017 and 0.008 s, respectively. [ABSTRACT FROM AUTHOR]

This paper describes a novel method for detecting child malnutrition based on artificial intelligence and facial photography. Estimates of severe and moderate acute malnutrition in children are critical for rapid emergency responses. However, the two traditional measurement methods, mid‐upper arm circumference (MUAC) and weight‐for‐height (WFH), are impractical in conflict and catastrophic disaster situations. They require well‐trained enumerators, cumbersome equipment, and close supervision. The Method for Extremely Rapid Observation of Nutritional Status (MERON) addresses the problem, using simple facial photographs. Facial features are extracted to predict Body Mass Index (BMI) in adults and Weight for Height Z Score (WFHZ) in children under five. MERON correctly predicts adult BMI classification with 78% accuracy. A variant of the model, trained on a sample of 3167 children in Kenya, successfully classified 60% of cases. On most measures, MERON was easier and more culturally acceptable to use than the traditional measurement methods. If MERON were to be trained and validated on a larger sample, with more extreme cases, it would provide a practical solution to a recurrent humanitarian problem. [ABSTRACT FROM AUTHOR]

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats. [ABSTRACT FROM AUTHOR]

Film cooling is an effective technology for improving the thrust-to-weight ratio of an aero-engine. The axial orientation of film cooling holes is one of the key factors determining the film cooling effect. However, the machining quality of cooling holes is still carried out by manual inspection, whose accuracy and consistency cannot be guaranteed. To address this problem, this paper proposes an axial orientation extraction algorithm based on laser point cloud processing. Firstly, the blade surface is scanned by a line laser sensor and a blade point cloud is obtained. Secondly, a single film hole can be segmented from the blade point cloud based on scanning line fitting residual. Thirdly, an improved Gaussian map is to complement the arc belt obtained by Gaussian map to obtain a complete circle along an error-sensitive direction. Finally, the axial orientation is estimated by random sample consensus with an initial value. The accuracy and consistency of the algorithm are verified by comparing the extraction result by the algorithm with those by the Geomagic software. The result shows that the axial accuracy extracted by the algorithm can reach 0.405°. [ABSTRACT FROM AUTHOR]

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research. [ABSTRACT FROM AUTHOR]

Infrared target detection is widely used in industrial fields, such as environmental monitoring, automatic driving, etc., and the detection of weak targets is one of the most challenging research topics in this field. Due to the small size of these targets, limited information and less surrounding contextual information, it increases the difficulty of target detection and recognition. To address these issues, this paper proposes YOLO-ISTD, an improved method for infrared small target detection based on the YOLOv5-S framework. Firstly, we propose a feature extraction module called SACSP, which incorporates the Shuffle Attention mechanism and makes certain adjustments to the CSP structure, enhancing the feature extraction capability and improving the performance of the detector. Secondly, we introduce a feature fusion module called NL-SPPF. By introducing an NL-Block, the network is able to capture richer long-range features, better capturing the correlation between background information and targets, thereby enhancing the detection capability for small targets. Lastly, we propose a modified K-means clustering algorithm based on Distance-IoU (DIoU), called K-means_DIOU, to improve the accuracy of clustering and generate anchors suitable for the task. Additionally, modifications are made to the detection heads in YOLOv5-S. The original 8, 16, and 32 times downsampling detection heads are replaced with 4, 8, and 16 times downsampling detection heads, capturing more informative coarse-grained features. This enables better understanding of the overall characteristics and structure of the targets, resulting in improved representation and localization of small targets. Experimental results demonstrate significant achievements of YOLO-ISTD on the NUST-SIRST dataset, with an improvement of 8.568% in mAP@0.5 and 8.618% in mAP@0.95. Compared to the comparative models, the proposed approach effectively addresses issues of missed detections and false alarms in the detection results, leading to substantial improvements in precision, recall, and model convergence speed. [ABSTRACT FROM AUTHOR]

