Parallel Features Research Articles

A facial expression recognition network using hybrid feature extraction.

Facial expression recognition faces great challenges due to factors such as face similarity, image quality, and age variation. Although various existing end-to-end Convolutional Neural Network (CNN) architectures have achieved good classification results in facial expression recognition tasks, these network architectures share a common drawback that the convolutional kernel can only compute the correlation between elements of a localized region when extracting expression features from an image. This leads to difficulties for the network to explore the relationship between all the elements that make up a complete expression. In response to this issue, this article proposes a facial expression recognition network called HFE-Net. In order to capture the subtle changes of expression features and the whole facial expression information at the same time, HFE-Net proposed a Hybrid Feature Extraction Block. Specifically, Hybrid Feature Extraction Block consists of parallel Feature Fusion Device and Multi-head Self-attention. Among them, Feature Fusion Device not only extracts the local information in expression features, but also measures the correlation between distant elements in expression features, which helps the network to focus more on the target region while realizing the information interaction between distant features. And Multi-head Self-attention can calculate the correlation between the overall elements in the feature map, which helps the network to extract the overall information of the expression features. We conducted a lot of experiments on four publicly available facial expression datasets and verified that the Hybrid Feature Extraction Block constructed in this paper can improve the network's recognition ability for facial expressions.

A Hybrid Improved Dual-Channel and Dual-Attention Mechanism Model for Water Quality Prediction in Nearshore Aquaculture

The aquatic environment in aquaculture serves as the foundation for the survival and growth of aquatic animals, while a high-quality water environment is a necessary condition for promoting efficient and healthy aquaculture development. To effectively guide early warnings and the regulation of water quality in aquaculture, this study proposes a predictive model based on a dual-channel and dual-attention mechanism, namely, the DAM-ResNet-LSTM model. This model encompasses two parallel feature extraction channels: a residual network (ResNet) and long short-term memory (LSTM), with dual-attention mechanisms integrated into each channel to enhance the model’s feature representation capabilities. Then, the proposed model is trained, validated, and tested using water quality and meteorological parameter data collected by an offshore farm environmental monitoring system. The results demonstrate that the proposed dual-channel structure and dual-attention mechanism can significantly improve the predictive performance of the model. The prediction accuracy for pH, dissolved oxygen (DO), and salinity (SAL) (with Nash coefficients of 0.9361, 0.9396, and 0.9342, respectively) is higher than that for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), nitrite (NO2−), and active phosphate (AP) (with Nash coefficients of 0.8578, 0.8542, 0.8372, and 0.8294, respectively). Compared to the single-channel model DA-ResNet (ResNet integrated with the proposed dual-attention mechanism), the Nash coefficients for predicting pH, DO, SAL, COD, NH3-N, NO2−, and AP increase by 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, and 14.99%, respectively. Compared to the single-channel DA-LSTM model (LSTM integrated with the proposed dual-attention mechanism), the corresponding increases in Nash coefficients are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, and 10.2%, respectively. Compared to the ResNet-LSTM (ResNet and LSTM in parallel) model without the attention mechanism, the improvements in Nash coefficients are 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, and 4.13%, respectively. The predictive performance of the model fulfills the practical requirements for accurate forecasting of water quality in nearshore aquaculture.

Multiscale Convolution-Based Efficient Channel Estimation Techniques for OFDM Systems

With the advancement of wireless communication technology, the significance of efficient and accurate channel estimation methods has grown substantially. Recently, deep learning-based methods are being adopted to estimate channels with higher precision than traditional methods, even in the absence of prior channel statistics. In this paper, we propose two deep learning-based channel estimation models, CAMPNet and MSResNet, which are designed to consider channel characteristics from a multiscale perspective. The convolutional attention and multiscale parallel network (CAMPNet) accentuates critical channel characteristics by utilizing parallel multiscale features and convolutional attention, while the multiscale residual network (MSResNet) integrates information across various scales through cross-connected multiscale convolutional structures. Both models are designed to perform robustly in environments with complex frequency domain information and various Doppler shifts. Experimental results demonstrate that CAMPNet and MSResNet achieve superior performance compared to existing channel estimation methods within various channel models. Notably, the proposed models show exceptional performance in high signal-to-noise ratio (SNR) environments, achieving up to a 48.98% reduction in mean squared error(MSE) compared to existing methods at an SNR of 25dB. In experiments evaluating the generalization capabilities of the proposed models, they show greater stability and robustness compared to existing methods. These results suggest that deep learning-based channel estimation models have the potential to overcome the limitations of existing methods, offering high performance and efficiency in real-world communication environments.

