Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by le...Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.展开更多
For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high com...For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.展开更多
As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additi...As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.展开更多
The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiment...The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
On Twitter,people often use hashtags to mark the subject of a tweet.Tweets have specific themes or content that are easy for people to manage.With the increase in the number of tweets,how to automatically recommend ha...On Twitter,people often use hashtags to mark the subject of a tweet.Tweets have specific themes or content that are easy for people to manage.With the increase in the number of tweets,how to automatically recommend hashtags for tweets has received wide attention.The previous hashtag recommendation methods were to convert the task into a multi-class classification problem.However,these methods can only recommend hashtags that appeared in historical information,and cannot recommend the new ones.In this work,we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task.To train and evaluate the proposed method,we used the real tweet data which is collected from Twitter.Experimental results show that the proposed method can be significantly better than the most advanced method.Compared with the state-of-the-art methods,the accuracy of our method has been increased 4%.展开更多
The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power suppor...The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.展开更多
Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpret...Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.展开更多
The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accur...The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accuracy for UWB localization system in indoor environment.So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy.In this paper,a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed.The proposed FCN-Attention algorithm utilizes a Fully Convolution Network(FCN)for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy.The proposed algorithm is evaluated using an open-source dataset,a local collected dataset and a mixed dataset created from these two datasets.The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24%on the open-source dataset,100%on the local collected dataset and 92.01%on the mixed dataset,which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper.展开更多
Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment,the method of remaining life prediction that combines the self-...Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment,the method of remaining life prediction that combines the self-attention mechanism with the long short-term memory neural network(LSTM-NN)is proposed,called Self-Attention-LSTM.First,the auto-encoder is used to obtain the component-level state information;second,the state information of each component is input into the self-attention mechanism to learn the correlation between components;then,the multi-component correlation matrix is added to the LSTM input gate,and the LSTM-NN is used for life prediction.Finally,combined with the commercial modular aero-propulsion system simulation data set(C-MAPSS),the experiment was carried out and compared with the existing methods.Research results show that the proposed method can achieve better prediction accuracy and verify the feasibility of the method.展开更多
Relation extraction is an important task in NLP community.However,some models often fail in capturing Long-distance dependence on semantics,and the interaction between semantics of two entities is ignored.In this pape...Relation extraction is an important task in NLP community.However,some models often fail in capturing Long-distance dependence on semantics,and the interaction between semantics of two entities is ignored.In this paper,we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM(SA-Bi-LSTM)to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information,and capture Long-distance dependence on semantics.We conduct experiments using the SemEval-2010 Task 8 dataset.Extensive experiments and the results demonstrated that the proposed method is effective against relation classification,which can obtain state-ofthe-art classification accuracy just with minimal feature engineering.展开更多
Background A crucial element of human-machine interaction,the automatic detection of emotional states from human speech has long been regarded as a challenging task for machine learning models.One vital challenge in s...Background A crucial element of human-machine interaction,the automatic detection of emotional states from human speech has long been regarded as a challenging task for machine learning models.One vital challenge in speech emotion recognition(SER)is learning robust and discriminative representations from speech.Although machine learning methods have been widely applied in SER research,the inadequate amount of available annotated data has become a bottleneck impeding the extended application of such techniques(e.g.,deep neural networks).To address this issue,we present a deep learning method that combines knowledge transfer and self-attention for SER tasks.Herein,we apply the log-Mel spectrogram with deltas and delta-deltas as inputs.Moreover,given that emotions are time dependent,we apply temporal convolutional neural networks to model the variations in emotions.We further introduce an attention transfer mechanism,which is based on a self-attention algorithm to learn long-term dependencies.The self-attention transfer network(SATN)in our proposed approach takes advantage of attention transfer to learn attention from speech recognition,followed by transferring this knowledge into SER.An evaluation built on Interactive Emotional Dyadic Motion Capture(IEMOCAP)dataset demonstrates the effectiveness of the proposed model.展开更多
Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operati...Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.展开更多
Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments.Several IIoT nodes operate confidential data(s...Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments.Several IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied structure.Intrusion Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant attention.However,existing ML and DL-based IDS still face a number of obstacles that must be overcome.For instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory devices.Imbalanced and fewer data potentially lead to low performance on existing IDS.This paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant features.The proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for prediction.The performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID datasets.These datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.展开更多