Binary code similarity detection plays a crucial role in various applications within binary security, including vulnerability detection, malicious software analysis, etc. However, existing methods suffer from limited differentiation in binary embedding representations across different compilation environments, lacking dynamic high-level semantics. Moreover, current approaches often neglect multi-level semantic feature extraction, thereby failing to acquire precise semantic information about the binary code. To address these limitations, this paper introduces a novel detection solution called BinBcla. This method employs an enhanced pre-training model to generate instruction embeddings with dynamic semantics for binary functions. Subsequently, multi-feature fusion technique is utilized to extract local semantic information and long-distance global features from the code, respectively, employing self-attention to comprehend the structure information of the code. Finally, an improved cosine similarity method is employed to learn relationships among all elements of the distance vectors, thereby enhancing the model's robustness to new sample functions. Experiments are conducted across different architectures, compilers, and optimization levels. The results indicate that BinBcla achieves higher accuracy, precision and F1 score compared to existing methods. [ABSTRACT FROM AUTHOR]

Ancient murals have suffered from continuous damage over time, and especially paint loss disease. Therefore, disease labeling, as the basis for ancient mural restoration, plays an important role in the protection of cultural relics. The predominant method of disease labeling is currently manual labeling, which is highly dependent on expert experience, time consuming, inefficient and results in inconsistent accuracy of the marking effect. In this paper, we propose a labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation. The proposed framework involves first the extraction of features from the hyperspectral image, and then image segmentation is performed based on the spatial features to obtain more accurate region boundaries. Then, the hyperspectral image's regions are classified based on their spatial-spectral characteristics, and the candidate areas of paint loss disease are obtained. Finally, by leveraging the true color image segmentation results, the proposed disease labeling strategy combines the results of classification and segmentation to propose the final paint loss disease labeling areas. The experimental results show that the proposed method can not only combine the hyperspectral space and spectral information effectively to obtain accurate labeling of paint loss disease, but can also mark the paint loss disease not easily observed using ordinary digital cameras. Compared with the state-of-the-art methods, the proposed framework could be promising for accurate and effective paint loss disease labeling for ancient murals. [ABSTRACT FROM AUTHOR]

Generally, the recognition performance of lightweight models is often lower than that of large models. Knowledge distillation, by teaching a student model using a teacher model, can further enhance the recognition accuracy of lightweight models. In this paper, we approach knowledge distillation from the perspective of intermediate feature-level knowledge distillation. We combine a cross-stage feature fusion symmetric framework, an attention mechanism to enhance the fused features, and a contrastive loss function for teacher and student models at the same stage to comprehensively implement a multistage feature fusion knowledge distillation method. This approach addresses the problem of significant differences in the intermediate feature distributions between teacher and student models, making it difficult to effectively learn implicit knowledge and thus improving the recognition accuracy of the student model. Compared to existing knowledge distillation methods, our method performs at a superior level. On the CIFAR100 dataset, it boosts the recognition accuracy of ResNet20 from 69.06% to 71.34%, and on the TinyImagenet dataset, it increases the recognition accuracy of ResNet18 from 66.54% to 68.03%, demonstrating the effectiveness and generalizability of our approach. Furthermore, there is room for further optimization of the overall distillation structure and feature extraction methods in this approach, which requires further research and exploration. [ABSTRACT FROM AUTHOR]

In this paper, a method based on transfer learning is proposed to recover the three-dimensional shape of cultural relics faces from a single old photo. It can simultaneously reconstruct the three-dimensional facial structure and align the texture of the cultural relics with fewer training samples. The UV position map is used to represent the three-dimensional shape in space and act as the output of the network. A convolutional neural network is used to reconstruct the UV position map from a single 2D image. In the training process, the human face data is used for pre-training, and then a small amount of artifact data is used for fine-tuning. A deep learning model with strong generalization ability is trained with fewer artifact data, and a three-dimensional model of the cultural relic face can be reconstructed from a single old photograph. The methods can train more complex deep networks without a large amount of cultural relic data, and no over-fitting phenomenon occurs, which effectively solves the problem of fewer cultural relic samples. The method is verified by restoring a Chinese Terracotta Warrior with facial cracks and a Buddha with a broken nose. Other applications can be used in the fields such as texture recovery, facial feature extraction, and three-dimensional model estimation of the damaged cultural relics or sculptures in the photos. [ABSTRACT FROM AUTHOR]