Speech Emotion Recognition Model Based on Joint Modeling of Discrete and Dimensional Emotion Representation

This paper introduces a novel joint model architecture for Speech Emotion Recognition (SER) that integrates both discrete and dimensional emotional representations, allowing for the simultaneous training of classification and regression tasks to improve the comprehensiveness and interpretability of emotion recognition. By employing a joint loss function that combines categorical and regression losses, the model ensures balanced optimization across tasks, with experiments exploring various weighting schemes using a tunable parameter to adjust task importance. Two adaptive weight balancing schemes, Dynamic Weighting and Joint Weighting, further enhance performance by dynamically adjusting task weights based on optimization progress and ensuring balanced emotion representation during backpropagation. The architecture employs parallel feature extraction through independent encoders, designed to capture unique features from multiple modalities, including Mel-frequency Cepstral Coefficients (MFCC), Short-term Features (STF), Mel-spectrograms, and raw audio signals. Additionally, pre-trained models such as Wav2Vec 2.0 and HuBERT are integrated to leverage their robust latent features. The inclusion of self-attention and co-attention mechanisms allows the model to capture relationships between input modalities and interdependencies among features, further improving its interpretability and integration capabilities. Experiments conducted on the IEMOCAP dataset using a leave-one-subject-out approach demonstrate the model’s effectiveness, with results showing a 1–2% accuracy improvement over classification-only models. The optimal configuration, incorporating the joint architecture, dynamic weighting, and parallel processing of multimodal features, achieves a weighted accuracy of 72.66%, an unweighted accuracy of 73.22%, and a mean Concordance Correlation Coefficient (CCC) of 0.3717. These results validate the effectiveness of the proposed joint model architecture and adaptive balancing weight schemes in improving SER performance.

MB-Net: A network for accurately identifying creeping landslides from wrapped interferograms

The efficient and automated identification of landslide hazards is essential for socio-economic development and human safety. Integrating the feature extraction capabilities of deep learning with the millimeter-level precision of Interferometric Synthetic Aperture Radar (InSAR) technology establishes a foundation for this task. However, current methods require unwrapping interferograms, and even converting them into deformation products before identifying landslide hazards. This process is susceptible to unwrapping errors, resulting in inefficient data utilization, and demands considerable time and labor. To overcome these challenges, wrapped interferograms are directly utilized for identifying creeping landslides. In this study, trigonometric functions are applied to improve the representation of interferograms and to further enhance the data through rendering. Secondly, a multi-branch semantic segmentation network (MB-Net) was designed, with parallel branch encoding and progressive feature fusion to optimize the model’s ability to learn interferometric phases. Experimental results indicate a good performance, with the F1-score of 80.91 %, the Intersection over Union (IoU) of 67.94 %, and the Matthews correlation coefficient (MCC) of 80.16 % on the ISSLIDE dataset. To further validate the generalization capability of MB-Net, the public COMET-LiCS Sentinel-1 InSAR portal data was utilized, focusing on the middle reaches of the Jinsha River in China. The results highlight MB-Net’s efficacy in spatial transferability analysis. These findings emphasize the potential of our approach for large-scale landslide hazard identification, providing a crucial foundation for the utilization of interferograms in creeping landslide detection.

A hypergraph cell membrane computing network model for soybean disease identification

Accurate identification of soybean leaf diseases is essential to improving quality and yield. Aiming at the problem of insufficient data volume that may lead to model overfitting and low recognition ability, this paper proposes a hypergraph cell membrane computing network model for soybean disease identification (HcmcNet). The main components of HcmcNet are the pyramid convolutional feature extraction membrane, the ordinary feature extraction membrane, the U-type feature extraction membrane, and the dynamic attention membrane. The three parallel feature extraction membranes are designed to improve the model’s ability to capture disease features. The dynamic attention membrane aims to enhance the model’s expressiveness and performance by dynamically adjusting the attentional weights of the three feature extraction membranes to fuse the disease features effectively. Soybean leaf disease images were used to create the dataset and conduct experiments. The experimental results show that HcmcNet achieves 98% accuracy on the test set. Compared with classical models, HcmcNet shows obvious advantages in several evaluation metrics. We also conducted experiments on public datasets. The results show that it is feasible to use HcmcNet for soybean leaf disease recognition, and HcmcNet has higher classification accuracy and stronger generalization ability on small sample datasets. HcmcNet has great application prospects in soybean leaf disease recognition.