Early and timely diagnosis of stroke is critical for effective treatment,and the electroencephalogram(EEG)offers a low-cost,non-invasive solution.However,the shortage of high-quality patient EEG data often hampers the...Early and timely diagnosis of stroke is critical for effective treatment,and the electroencephalogram(EEG)offers a low-cost,non-invasive solution.However,the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning.To address this issue,our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention(PCGAN-EASA),which incrementally improves the quality of generated EEG features.This network can yield full-scale,fine-grained EEG features from the low-scale,coarse ones.Specially,to overcome the limitations of traditional generative models that fail to generate features tailored to individual patient characteristics,we developed an encoder with an effective approximating self-attention mechanism.This encoder not only automatically extracts relevant features across different patients but also reduces the computational resource consumption.Furthermore,the adversarial loss and reconstruction loss functions were redesigned to better align with the training characteristics of the network and the spatial correlations among electrodes.Extensive experimental results demonstrate that PCGAN-EASA provides the highest generation quality and the lowest computational resource usage compared to several existing approaches.Additionally,it significantly improves the accuracy of subsequent stroke classification tasks.展开更多
Traditional based deep learning intrusion detection methods face problems such as insufficient cloud storage,data privacy leaks,high com-munication costs,unsatisfactory detection rates,and false positive rate.To addre...Traditional based deep learning intrusion detection methods face problems such as insufficient cloud storage,data privacy leaks,high com-munication costs,unsatisfactory detection rates,and false positive rate.To address existing issues in intrusion detection,this paper presents a novel approach called CS-FL,which combines Federated Learning and a Self-Attention Fusion Convolutional Neural Network.Federated Learning is a new distributed computing model that enables individual training of client data without uploading local data to a central server.at the same time,local training results are uploaded and integrated across all participating clients to produce a global model.The sharing model reduces communication costs,protects data privacy,and solves problems such as insufficient cloud storage and“data islands”for each client.In the proposed method,a hybrid model is formed by integrating the self-Attention and similar parts of the Convolutional Neural Network in the local data processing.This approach not only enhances the performance of the hybrid model but also reduces computational overhead compared to pure hybrid neural networks.Results from experiments on the NSL-KDD dataset show that the proposed method outperforms other intrusion detection techniques,resulting in a significant improvement in performance.This demonstrates the effectiveness of the proposed approach in improving intrusion detection accuracy.展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
Wireless quantum communication networks transfer quantum state by teleportation. Existing research focuses on maximal entangled pairs. In this paper, we analyse the distributed wireless quantum communication networks ...Wireless quantum communication networks transfer quantum state by teleportation. Existing research focuses on maximal entangled pairs. In this paper, we analyse the distributed wireless quantum communication networks with partially entangled pairs. A quantum routing scheme with multi-hop teleportation is proposed. With the proposed scheme, is not necessary for the quantum path to be consistent with the classical path. The quantum path and its associated classical path are established in a distributed way. Direct multi-hop teleportation is conducted on the selected path to transfer a quantum state from the source to the destination. Based on the feature of multi-hop teleportation using partially entangled pairs, if the node number of the quantum path is even, the destination node will add another teleportation at itself. We simulated the performance of distributed wireless quantum communication networks with a partially entangled state. The probability of transferring the quantum state successfully is statistically analyzed. Our work shows that multi-hop teleportation on distributed wireless quantum networks with partially entangled pairs is feasible.展开更多
Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the ...Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.展开更多
To realize practical wide-area quantum communication,a satellite-to-ground network with partially entangled states is developed in this paper.For efficiency and security reasons,the existing method of quantum communic...To realize practical wide-area quantum communication,a satellite-to-ground network with partially entangled states is developed in this paper.For efficiency and security reasons,the existing method of quantum communication in distributed wireless quantum networks with partially entangled states cannot be applied directly to the proposed quantum network.Based on this point,an efficient and secure quantum communication scheme with partially entangled states is presented.In our scheme,the source node performs teleportation only after an end-to-end entangled state has been established by entanglement swapping with partially entangled states.Thus,the security of quantum communication is guaranteed.The destination node recovers the transmitted quantum bit with the help of an auxiliary quantum bit and specially defined unitary matrices.Detailed calculations and simulation analyses show that the probability of successfully transferring a quantum bit in the presented scheme is high.In addition,the auxiliary quantum bit provides a heralded mechanism for successful communication.Based on the critical components that are presented in this article an efficient,secure,and practical wide-area quantum communication can be achieved.展开更多
文摘Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.
基金supported by National Natural Science Foundation of China(Nos.61261016,61661025)Science and Technology Plan of Gansu Province(No.20JR10RA273).
文摘For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.
基金supported by the National Key Research and Development Program of China(2020YFC2200901)。
文摘As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.