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields. [ABSTRACT FROM AUTHOR]

Various types of foreign fibres may be mixed in the planting, transportation, and production processes of cotton, which not only cause equipment to be out of control, but also leads to a decrease in the quality of cotton textile products and economic losses. The machine vision based detection method for cotton foreign fibres is widely used. Based on existing related research, we construct a classification dataset for cotton foreign fibres in practical scenarios, named the CF2113‐10 dataset. The authors design a basic foreign fibre classification network called CottonNet that balances performance and efficiency. The classification accuracy on the validation set reached 94.2%. In order to enhance the high‐level feature extraction ability, this paper improves the feature fusion method of residual networks and proposes CottonNet‐Res, which improves the classification accuracy to 95.1%. Finally, a classification model based on feature difference fitting, CottonNet‐Fusion, is proposed to address the classification problem of foreign fibre images sampled in complex environments. The classification accuracy of foreign fibre images sampled in ordinary scenes has improved to 97.4%, while the images sampled in complex environments maintain an accuracy of 90.3%. [ABSTRACT FROM AUTHOR]

Accurate classification of substation equipment images remains challenging due to various factors such as unexpected illumination, viewing angles, scale variations, shadows, surface contaminants, and different elements sharing similar appearances. This paper presents a novel two‐stage substation equipment classification method based on dual‐scale attention. Leveraging the region proposal technique from Faster‐regions with CNN features (RCNN), the input images are initially decomposed into multiple scales to capture latent features. A dual‐scale attention module is introduced to enhance the precision of feature extraction. Furthermore, a two‐stage network is proposed to address the challenge of classifying closely similar substation equipment. A multi‐layer perceptron performs a coarse classification to categorize the equipment into broad categories. Then, a lightweight classifier is employed for fine‐grained subclassification, further distinguishing equipment within the same broad category. To mitigate the issue of limited training data, a specialized dataset is collected and annotated for the substation equipment classification. Experimental results demonstrate that the proposed method achieves remarkable accuracy, recall, and F1‐score surpassing 0.91, outperforming mainstream approaches in terms of recall and F1 scores. Ablation experiments further validate the significant contributions of both the dual‐scale attention and the two‐stage classification module in improving the overall performance of the classification network. [ABSTRACT FROM AUTHOR]

In recent years, research on infrared and visible image fusion has mainly focused on deep learning‐based approaches, particularly deep neural networks with auto‐encoder architectures. However, these approaches suffer from problems such as insufficient feature extraction capability and inefficient fusion strategies. Therefore, this paper introduces a novel image fusion network to address the limitations of infrared and visible image fusion networks with auto‐encoder architectures. In the designed network, the encoder employs a multi‐branch cascade structure, and these convolution branches with different kernel sizes provide the encoder with an adaptive receptive field to extract multi‐scale features. In addition, the fusion layer incorporates a non‐local attention module that is inspired by the self‐attention mechanism. With its global receptive field, this module is used to build a non‐local attention fusion network, which works together with the l1${l}_1$‐norm spatial fusion strategy to extract, split, filter, and fuse global and local features. Comparative experiments on the TNO and MSRS datasets demonstrate that the proposed method outperforms other state‐of‐the‐art fusion approaches. [ABSTRACT FROM AUTHOR]

Slug flow processes draw much attention in chemical and pharmaceutical manufacturing thanks to their ability to eliminate downtime costs and batch‐to‐batch variation. Using in‐line video can monitor the current process status and improve the stability of the slug flow. However, there are no effective image processing methods aiming at such videos from chemical experiments due to limited training sample size and the variety of experimental settings. The paper proposes a training‐free method to automatically detect and measure the slugs from in‐line videos. Multiple image features are fused to identify the slug shapes under various lighting conditions, a six‐point model is fitted to achieve better volume estimation, and a consistency score is estimated to quantify the detection uncertainty. The proposed algorithm achieves similar results to manual labeling for various types of slug flow processes, improving the accuracy of slug column estimation by a large margin compared to the existing automatic methods. Finally, using it to monitor the slug flow process's stability and the change of the slug volume in an injection point are demonstrated. The method not only shows that handcrafted image features have the capability for detection and segmentation from chemical experiment images but also paves the road for a training‐based algorithm for the task. [ABSTRACT FROM AUTHOR]

