The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized H...The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information.展开更多
Thermoresponsive biotinylated dendronized copolymers carrying dendritic oligoethylene glycol（OEG）pendants were prepared via free radical polymerization,and their protein recognitions based on biotin-avidin interacti...Thermoresponsive biotinylated dendronized copolymers carrying dendritic oligoethylene glycol（OEG）pendants were prepared via free radical polymerization,and their protein recognitions based on biotin-avidin interaction investigated.Both first（PG1） and second generation（PG2） dendronized copolymers were designed to examine possible thickness effects on the interaction between biotin and avidin.Inherited from the outstanding thermoresponsive properties from OEG dendrons,these biotinylated cylindrical copolymers show characteristic thermoresponsive behavior which provides an envelope to capture avidin through switching temperatures above or below their phase transition temperatures（T_（cp）s）.Thus,the recognition of polymer-supported biotin with avidin was investigated with UV/vis spectroscopy and dynamic laser light scattering.In contrast to the case for PG1,the increased thickness for copolymer PG2 hinders partially and inhibits the recognition of biotin moieties with avidin either below or above its T_（cp）.This demonstrates the significant architecture effects from dendronized polymers on the biotin moieties to shift onto periphery of the collapsed aggregates,which should be a prerequisite for protein recognition.These kinds of novel thermoresponsive copolymers may pave a way for the interesting biological applications in areas such as reversible activity control of enzyme or proteins,and for controlled delivery of drugs or genes.展开更多
Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep perf...Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.展开更多
Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in ...Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in many applications,such as smart home,healthcare,human computer interaction,sports analysis,and especially,intelligent surveillance.In this paper,we propose a robust and efficient HAR system by leveraging deep learning paradigms,including pre-trained models,CNN architectures,and their average-weighted fusion.However,due to the diversity of human actions and various environmental influences,as well as a lack of data and resources,achieving high recognition accuracy remain elusive.In this work,a weighted average ensemble technique is employed to fuse three deep learning models:EfficientNet,ResNet50,and a custom CNN.The results of this study indicate that using a weighted average ensemble strategy for developing more effective HAR models may be a promising idea for detection and classification of human activities.Experiments by using the benchmark dataset proved that the proposed weighted ensemble approach outperformed existing approaches in terms of accuracy and other key performance measures.The combined average-weighted ensemble of pre-trained and CNN models obtained an accuracy of 98%,compared to 97%,96%,and 95%for the customized CNN,EfficientNet,and ResNet50 models,respectively.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce...The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.展开更多
Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest....Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks.展开更多
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp...Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.展开更多
Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex dataset...Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.展开更多
Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food proce...Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.展开更多
Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to...Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to friction pain.The results showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex(PFC).The activation area decreased,and the negative activation intensity in the PFC region increased with increasing intensity of pain.The inhibitory interactions between different brain regions,especially between the PFC and primary somatosensory cortex(SI)regions were enhanced,and excitatory-inhibitory connections between the medial and lateral pain pathways were balanced during pain perception.The percentage power spectral density of theαrhythm(Dα),dominant singularity strength(αpeak)and longest vertical line(Vmax)of EEG signals induced by pain significantly decreased,and the percent-age power spectral density of theβrhythm(Dβ)significantly increased.The combination of multiple features of Dα,Dβ,αpeak and Vmax could significantly improve the average recognition accuracy of different pain states.This study elucidated the neural processing mechanisms of friction-induced pain,and EEG features associated with friction pain were extracted and recognized.It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-Computer Interface(BCI)system related to pain.展开更多
This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals.The proposed framework combines convolutional neural networks(CNNs)to extract spatial and local temporal fea...This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals.The proposed framework combines convolutional neural networks(CNNs)to extract spatial and local temporal features with the Transformer encoder that captures long-range dependencies in time-series data through multi-head attention.Model performance was evaluated on two widely used tactile biosignal datasets,HAART and CoST,which contain diverse affective touch gestures recorded from pressure sensor arrays.TheCNN-Transformer model achieved recognition rates of 93.33%on HAART and 80.89%on CoST,outperforming existing methods on both benchmarks.By incorporating temporal windowing,the model enables instantaneous prediction,improving generalization across gestures of varying duration.These results highlight the effectiveness of deep learning for tactile biosignal processing and demonstrate the potential of theCNN-Transformer approach for future applications in wearable sensors,affective computing,and biomedical monitoring.展开更多