Remaining useful life prediction of rolling bearings based on parallel feature extraction

Purpose This study aims to introduce a method for predicting the remaining useful life (RUL) of bearings based on parallel feature extraction. The proposed model provides prior knowledge and removes redundant handcrafted feature information, additionally, which focuses on the important features at different time scales. Design/methodology/approach Distinct from traditional parallel feature extraction methods, which can lead to information redundancy, a one-dimensional convolutional autoencoder is introduced to process selected indicators to remove redundancy and retain useful feature information. To fully capture the important degradation information within different stages in the feature sequences, a novel multi-scale attention feature fusion module is proposed to extract degradation features at different time scales. Considering the impact of degradation modes on RUL prediction, a dual-task prediction module based on no degradation mode labels is designed to obtain accurate RUL. Findings Comparative experiments and ablation studies on the PHM2012 bearing dataset verified the effectiveness of the proposed method. Furthermore, the rationality of the selected parameters is confirmed through model parameter analysis. Originality/value The novelty of the proposed method is that it not only provides prior knowledge but also further removes redundant information from prior knowledge. In addition, the distribution differences between the original features and their multi-scale convolution results are measured through Kullback–Leibler divergence as the attention scores, which allows the proposed method to focus on important information at different time scales.

AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images

The semantic segmentation of high-resolution remote sensing images (HRRSIs) faces persistent challenges in handling complex architectural structures and shadow occlusions, limiting the effectiveness of existing deep learning approaches. To address these limitations, we propose an attention-focused feature enhancement network (AFENet) with a novel encoder–decoder architecture. The encoder architecture combines ResNet50 with a parallel multistage feature enhancement group (PMFEG), enabling robust feature extraction through optimized channel reduction, scale expansion, and channel reassignment operations. Building upon this foundation, we develop a global multi-scale attention mechanism (GMAM) in the decoder that effectively synthesizes spatial information across multiple scales by learning comprehensive global–local relationships. The architecture is further enhanced by an efficient feature-weighted fusion module (FWFM) that systematically integrates remote spatial features with local semantic information to improve segmentation accuracy. Experimental results across diverse scenarios demonstrate that AFENet achieves superior performance in building structure detection, exhibiting enhanced segmentation connectivity and completeness compared to state-of-the-art methods.

Scalable quantum convolutional neural network for image classification

Quantum machine learning (QML) is a promising area of research that combines the capability of quantum computing with machine learning approaches with the goal of outperforming traditional computers while processing vast amounts of data and solving challenging problems. Meanwhile, Convolutional Neural Networks (CNNs) excel in areas such as image classification, by extracting features more efficiently than traditional neural networks. Although traditional CNNs have shown good performance in image classification, training CNNs demands significant computational resources. Quantum machine learning also confronts several obstacles and constraints at present, and the number of qubits as well as the scalability of quantum computers need to be enhanced. In this paper, we propose a technique termed Scalable Quantum Convolutional Neural Networks (SQCNN) to overcome these limitations. We implemented the model on the TensorFlow Quantum platform, where we carried out simulations with the MNIST and Fashion MNIST datasets. The experimental results reveal that SQCNN achieves an average classification accuracy of 99.79%. Compared with existing quantum neural network models, our model not only has higher classification accuracy, but also demonstrates strong performance across other evaluation metrics. It is worth mentioning that the quantum circuit we designed draws on the idea of convolutional neural networks, which can better learn features by relying on superposition and entanglement between quantum gates. In particular, multiple independent quantum devices in the SQCNN system can extract features in parallel. This design allows the flexible use of quantum devices of different sizes, thereby achieving larger-scale machine learning tasks.