Machine learned fault detection approaches are being increasingly used for fault detection in distribution grid. However, the performance of the models can be improved by customizing the models. In this regard, a customised artificial neural network (CANN) for fault detection in a distribution grid is proposed in this paper. The proposed work develops a CANN that combines the "up‐pyramid" and "down‐pyramid" model of ANN into a "custom‐pyramid" model. As a result, the same model can be used both for determining the types of fault as well as its location. The data needed to train the model has been taken from a reconfigured IEEE‐33 bus distribution system developed in Typhoon HIL real‐time simulator. Spectral‐kurtosis is utilized for extraction of features of the faulted transient signals which are used as input data to develop the CANN. The result showcases that the reduction of input features reduces computational complexity without compromising its accuracy. The proposed model classifies fault location with an accuracy of 95.43%. The proposed method also identifies fault type with an accuracy of 96.08%. Several test cases have been developed to test the method. The method proved to be able to perform in most of the cases. [ABSTRACT FROM AUTHOR]

Hall thrusters are electric propulsion devices that are widely used in modern spaceflight. To obtain the critical corrosion data effectively and accurately during long-life tests of Hall thrusters, this paper proposes an automated corrosion measurement method based on three-dimensional point cloud technology. This method uses binocular structured light scanning method to obtain surface point cloud data. The point cloud topology relationship of these disordered data is also established by KD-tree and radius filtering. The base plane is determined according to the average of the outer circle plane in the discharge chamber, and the original point cloud data is sliced transversely and longitudinally. The point cloud of section data is extracted and the corrosion width data is obtained according to the round hole feature extraction and center of circle calculation. The corrosion angle is calculated according to the angle between the straight line and the normal of the section. The proposed method has been verified and the results show that the overall error accuracy is within 60 μm, which meets the requirements for automatic measurement of Hall thruster corrosion. It is a real and effective method to obtain the critical corrosion data during long-life tests of Hall thrusters. [ABSTRACT FROM AUTHOR]

Indoor localization has long been a challenging task due to the complexity and dynamism of indoor environments. This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM. Firstly, a dynamic feature filtering based on unsupervised learning and moving consistency checks is developed to eliminate the features of dynamic objects. Secondly, an improved line feature extraction algorithm based on LSD is proposed to optimize the effect of geometric feature extraction. Thirdly, geometric features are used to optimize localizability estimation, and an adaptive weight model and attention mechanism are built using the method of region delimitation and region growth. Finally, to verify the effectiveness and robustness of localizability estimation, multiple indoor experiments using the EuRoC dataset and TUM RGB-D dataset are conducted. Compared with ORBSLAM2, the experimental results demonstrate that absolute trajectory accuracy can be improved by 95% for equivalent processing speed in walking sequences. In fr3/walking_xyz and fr3/walking_half, ULG-SLAM tracks more trajectories than DS-SLAM, and the ATE RMSE is improved by 36% and 6%, respectively. Furthermore, the improvement in robot localizability over DynaSLAM is noteworthy, coming in at about 11% and 3%, respectively. [ABSTRACT FROM AUTHOR]

SAR image target recognition relies heavily on a large number of annotated samples, making it difficult to classify the unseen class targets. Due to the lack of effective category auxiliary information, the current zero-shot target recognition methods for SAR images are limited to inferring only one unseen class rather than classifying multiple unseen classes. To address this issue, a conditional generative network with the category features from the simulated images for zero-shot SAR target recognition is proposed in this paper. Firstly, the deep features are extracted from the simulated images and fused into the category features that characterize the entire class. Then, a conditional VAE-GAN network is constructed to generate the feature instances of the unseen classes. The high-level semantic information shared in the category features aids in generalizing the mapping learned from the seen classes to the unseen classes. Finally, the generated features of the unseen classes are used to train a classifier that can classify the real unseen images. The classification accuracies for the targets of the three unseen classes based on the proposed method can reach 99.80 ± 1.22% and 71.57 ± 2.28% with the SAMPLE dataset and the MSTAR dataset, respectively. The advantage and validity of the proposed architecture are indicated with a small number of the seen classes and a small amount of the training data. Furthermore, the proposed method can be extended to generalized zero-shot recognition. [ABSTRACT FROM AUTHOR]