文摘The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
文摘On Twitter,people often use hashtags to mark the subject of a tweet.Tweets have specific themes or content that are easy for people to manage.With the increase in the number of tweets,how to automatically recommend hashtags for tweets has received wide attention.The previous hashtag recommendation methods were to convert the task into a multi-class classification problem.However,these methods can only recommend hashtags that appeared in historical information,and cannot recommend the new ones.In this work,we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task.To train and evaluate the proposed method,we used the real tweet data which is collected from Twitter.Experimental results show that the proposed method can be significantly better than the most advanced method.Compared with the state-of-the-art methods,the accuracy of our method has been increased 4%.
基金supported by the National Key Research and Development Plan(No.2022YFB2902701)the key Natural Science Foundation of Shenzhen(No.JCYJ20220818102209020).
文摘The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.
基金This work was supported by National Key R&D Program of China(2019YFE0102900).
文摘Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.
基金supported by the National Key Research and Development Program of China[grant No.2016YF B0502200]the Postdoctoral Research Foundation of China[grant No.2020M682480]the Fundamental Research Funds for the Central Universities[grant No.2042021kf0009]。
文摘The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accuracy for UWB localization system in indoor environment.So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy.In this paper,a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed.The proposed FCN-Attention algorithm utilizes a Fully Convolution Network(FCN)for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy.The proposed algorithm is evaluated using an open-source dataset,a local collected dataset and a mixed dataset created from these two datasets.The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24%on the open-source dataset,100%on the local collected dataset and 92.01%on the mixed dataset,which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper.
基金the National Natural Science Foundation of China(Nos.51875451 and 51834006)。
文摘Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment,the method of remaining life prediction that combines the self-attention mechanism with the long short-term memory neural network(LSTM-NN)is proposed,called Self-Attention-LSTM.First,the auto-encoder is used to obtain the component-level state information;second,the state information of each component is input into the self-attention mechanism to learn the correlation between components;then,the multi-component correlation matrix is added to the LSTM input gate,and the LSTM-NN is used for life prediction.Finally,combined with the commercial modular aero-propulsion system simulation data set(C-MAPSS),the experiment was carried out and compared with the existing methods.Research results show that the proposed method can achieve better prediction accuracy and verify the feasibility of the method.
文摘Relation extraction is an important task in NLP community.However,some models often fail in capturing Long-distance dependence on semantics,and the interaction between semantics of two entities is ignored.In this paper,we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM(SA-Bi-LSTM)to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information,and capture Long-distance dependence on semantics.We conduct experiments using the SemEval-2010 Task 8 dataset.Extensive experiments and the results demonstrated that the proposed method is effective against relation classification,which can obtain state-ofthe-art classification accuracy just with minimal feature engineering.
基金the National Natural Science Foundation of China(62071330)the National Science Fund for Distinguished Young Scholars(61425017)+3 种基金the Key Program of the National Natural Science Foundation(61831022)the Key Program of the Natural Science Foundation of Tianjin(18JCZDJC36300)the Open Projects Program of the National Laboratory of Pattern Recognition and the Senior Visiting Scholar Program of Tianjin Normal Universitythe Innovative Medicines Initiative 2 Joint Undertaking(115902),which receives support from the European Union's Horizon 2020 research and innovation program and EFPIA.
文摘Background A crucial element of human-machine interaction,the automatic detection of emotional states from human speech has long been regarded as a challenging task for machine learning models.One vital challenge in speech emotion recognition(SER)is learning robust and discriminative representations from speech.Although machine learning methods have been widely applied in SER research,the inadequate amount of available annotated data has become a bottleneck impeding the extended application of such techniques(e.g.,deep neural networks).To address this issue,we present a deep learning method that combines knowledge transfer and self-attention for SER tasks.Herein,we apply the log-Mel spectrogram with deltas and delta-deltas as inputs.Moreover,given that emotions are time dependent,we apply temporal convolutional neural networks to model the variations in emotions.We further introduce an attention transfer mechanism,which is based on a self-attention algorithm to learn long-term dependencies.The self-attention transfer network(SATN)in our proposed approach takes advantage of attention transfer to learn attention from speech recognition,followed by transferring this knowledge into SER.An evaluation built on Interactive Emotional Dyadic Motion Capture(IEMOCAP)dataset demonstrates the effectiveness of the proposed model.
基金supported by the National Key R&D Program of China(No.2022YFB4301102).
文摘Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.
基金Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,Grant/Award Number:NU/IFC/02/SERC/-/31Institutional Funding Committee at Najran University,Kingdom of Saudi Arabia。
文摘Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments.Several IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied structure.Intrusion Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant attention.However,existing ML and DL-based IDS still face a number of obstacles that must be overcome.For instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory devices.Imbalanced and fewer data potentially lead to low performance on existing IDS.This paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant features.The proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for prediction.The performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID datasets.These datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.