Lightweight Neural Network for Centroid Detection of Weak, Small Infrared Targets via Background Matching in Complex Scenes

In applications such as aerial object interception and ballistic estimation, it is crucial to precisely detect the centroid position of the target rather than to merely identify the position of the target bounding box or segment all pixels belonging to the target. Due to the typically long distances between targets and imaging devices in such scenarios, targets often exhibit a low contrast and appear as dim, obscure shapes in infrared images, which represents a challenge for human observation. To rapidly and accurately detect small targets, this paper proposes a lightweight, end-to-end detection network for small infrared targets. Unlike existing methods, the input of this network is five consecutive images after background matching. This design significantly improves the network’s ability to extract target motion features and effectively reduces the interference of static backgrounds. The network mainly consists of a local feature aggregation module (LFAM), which uses multiple-sized convolution kernels to capture multi-scale features in parallel and integrates multiple spatial attention mechanisms to achieve accurate feature fusion and effective background suppression, thereby enhancing the ability to detect small targets. To improve the accuracy of predicted target centroids, a centroid correction algorithm is designed. In summary, this paper presents a lightweight centroid detection network based on background matching for weak, small infrared targets. The experimental results show that, compared to directly inputting a sequence of images into the neural network, inputting a sequence of images processed by background matching can increase the detection rate by 9.88%. Using the centroid correction algorithm proposed in this paper can therefore improve the centroid localization accuracy by 0.0134.

A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection

In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. General object detection frameworks encounter difficulties in adequately tackling these challenges, leading to substantial information loss during network downsampling, inadequate feature fusion, and being unable to meet real-time requirements. In this paper, we propose a Real-Time Small Object Detection YOLO (RTSOD-YOLO) model to tackle the various challenges faced in UAVs detection. We further enhance the adaptive nature of the Adown module by incorporating an adaptive spatial attention mechanism. This mechanism processes the downsampled feature maps, enabling the model to better focus on key regions. Secondly, to address the issue of insufficient feature fusion, we employ combined serial and parallel triple feature encoding (TFE). This approach fuses scale-sequence features from both shallow features and twice-encoded features, resulting in a new small-scale object detection layer. While enhancing the global context awareness of the existing detection layers, this also enriches the small-scale object detection layer with detailed information. Since rich redundant features often ensure a comprehensive understanding of the input, which is a key characteristic of deep neural networks, we propose a more efficient redundant feature generation module. This module generates more feature maps with fewer parameters. Additionally, we introduce reparameterization techniques to compensate for potential feature loss while further improving the model’s inference speed. Experimental results demonstrate that our proposed RTSOD-YOLO achieves superior detection performance, with mAP50/mAP50:95 reaching 97.3%/51.7%, which represents improvement of 3%/3.5% over YOLOv8, and 2.6%/0.1% higher than YOLOv10. Additionally, it has the lowest parameter count and FLOPs, making it highly efficient in terms of computational resources.

SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by 1.72%, 5.19%, and 1.94% in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods.

Hybrid deep models for parallel feature extraction and enhanced emotion state classification

Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results. This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality reduction via Principal Component Analysis (PCA). The reduced features are then classified using a multi-class Support Vector Machine (SVM) to categorize different types of emotions. The proposed model was evaluated using the DEAP and EEG Brainwave datasets, both well-suited for emotion analysis due to their comprehensive EEG signal recordings and diverse emotional stimuli. The DEAP dataset includes EEG signals from 32 participants who watched 40 one-minute music videos, while the EEG Brainwave dataset categorizes emotions into positive, negative, and neutral based on EEG recordings from participants exposed to six different film clips. The proposed model achieved an accuracy of 95.54% and 97.26% for valence and arousal categories in the DEAP dataset, respectively, and 98.42% for the EEG Brainwave dataset. These results significantly outperform existing methods, demonstrating the model’s superior performance in terms of precision, recall, F1-score, specificity, and Mathew correlation coefficient. The integration of AlexNet and DenseNet, combined with PCA and multi-class SVM, makes this approach particularly effective for capturing the intricate patterns in EEG data, highlighting its potential for applications in human-computer interaction and mental health monitoring, marking a significant advancement over traditional methods.

Application of improved deep learning algorithm in obstacle avoidance of UAV

Abstract To enhance the intelligent obstacle avoidance capabilities of unmanned aerial vehicles (UAVs), we introduce a refined version of the You Only Look Once (YOLO) algorithm, termed Improved YOLO (I-YOLO). This novel algorithm not only maintains the swift detection speed inherent in the original YOLO but also augments its feature enhancement network. Specifically, we propose a three-branch parallel feature pyramid network (TBPFP) to capture richer semantic information pertaining to the context of small obstacles. Furthermore, we incorporate a generative adversarial network (GAN) in a cascaded manner to synthesize more realistic super-resolution images, thereby bolstering detection accuracy. Experimental evaluations demonstrate that, in comparison to the baseline YOLO algorithm, our I-YOLO variant exhibits superior detection precision and feature extraction capabilities in the perception of intricate environmental targets. While there is a modest decrement in detection speed, it remains adequate to fulfill real-time requirements, particularly in the context of video stream processing.