Before joining the Beijing Dragon Shadow Puppetry Art Theatre,this group of young people rarely had the chance to meet people like themselves,lacked friends,faced obstacles at work,and felt alienated from society.Now,...Before joining the Beijing Dragon Shadow Puppetry Art Theatre,this group of young people rarely had the chance to meet people like themselves,lacked friends,faced obstacles at work,and felt alienated from society.Now,they have gained appreciation,recognition,confidence,friendships,and love.展开更多
The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen cl...The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.展开更多
What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview wit...What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview with this journal,Professor Shen Weirong ofTsinghua University discusses these issues on the basis of his research.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
Permeable electronics promise improved physiological comfort,but remain constrained by limited functional integration and poor mechanical robustness.Here,we report a three-dimensional(3D)permeable electronic system th...Permeable electronics promise improved physiological comfort,but remain constrained by limited functional integration and poor mechanical robustness.Here,we report a three-dimensional(3D)permeable electronic system that overcomes these challenges by combining electrospun SEBS nanofiber mats,high-resolution liquid metal conductors patterned via thermal imprinting(50μm),and a strain isolators(SIL)that protects vertical interconnects(VIAs)from stress concentration.This architecture achieves ultrahigh air permeability(>5.09 m L cm^(-2)min^(-1)),exceptional stretchability(750%fracture strain),and reliable conductivity maintained through more than 32,500 strain cycles.Leveraging these advances,we have integrated multilayer circuits,strain sensors,and a three-axis accelerometer to achieve a fully integrated,stretchable,permeable wireless real-time gesture recognition glove.The system enables accurate sign language interpretation(98%)and seamless robotic hand control,demonstrating its potential for assistive technologies.By uniting comfort,durability,and high-density integration,this work establishes a versatile platform for nextgeneration wearable electronics and interactive human-robot interfaces.展开更多
Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame de...Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.展开更多
Multichannel signals have the characteristics of information diversity and information consistency.To better explore and utilize the affinity relationship within multichannel signals,a new graph learning technique bas...Multichannel signals have the characteristics of information diversity and information consistency.To better explore and utilize the affinity relationship within multichannel signals,a new graph learning technique based on low rank tensor approximation is proposed for multichannel monitoring signal processing and utilization.Firstly,the affinity relationship of multichannel signals can be acquired based on the clustering results of each channel signal.Wherein an affinity tensor is constructed to integrate the diverse and consistent information of the clustering information among multichannel signals.Secondly,a low-rank tensor optimization model is built and the joint affinity matrix is optimized with the assistance of the strong confidence affinity matrix.Through solving the optimization model,the fused affinity relationship graph of multichannel signals can be obtained.Finally,the multichannel fused clustering results can be acquired though the updated joint affinity relationship graph.The multichannel signal utilization examples in health state assessment with public datasets and microwave detection with actual echoes verify the advantages and effectiveness of the proposed method.展开更多
The detection of amino acid enantiomers holds significant importance in biomedical,chemical,food,and other fields.Traditional chiral recognition methods using fluorescent probes primarily rely on fluorescence intensit...The detection of amino acid enantiomers holds significant importance in biomedical,chemical,food,and other fields.Traditional chiral recognition methods using fluorescent probes primarily rely on fluorescence intensity changes,which can compromise accuracy and repeatability.In this study,we report a novel fluorescent probe(R)-Z1 that achieves effective enantioselective recognition of chiral amino acids in water by altering emission wavelengths(>60 nm).This water-soluble probe(R)-Z1 exhibits cyan or yellow-green luminescence upon interaction with amino acid enantiomers,enabling reliable chiral detection of 14 natural amino acids.It also allows for the determination of enantiomeric excess through monitoring changes in luminescent color.Additionally,a logic operation with two inputs and three outputs was constructed based on these optical properties.Notably,amino acid enantiomers were successfully detected via dual-channel analysis at both the food and cellular levels.This study provides a new dynamic luminescence-based tool for the accurate sensing and detection of amino acid enantiomers.展开更多
基金The National Natural Science Foundation of China (No70571087)the National Science Fund for Distinguished Young Scholarsof China (No70625005)
文摘The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information.