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model's ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection. [ABSTRACT FROM AUTHOR]

The magnetic flux leakage (MFL) technique is widely employed for nondestructive testing of ferromagnetic specimens and materials, including wire ropes, bridge cables, and pipelines. As regards the MFL testing, extracting features from MFL signals is crucial for defect recognition and estimation of corresponding widths. Deep learning has been extensively used for feature extraction, but it often performs inadequately on a small sample dataset. To address this limitation, this paper develops a network framework that combines the Wavelet Scattering Transform (WST) and Neural Networks (NN) for defect width estimation. The WST is a knowledge-based feature extraction technique with a structure similar to convolutional neural networks. It offers a translation-invariant representation of signal features using a redundant dictionary of wavelets. The NN then maps the WST feature representation to the defect width information. Experiments on real steel plates with defects are carried out to validate the effectiveness of the proposed framework. Quantitative comparisons of the experimental results demonstrate that the proposed framework achieves better estimation performance in handling MFL signals and has superiority in scenarios with limited training samples. [ABSTRACT FROM AUTHOR]

Feature extraction and matching play integral roles in autonomous vision navigation. As a typical morphological feature, craters can be used as navigation landmarks. However, the spin of small bodies introduces substantial variations in illumination and viewing angles within acquired images, thereby posing challenges to feature extraction and matching algorithms. Addressing these challenges, a deep learning-based approach emerges as pivotal in mitigating the limitations of existing navigation algorithms. This paper presents an algorithm designed to segment noteworthy craters and irregular regions on such small bodies, thereby furnishing crucial autonomous navigation data for missions involving uncharted terrains. By seamlessly integrating deep learning techniques with conventional computer vision-based feature description methods, the algorithm achieves matching segmentation results of noteworthy precision. The experimental results show that the algorithm has better results compared to the existing mainstream segmentation networks. And it can achieve the accurate matching of segmentation results. • Accurate segmentation of small-body craters via deep-learning Algorithms. • Gradient descriptor construction method based on segmentation results. • The proposed method has viewpoint, illumination, scale, and rotation invariance. [ABSTRACT FROM AUTHOR]

Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model's adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments. [ABSTRACT FROM AUTHOR]

Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models' oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks. [ABSTRACT FROM AUTHOR]

This research paper aims to ensure the reliable and efficient operation of grid-connected solid-state transformers (SSTs) by detecting and evaluating various undesirable operating conditions. The study considers different types of faults, including internal faults like open switches and open capacitors, external faults such as symmetrical and asymmetrical faults occurring at various locations of the SST, and abnormalities on the grid side known as sympathetic inrush conditions. To analyze these operating conditions, the secondary current of the high-frequency transformer is normalized and decomposed using the discrete wavelet transform (DWT) and wavelet packet transform (WPT). From the DWT and WPT decomposition at multiple levels, several statistical parameters are calculated. These statistical parameters are carefully selected from different decomposition levels to enhance the effectiveness of the detection algorithm utilizing DWT and WPT. In order to quickly identify and classify all operating conditions that impact the performance of the grid-connected SST, a three-layer feedforward artificial neural network (ANN) is employed, using the selected statistical features. The accuracy and efficiency of the ANN-based classification approach are evaluated by assessing the effectiveness of the statistical features obtained from DWT and WPT. Simulation results have been altered by introducing various noise levels to systematically assess the performance of the proposed algorithms. The average accuracy of the DWT-ANN algorithm stands at 97.89%, while the WPT-ANN algorithm achieves a slightly elevated accuracy level of 98.01%. This notable similarity in accuracy curtails from the careful selection of the wavelet function, decomposition level, and feature sets. [ABSTRACT FROM AUTHOR]