基金supported by the General Program under grant funded by the National Natural Science Foundation of China(NSFC)(No.62171307)the Basic Research Program of Shanxi Province under grant funded by the Department of Science and Technology of Shanxi Province(China)(No.202103021224113).
文摘Early and timely diagnosis of stroke is critical for effective treatment,and the electroencephalogram(EEG)offers a low-cost,non-invasive solution.However,the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning.To address this issue,our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention(PCGAN-EASA),which incrementally improves the quality of generated EEG features.This network can yield full-scale,fine-grained EEG features from the low-scale,coarse ones.Specially,to overcome the limitations of traditional generative models that fail to generate features tailored to individual patient characteristics,we developed an encoder with an effective approximating self-attention mechanism.This encoder not only automatically extracts relevant features across different patients but also reduces the computational resource consumption.Furthermore,the adversarial loss and reconstruction loss functions were redesigned to better align with the training characteristics of the network and the spatial correlations among electrodes.Extensive experimental results demonstrate that PCGAN-EASA provides the highest generation quality and the lowest computational resource usage compared to several existing approaches.Additionally,it significantly improves the accuracy of subsequent stroke classification tasks.
基金sponsored by the National Natural Science Foundation of China under Grants 62271264,61972207,and 42175194the Project through the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institution.
文摘Traditional based deep learning intrusion detection methods face problems such as insufficient cloud storage,data privacy leaks,high com-munication costs,unsatisfactory detection rates,and false positive rate.To address existing issues in intrusion detection,this paper presents a novel approach called CS-FL,which combines Federated Learning and a Self-Attention Fusion Convolutional Neural Network.Federated Learning is a new distributed computing model that enables individual training of client data without uploading local data to a central server.at the same time,local training results are uploaded and integrated across all participating clients to produce a global model.The sharing model reduces communication costs,protects data privacy,and solves problems such as insufficient cloud storage and“data islands”for each client.In the proposed method,a hybrid model is formed by integrating the self-Attention and similar parts of the Convolutional Neural Network in the local data processing.This approach not only enhances the performance of the hybrid model but also reduces computational overhead compared to pure hybrid neural networks.Results from experiments on the NSL-KDD dataset show that the proposed method outperforms other intrusion detection techniques,resulting in a significant improvement in performance.This demonstrates the effectiveness of the proposed approach in improving intrusion detection accuracy.
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
基金Project supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China （Grant No. 60921063） and the National High Technology Research and Development Program of China （Grant No. 2013AA013601）.
文摘Wireless quantum communication networks transfer quantum state by teleportation. Existing research focuses on maximal entangled pairs. In this paper, we analyse the distributed wireless quantum communication networks with partially entangled pairs. A quantum routing scheme with multi-hop teleportation is proposed. With the proposed scheme, is not necessary for the quantum path to be consistent with the classical path. The quantum path and its associated classical path are established in a distributed way. Direct multi-hop teleportation is conducted on the selected path to transfer a quantum state from the source to the destination. Based on the feature of multi-hop teleportation using partially entangled pairs, if the node number of the quantum path is even, the destination node will add another teleportation at itself. We simulated the performance of distributed wireless quantum communication networks with a partially entangled state. The probability of transferring the quantum state successfully is statistically analyzed. Our work shows that multi-hop teleportation on distributed wireless quantum networks with partially entangled pairs is feasible.
基金the National Natural Science Foundation of China(NSFC)(Grant No.61671072).
文摘Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.
基金supported by the National Natural Science Foundation of China(Grant Nos.61072067 and 61372076)the 111 Project(Grant No.B08038)+1 种基金the Fund from the State Key Laboratory of Integrated Services Networks(Grant No.ISN 1001004)the Fundamental Research Funds for the Central Universities(Grant Nos.K5051301059 and K5051201021)
文摘To realize practical wide-area quantum communication,a satellite-to-ground network with partially entangled states is developed in this paper.For efficiency and security reasons,the existing method of quantum communication in distributed wireless quantum networks with partially entangled states cannot be applied directly to the proposed quantum network.Based on this point,an efficient and secure quantum communication scheme with partially entangled states is presented.In our scheme,the source node performs teleportation only after an end-to-end entangled state has been established by entanglement swapping with partially entangled states.Thus,the security of quantum communication is guaranteed.The destination node recovers the transmitted quantum bit with the help of an auxiliary quantum bit and specially defined unitary matrices.Detailed calculations and simulation analyses show that the probability of successfully transferring a quantum bit in the presented scheme is high.In addition,the auxiliary quantum bit provides a heralded mechanism for successful communication.Based on the critical components that are presented in this article an efficient,secure,and practical wide-area quantum communication can be achieved.