基金the National Natural Science Foundation of China(Nos.21374058,21474060 and 21574078)the Ph.D. Programs Foundation of Ministry of Education of China(No 201331081100166)the Shanghai Rising-Star Program(No.16QA1401800)
文摘Thermoresponsive biotinylated dendronized copolymers carrying dendritic oligoethylene glycol（OEG）pendants were prepared via free radical polymerization,and their protein recognitions based on biotin-avidin interaction investigated.Both first（PG1） and second generation（PG2） dendronized copolymers were designed to examine possible thickness effects on the interaction between biotin and avidin.Inherited from the outstanding thermoresponsive properties from OEG dendrons,these biotinylated cylindrical copolymers show characteristic thermoresponsive behavior which provides an envelope to capture avidin through switching temperatures above or below their phase transition temperatures（T_（cp）s）.Thus,the recognition of polymer-supported biotin with avidin was investigated with UV/vis spectroscopy and dynamic laser light scattering.In contrast to the case for PG1,the increased thickness for copolymer PG2 hinders partially and inhibits the recognition of biotin moieties with avidin either below or above its T_（cp）.This demonstrates the significant architecture effects from dendronized polymers on the biotin moieties to shift onto periphery of the collapsed aggregates,which should be a prerequisite for protein recognition.These kinds of novel thermoresponsive copolymers may pave a way for the interesting biological applications in areas such as reversible activity control of enzyme or proteins,and for controlled delivery of drugs or genes.
文摘Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R765),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in many applications,such as smart home,healthcare,human computer interaction,sports analysis,and especially,intelligent surveillance.In this paper,we propose a robust and efficient HAR system by leveraging deep learning paradigms,including pre-trained models,CNN architectures,and their average-weighted fusion.However,due to the diversity of human actions and various environmental influences,as well as a lack of data and resources,achieving high recognition accuracy remain elusive.In this work,a weighted average ensemble technique is employed to fuse three deep learning models:EfficientNet,ResNet50,and a custom CNN.The results of this study indicate that using a weighted average ensemble strategy for developing more effective HAR models may be a promising idea for detection and classification of human activities.Experiments by using the benchmark dataset proved that the proposed weighted ensemble approach outperformed existing approaches in terms of accuracy and other key performance measures.The combined average-weighted ensemble of pre-trained and CNN models obtained an accuracy of 98%,compared to 97%,96%,and 95%for the customized CNN,EfficientNet,and ResNet50 models,respectively.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.
基金supported in part by the National Natural Science Foundation of China:61773330.
文摘Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128 and 62306139the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.
文摘Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.
基金National Natural Science Foundation of China(grant number:52375224)Natural Science Foundation of Jiangsu Province(grant number:BK20242086)+2 种基金Priority Academic Program Development of Jiangsu Higher Education Institutions,a project supported by"the Fundamental Research Funds for the Central Universities"(grant number:202410976)Graduate Innovation Program of China University of Mining and Technology(grant number:2024WLKXJ075)Postgraduate Research&Practice Innovation Program of Jiangsu Province(grant number:KYCX24_2719).
文摘Pain,as a common symptom,seriously affects the patient's health.The aim of this work was to study the physiological responses of the brain and identify the features of Electroencephalography(EEG)signals related to friction pain.The results showed that the primary brain activation evoked by friction pain was located in the Prefrontal Cortex(PFC).The activation area decreased,and the negative activation intensity in the PFC region increased with increasing intensity of pain.The inhibitory interactions between different brain regions,especially between the PFC and primary somatosensory cortex(SI)regions were enhanced,and excitatory-inhibitory connections between the medial and lateral pain pathways were balanced during pain perception.The percentage power spectral density of theαrhythm(Dα),dominant singularity strength(αpeak)and longest vertical line(Vmax)of EEG signals induced by pain significantly decreased,and the percent-age power spectral density of theβrhythm(Dβ)significantly increased.The combination of multiple features of Dα,Dβ,αpeak and Vmax could significantly improve the average recognition accuracy of different pain states.This study elucidated the neural processing mechanisms of friction-induced pain,and EEG features associated with friction pain were extracted and recognized.It was helpful to study the brain feedback mechanisms of pain and control signals of Brain-Computer Interface(BCI)system related to pain.
文摘This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals.The proposed framework combines convolutional neural networks(CNNs)to extract spatial and local temporal features with the Transformer encoder that captures long-range dependencies in time-series data through multi-head attention.Model performance was evaluated on two widely used tactile biosignal datasets,HAART and CoST,which contain diverse affective touch gestures recorded from pressure sensor arrays.TheCNN-Transformer model achieved recognition rates of 93.33%on HAART and 80.89%on CoST,outperforming existing methods on both benchmarks.By incorporating temporal windowing,the model enables instantaneous prediction,improving generalization across gestures of varying duration.These results highlight the effectiveness of deep learning for tactile biosignal processing and demonstrate the potential of theCNN-Transformer approach for future applications in wearable sensors,affective computing,and biomedical monitoring.