In remote sensing image fusion, the conventional Convolutional Neural Networks (CNNs) extract local features of the image through layered convolution, which is limited by the receptive field and struggles to capture global features. Transformer utilizes self-attention to capture long-distance dependencies in images, which has a global receptive field, but the computational cost for high-resolution images is excessively high. In response to the above issues, this paper draws inspiration from the FusionNet network, harnessing the local detail acquisition capability of CNNs and the global data procuring capacity of Transformer. It presents a novel method for remote sensing image sharpening named Guided Filtering-Cross Stage Partial Network-Transformer, abbreviated as GF-CSTNet. This solution unifies the strengths of Guided Filtering (GF), Cross Stage Partial Network (CSPNet), and Transformer. Firstly, this method utilizes GF to enhance the acquired remote sensing image data. The CSPNet and Transformer structures are then combined to further enhance fusion performance by leveraging their respective advantages. Subsequently, a Rep-Conv2Former method is designed to streamline attention and extract diverse receptive field features through a multi-scale convolution modulator block. Simultaneously, a reparameterization module is constructed to integrate the multiple branches generated during training into a unified branch during inference, thereby optimizing the model's inference speed. Finally, a residual learning module incorporating attention has been devised to augment the modeling and feature extraction capabilities of images. Experimental results obtained from the GaoFen-2 and WorldView-3 datasets demonstrate the effectiveness of the proposed GF-CSTNet approach. It effectively extracts detailed information from images while avoiding the problem of spectral distortion. [ABSTRACT FROM AUTHOR]

There exist numerous multi‐path signals with impulsive noise (IN) in a multi‐path propagation environment. The direction‐of‐arrival (DOA) is highly challenging to be assessed because of the strong IN. For various military along with civilian applications, DOA estimation has turned into a hopeful technology. But, estimating DOA for multipath signals with IN environments is extremely complicated. A desirable output is not attained by several existent techniques, namely subspace‐centric approaches, maximum likelihood techniques, along with sparse representation‐centric methods, for DOA assessment. Therefore, an effective satin bowerbird optimization‐based deep learning neural network centered DOA estimation upon single snapshots is proposed in this paper for attaining robust and precise DOA estimation in multipath and IN environments. An arbitrary array configuration is employed by utilizing the proposed work as an input signal for establishing the received signal's model. Pre‐processing, time‐frequency domain conversion, feature extraction, feature reduction and DOA estimation are the proposed method's 5 disparate stages. To evince the proposed model's efficacy, its outcomes are analogized with other prevailing techniques regarding some performance metrics. The proposed model attains the root mean square error of 0.6484366‐AZ and 0.6484366‐EA, root mean squared percentage error (RMPSE) of 4.636107‐AZ and 5.628599‐EA and probability of resolution of 0.967999‐AZ and 0.962841‐EA, which are extremely less when compared to the prevailing methods; thus, depicting the superiority of the proposed model for an effective DOA estimation. [ABSTRACT FROM AUTHOR]

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra "discriminator" named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two "discriminators" correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model's feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual "discriminators". Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, computational intelligence-assisted autonomous assessment of education quality has become a more and more general concern in the area of smart education management. As education quality assessment is a complicated process with multiple heterogeneous factors, it remains challenging to make effective assessment using simple information modality and criteria. To deal with this issue, this paper introduces multiscale deep learning, and proposes a novel data-driven autonomous assessment framework for education quality. In particular, four aspects of heterogeneous indicators are selected as the basic indexes and a dilated convolutional neural network structure is formulated to perform multiscale feature extraction. Then, the structural equation is adopted to make multivariate characterization and output the final assessment results. At last, some simulations are carried out on realistic education operation data to evaluate the efficiency of the proposed autonomous assessment framework. Two aspects of findings can be deduced from the results. For one thing, multisource fusion of multiple indicators well makes sense to autonomous education assessment. For another, multiscale deep learning can provide some beneficial promotion to assessment efficiency. [ABSTRACT FROM AUTHOR]

Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes. [ABSTRACT FROM AUTHOR]