Optimization of steel plate quality inspection driven by PscSE and SPPFELAN

AbstractBased on the improved YOLOv8n, a steel plate defect detection and recognition method is proposed to address the high labor costs and workload of traditional tasks. SPPFELAN processes inputs in parallel to enhance computational efficiency by executing multiple pooling operations simultaneously. The parallel feature fusion module PscSE, using a mixed‐dimension SE attention mechanism (scSE), captures global and channel‐related information better, improving characterization capability. The EIOU loss function addresses the ambiguous aspect ratio definition of CIOU loss, enhancing detection accuracy and accelerating convergence. Results show the YOLOv8n‐PscSE‐SPPFELAN model achieves 76.9% mAP@0.5 on the Northeastern University steel plate dataset, a 4.6% improvement over the original YOLOv8n, with a computation amount of 7.7 GFLOPs, reducing resource usage and greatly improving detection speed.

MLFEU-NET: A Multi-scale Low-level Feature Enhancement Unet for breast lesions segmentation in ultrasound images

Breast lesions constantly threaten the health of females. Segmentation methods of breast lesions are important for clinical diagnosis, and neural networks have been widely used and played a significant role in this field. However, false detections and miss detections still exist due to the variability in the shape and location of breast lesions, artifacts and noise in breast ultrasound (BUS) images, and structural defects in conventional segmentation networks. In this paper, a multi-scale low-level feature enhancement Unet (MLFEU-net) structure is presented, consisting of the U-net structure, low-level feature enhancement block (LFEB), and a parallel multiscale feature fusion (PMFF) module. Specifically, LFEB enhances the detail information during shallow downsampling and further feature selection. Meanwhile, in the neck of Unet, PMFF module uses different scales of dilation convolution to provide different sizes of sensory fields, and to efficiently merge shallow and deep features filtered, leads to more accurate segmentation results. To evaluate the MLFEU-net’s segmentation ability, five quantitative metrics were used on two breast ultrasound datasets, and it was compared with several advanced segmentation techniques. The results illustrate that our approach surpasses other methods in performance. Moreover, robustness experiments validate the effectiveness of our approach in achieving robust segmentation of breast lesions.

Spatio-temporal fusion with motion masks for the moving small target detection from remote-sensing videos

Moving small target detection is an eye-catching research topic, full of great challenges in object detection field. Its primary difficulties exist in (i) the very small size proportion of the target region in background images, (ii) the quite weak contrast to background and (iii) the perception of slight target motion. Currently-existing detection methods mainly utilize the image features from spatial domain. This kind of features is quite weak to small targets. More new feature kinds need to be concerned. In view of these, besides traditional image clues, motion features are currently attracting more and more attention in small target detection. In order to promote detection performance, this paper proposes a Spatio-Temporal Fusion Network (STFNet) with two parallel feature extraction branches for detecting moving small targets. In this network, one branch is designed to capture the traditional semantic features from spatial domain, and the other is devised to perceive the slight target motion features hidden in temporal domain. Meanwhile, to optimize the extraction of spatio-temporal features, a group of motion masks is specially designed to guide feature extractors to pay more precise attention to the positions where small targets appear. Moreover, we design a new bridging module to enhance the cross-domain and cross-scale fusion of spatio-temporal features. Through extensive comparison and ablation experiments on seven sub-datasets, it is proved that our new STFNet is effective and it is obviously superior to the compared ones in detecting moving small targets from satellite sequence images. It achieves a Precision of 0.93, an mAP50 of 71.10% and an F1 of 0.84, evidently higher than existing state-of-the-art methods. Our codes are available at https://github.com/UESTC-nnLab/STF.

Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm

Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment. The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K-means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. Additionally, tests demonstrate that combining canopy and the K-means algorithm to analyze data performs consistently and dependably on the Hadoop platform and has a clustering result that offers a scalable and effective solution for power system monitoring.