文摘Before joining the Beijing Dragon Shadow Puppetry Art Theatre,this group of young people rarely had the chance to meet people like themselves,lacked friends,faced obstacles at work,and felt alienated from society.Now,they have gained appreciation,recognition,confidence,friendships,and love.
基金supported by the General Program of the National Natural Science Foundation of China (Grant No.62575116)the National Natural Science Foundation of China (Grant No.62262005)+1 种基金the High-level Innovative Talents in Guizhou Province (Grant No.GCC[2023]033)the Open Project of the Text Computing and Cognitive Intelligence Ministry of Education Engineering Research Center(Grant No.TCCI250208)。
文摘The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.
文摘What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview with this journal,Professor Shen Weirong ofTsinghua University discusses these issues on the basis of his research.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
基金supported in part by the National Key R&D Program of China under Grant 2024YFB4405300 and 2022YFA1204300the Natural Science Foundation of Hunan Province under Grant 2023JJ20016+2 种基金the National Natural Science Foundation of China under Grants of 52221001 and 62090035the Key Research and Development Plan of Hunan Province under grants of 2022GK3002 and 2023GK2012the Key Program of Science and Technology Department of Hunan Province under grant of 2020XK2001。
文摘Permeable electronics promise improved physiological comfort,but remain constrained by limited functional integration and poor mechanical robustness.Here,we report a three-dimensional(3D)permeable electronic system that overcomes these challenges by combining electrospun SEBS nanofiber mats,high-resolution liquid metal conductors patterned via thermal imprinting(50μm),and a strain isolators(SIL)that protects vertical interconnects(VIAs)from stress concentration.This architecture achieves ultrahigh air permeability(>5.09 m L cm^(-2)min^(-1)),exceptional stretchability(750%fracture strain),and reliable conductivity maintained through more than 32,500 strain cycles.Leveraging these advances,we have integrated multilayer circuits,strain sensors,and a three-axis accelerometer to achieve a fully integrated,stretchable,permeable wireless real-time gesture recognition glove.The system enables accurate sign language interpretation(98%)and seamless robotic hand control,demonstrating its potential for assistive technologies.By uniting comfort,durability,and high-density integration,this work establishes a versatile platform for nextgeneration wearable electronics and interactive human-robot interfaces.
基金supported by the Major Program(JD)of Hubei Province under Grant No.2023BAA009the National Natural Science Foundation of China(Grant No.22105162)+1 种基金the Natural Science Foundation of Hubei Province(Grant No.2023AFB623)the Original Exploration Seed Fund of Hubei University。
文摘Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.
基金supported by Shanghai Aerospace Science and Technology Innovation Foundation(SAST2023-075)。
文摘Multichannel signals have the characteristics of information diversity and information consistency.To better explore and utilize the affinity relationship within multichannel signals,a new graph learning technique based on low rank tensor approximation is proposed for multichannel monitoring signal processing and utilization.Firstly,the affinity relationship of multichannel signals can be acquired based on the clustering results of each channel signal.Wherein an affinity tensor is constructed to integrate the diverse and consistent information of the clustering information among multichannel signals.Secondly,a low-rank tensor optimization model is built and the joint affinity matrix is optimized with the assistance of the strong confidence affinity matrix.Through solving the optimization model,the fused affinity relationship graph of multichannel signals can be obtained.Finally,the multichannel fused clustering results can be acquired though the updated joint affinity relationship graph.The multichannel signal utilization examples in health state assessment with public datasets and microwave detection with actual echoes verify the advantages and effectiveness of the proposed method.
基金the financial support from the National Natural Science Foundation of China(Nos.22377097,22307036,22074114)Natural Science Foundation of Hubei Province of China(Nos.2020CFB623,2021CFB556)Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education(No.LCX202305)。
文摘The detection of amino acid enantiomers holds significant importance in biomedical,chemical,food,and other fields.Traditional chiral recognition methods using fluorescent probes primarily rely on fluorescence intensity changes,which can compromise accuracy and repeatability.In this study,we report a novel fluorescent probe(R)-Z1 that achieves effective enantioselective recognition of chiral amino acids in water by altering emission wavelengths(>60 nm).This water-soluble probe(R)-Z1 exhibits cyan or yellow-green luminescence upon interaction with amino acid enantiomers,enabling reliable chiral detection of 14 natural amino acids.It also allows for the determination of enantiomeric excess through monitoring changes in luminescent color.Additionally,a logic operation with two inputs and three outputs was constructed based on these optical properties.Notably,amino acid enantiomers were successfully detected via dual-channel analysis at both the food and cellular levels.This study provides a new dynamic luminescence-based tool for the accurate sensing and detection of amino acid enantiomers.