Effective feature extraction and selection are crucial for the accurate classification and prediction of hand gestures based on electromyographic signals. In this paper, we systematically compare six filter and wrapper feature evaluation methods and investigate their respective impacts on the accuracy of gesture recognition. The investigation is based on several benchmark datasets and one real hand gesture dataset, including 15 hand force exercises collected from 14 healthy subjects using eight commercial sEMG sensors. A total of 37 time- and frequency-domain features were extracted from each sEMG channel. The benchmark dataset revealed that the minimum Redundancy Maximum Relevance (mRMR) feature evaluation method had the poorest performance, resulting in a decrease in classification accuracy. However, the RFE method demonstrated the potential to enhance classification accuracy across most of the datasets. It selected a feature subset comprising 65 features, which led to an accuracy of 97.14%. The Mutual Information (MI) method selected 200 features to reach an accuracy of 97.38%. The Feature Importance (FI) method reached a higher accuracy of 97.62% but selected 140 features. Further investigations have shown that selecting 65 and 75 features with the RFE methods led to an identical accuracy of 97.14%. A thorough examination of the selected features revealed the potential for three additional features from three specific sensors to enhance the classification accuracy to 97.38%. These results highlight the significance of employing an appropriate feature selection method to significantly reduce the number of necessary features while maintaining classification accuracy. They also underscore the necessity for further analysis and refinement to achieve optimal solutions. [ABSTRACT FROM AUTHOR]

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance. [ABSTRACT FROM AUTHOR]

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings. [ABSTRACT FROM AUTHOR]

Gait, a manifestation of one's walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual's health status. However, the current study has shortcomings in the extraction of temporal and spatial dependencies in joint motion, resulting in inefficiencies in pathological gait classification. In this paper, we propose a Frequency Pyramid Graph Convolutional Network (FP-GCN), advocating to complement temporal analysis and further enhance spatial feature extraction. specifically, a spectral decomposition component is adopted to extract gait data with different time frames, which can enhance the detection of rhythmic patterns and velocity variations in human gait and allow a detailed analysis of the temporal features. Furthermore, a novel pyramidal feature extraction approach is developed to analyze the inter-sensor dependencies, which can integrate features from different pathways, enhancing both temporal and spatial feature extraction. Our experimentation on diverse datasets demonstrates the effectiveness of our approach. Notably, FP-GCN achieves an impressive accuracy of 98.78% on public datasets and 96.54% on proprietary data, surpassing existing methodologies and underscoring its potential for advancing pathological gait classification. In summary, our innovative FP-GCN contributes to advancing feature extraction and pathological gait recognition, which may offer potential advancements in healthcare provisions, especially in regions with limited access to medical resources and in home-care environments. This work lays the foundation for further exploration and underscores the importance of remote health monitoring, diagnosis, and personalized interventions. [ABSTRACT FROM AUTHOR]

Road crack detection is one of the important parts of road safety detection. Aiming at the problems such as weak segmentation effect of basic U-Net on pavement crack, insufficient precision of crack contour segmentation, difficult to identify narrow crack and low segmentation accuracy, this paper proposes an improved U-net network pavement crack segmentation method. VGG16 and Up_Conv (Upsampling Convolution) modules are introduced as backbone network and feature enhancement network respectively, and the more abstract features in the image are extracted by using the Block depth separable convolution blocks, and the multi-scale features are captured and enhanced by higher level semantic information to improve the recognition accuracy of narrow cracks in the road surface. The improved network embedded the Ca(Channel Attention) attention mechanism in U-net's jump connection to enhance the crack portion to suppress background noise. At the same time, DG_Conv(Depthwise GSConv Convolution) module and UnetUp(Unet Upsampling) module are added in the decoding part to extract richer features through more convolutional layers in the network, so that the model pays more attention to the detailed part of the crack, so the segmentation accuracy can be improved. In order to verify the model's ability to detect cracks in complex backgrounds, experiments were carried out on CFD and Deepcrack datasets. The experimental results show that compared with the traditional U-net network F1-score and mIoU have increased by 13.6% and 9.9% respectively. Superior to advanced models such as U-net, Segnet and Linknet in accuracy and generalization ability, the improved model provides a new method for asphalt pavement crack detection. The model is more conducive to practical application and ground deployment, and can be applied in road maintenance projects. [ABSTRACT FROM AUTHOR]