Parallel finite-element codes for the Bogoliubov-de Gennes stability analysis of Bose-Einstein condensates

We present and distribute a parallel finite-element toolbox written in the free software FreeFEM for computing the Bogoliubov-de Gennes (BdG) spectrum of stationary solutions to one- and two-component Gross-Pitaevskii (GP) equations, in two or three spatial dimensions. The parallelization of the toolbox relies exclusively upon the recent interfacing of FreeFEM with the PETSc library. The latter contains itself a wide palette of state-of-the-art linear algebra libraries, graph partitioners, mesh generation and domain decomposition tools, as well as a suite of eigenvalue solvers that are embodied in the SLEPc library. Within the present toolbox, stationary states of the GP equations are computed by a Newton method. Branches of solutions are constructed using an adaptive step-size continuation algorithm. The combination of mesh adaptivity tools from FreeFEM with the parallelization features from PETSc makes the toolbox efficient and reliable for the computation of stationary states. Their BdG spectrum is computed using the SLEPc eigenvalue solver. We perform extensive tests and validate our programs by comparing the toolbox's results with known theoretical and numerical findings that have been reported in the literature. Program summaryProgram Title: FFEM_BdG_ddm_toolbox.zipCPC Library link to program files:https://doi.org/10.17632/w9hg964wpb.1Licensing provisions: GPLv3Programming language:FreeFEM (v 4.12) free software (www.freefem.org)Nature of problem: Among the plethora of configurations that may exist in Gross-Pitaevskii (GP) equations modeling one or two-component Bose-Einstein condensates, only the ones that are deemed spectrally stable (or even, in some cases, weakly unstable) have high probability to be observed in realistic ultracold atoms experiments. To investigate the spectral stability of solutions requires the numerical study of the linearization of GP equations, the latter commonly known as the Bogoliubov-de Gennes (BdG) spectral problem. The present software offers an efficient and reliable tool for the computation of eigenvalues (or modes) of the BdG problem for a given two- or three-dimensional GP configuration. Then, the spectral stability (or instability) can be inferred from its spectrum, thus predicting (or not) its observability in experiments.Solution method: The present toolbox in FreeFEM consists of the following steps. At first, the GP equations in two (2D) and three (3D) spatial dimensions are discretized by using P2 (piece-wise quadratic) Galerkin triangular (in 2D) or tetrahedral (in 3D) finite elements. For a given configuration of interest, mesh adaptivity in FreeFEM is deployed in order to reduce the size of the problem, thus reducing the toolbox's execution time. Then, stationary states of the GP equations are obtained by a Newton method whose backbone involves the use of a reliable and efficient linear solver judiciously selected from the PETSc1 library. Upon identifying stationary configurations, to trace branches of such solutions a parameter continuation method over the chemical potential in the GP equations (effectively controlling the number of atoms in a BEC) is employed with step-size adaptivity of the continuation parameter. Finally, the computation of the stability of branches of solutions (i.e. the BdG spectrum), is carried out by accurately solving, at each point in the parameter space, the underlying eigenvalue problem by using the SLEPc2 library. Three-dimensional computations are made affordable in the present toolbox by using the domain decomposition method (DDM). In the course of the computation, the toolbox stores not only the solutions but also the eigenvalues and respective eigenvectors emanating from the solution to the BdG problem. We offer examples for computing stationary configurations and their BdG spectrum in one- and two-component GP equations.Running time: From minutes to hours depending on the mesh resolution and space dimension.

Human Muscle sEMG Signal and Gesture Recognition Technology Based on Multi-Stream Feature Fusion Network

Surface electromyography signals have significant value in gesture recognition due to their ability to reflect muscle activity in real time. However, existing gesture recognition technologies have not fully utilized surface electromyography signals, resulting in unsatisfactory recognition results. To this end, firstly, a Butterworth filter was adopted to remove high-frequency noise from the signal. A combined method of moving translation threshold was introduced to extract effective signals. Then, a gesture recognition model based on multi-stream feature fusion network was constructed. Feature extraction and fusion were carried out through multiple parallel feature extraction paths, combined with convolutional neural networks and residual attention mechanisms. Compared to popular methods of the same type, this new recognition method had the highest recognition accuracy of 92.1% and the lowest recognition error of 5%. Its recognition time for a single-gesture image was as short as 4s, with a maximum Kappa coefficient of 0.92. Therefore, this method combining multi-stream feature fusion networks can effectively improve the recognition accuracy and robustness of gestures and has high practical value.