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice. [ABSTRACT FROM AUTHOR]

In this paper, a novel image reconstruction method for printmaking is proposed via the combination of nonlinear diffusion filtering and convolution neural network. Nonlinear diffusion filtering based on a partial differential equation abstracts the input image to extract color features and texture features of images by nonlinear structured tensors. The image color features and texture features are combined to obtain a feature vector, and the feature vector is fed into the model as the input data. Then, the pre-trained deep learning model VGG-19 is utilized as the backbone network for further feature representation, and the extracted feature maps of each layer of the VGG19 model can be visualized for image reconstruction. The quality of the reconstructed images of prints is improved with the powerful feature extraction and combination ability of neural networks, which has the advantages of fast reconstruction speed and high quality of reconstructed images. Finally, the simulation experiments can demonstrate that the proposed reconstruction approach can achieve relatively ideal reconstruction performance in scenes of printmaking. [ABSTRACT FROM AUTHOR]

Aiming at the problem of cartoon piracy and plagiarism, this paper proposes a method of cartoon copyright recognition based on character personality actions. This method can be used to compare the original cartoon actions with the action characteristics of pirated or copied cartoons to identify whether there is piracy or plagiarism. Firstly, an image preprocessing scheme for character extraction is designed. GrabCut interactive image segmentation algorithm was used to obtain cartoon characters, and then binarization and morphological processing were performed on the results. Secondly, a feature extraction scheme based on character profile, moving character and character pose is designed. By extracting the perimeter and area of the character contour, the length-to-width ratio of the smallest rectangle and the inclination angle of the contour, the character contour features are obtained. The three-dimensional coordinates are established by the central point position of the cartoon character in the two-dimensional image and the change of the character's zoom in and out, and the character's motion angle characteristics are calculated. By skeletonizing a character to obtain the pose characteristics, and using deburring operation to remove redundant branches, then extract the skeleton joint angle information. Finally, feature fusion is performed on the extracted features. The experimental results show that the proposed method breaks the limitation of the conventional single feature based recognition, and can better extract the character features including contour feature, motion feature and pose feature through multi-feature based extraction, so as to protect the cartoon copyright. [ABSTRACT FROM AUTHOR]

The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets [ABSTRACT FROM AUTHOR]

Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different writing styles, cursive scripts, and limited data make Urdu text recognition a complicated task. Major languages, such as English, have experienced advances in automated recognition, whereas low-resource languages, such as Urdu, still lag. Transformer-based models are promising for automated recognition in high- and low-resource languages such as Urdu. This paper presents a transformer-based method called ET-Network that integrates self-attention into EfficientNet for feature extraction and a transformer for language modeling. The use of self-attention layers in EfficientNet helps to extract global and local features that capture long-range dependencies. These features proceeded into a vanilla transformer to generate text, and a prefix beam search is used for the finest outcome. NUST-UHWR, UPTI2.0, and MMU-OCR-21 are three datasets used to train and test the ET Network for a handwritten Urdu script. The ET-Network improved the character error rate by 4% and the word error rate by 1.55%, while establishing a new state-of-the-art character error rate of 5.27% and a word error rate of 19.09% for Urdu handwritten text. [ABSTRACT FROM AUTHOR]

In response to the low matching accuracy of stereo matching algorithms in image regions with specular reflection, this paper proposes a multidimensional fusion stereo matching algorithm named MFANet. The algorithm embeds a multispectral attention module into the residual feature extraction network, utilizing two‐dimensional discrete cosine transforms to extract frequency features. In the pyramid pooling module, a coordinated attention mechanism is introduced to capture relevant positional information. In the cost aggregation part, the MFANet algorithm incorporates a three‐dimensional attention mechanism, focusing on the more important semantic information in high‐level features. By combining detailed information from low‐level features, semantic information from high‐level features, and contextual information, the algorithm generates features that are more conducive to disparity prediction. The MFANet algorithm is evaluated on three standard datasets (SceneFlow, KITTI2015, and KITTI2012). Experimental results demonstrate its robustness against specular reflection interference, accurate prediction of disparities in specular reflection pathological regions, and promising application prospects. [ABSTRACT FROM AUTHOR]