Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
Quantum information masking(QIM)is a crucial technique for protecting quantum data from being accessed by local subsystems.In this paper,we introduce a novel method for achieving1-uniform QIM in multipartite systems u...Quantum information masking(QIM)is a crucial technique for protecting quantum data from being accessed by local subsystems.In this paper,we introduce a novel method for achieving1-uniform QIM in multipartite systems utilizing a Fourier matrix.We further extend this approach to construct an orthogonal array with the aid of a Hadamard matrix,which is a specific type of Fourier matrix.This allows us to explore the relationship between 2-uniform QIM and orthogonal arrays.Through this framework,we derive two distinct 2-uniform quantum states,enabling the 2-uniform masking of original information within multipartite systems.Furthermore,we prove that the maximum number of quantum bits required for achieving a2-uniformly masked state is 2^(n)-1,and the minimum is 2^(n-1)+3.Moreover,our scheme effectively demonstrates the rich quantum correlations between multipartite systems and has potential application value in quantum secret sharing.展开更多
The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections.During the COVID-19 pandemic,the use of protective face masks was essential to reduce the...The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections.During the COVID-19 pandemic,the use of protective face masks was essential to reduce the risk of infection and spread of SARS-CoV-2.The face mask is able to significantly reduce the saliva droplet emission in front of the person.However,the use of masks also produces a particle leakage towards the back of the person,which could increase the infection risk of people behind the subject.Most of the experimental investigations applied invasive and/or complex experimental techniques to evaluate the face masks leakage.The primary objective of this study is to develop a novel,non-invasive methodology for assessing rearward droplet emission associated with the use of protective face masks.Specifically,a thermographic analysis of the thermal footprint released during ordinary and extraordinary respiratory activities is presented,evaluating the maximum temperature,the detection time,and the spread area of the thermal footprint.Both surgical and FFP2 face masks were tested.Two different subjects were involved in the experimentation to evaluate the influence of face conformation.The findings indicate that the area influenced by droplet dispersion is larger when wearing a surgical mask compared to an FFP2 mask,with the highest recorded temperatures observed for the surgical mask.The thermal footprint was found to be strongly dependent on individual facial morphology and mask fit.Notably,the FFP2 mask also altered the position of the thermal footprint,which was primarily confined to the region near the neck.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
The accurate segmentation of deep gray matter nuclei is critical for neuropathological research,disease diagnosis and treatment.Existing methods employ the supervised learning training approach,which requires large la...The accurate segmentation of deep gray matter nuclei is critical for neuropathological research,disease diagnosis and treatment.Existing methods employ the supervised learning training approach,which requires large labeled datasets.It is challenging and time-consuming to obtain such datasets for medical image analysis.In addition,these methods based on convolutional neural networks(CNNs)only achieve suboptimal performance due to the locality of convolutional operations.Vision Transformers(ViTs)efficiently model long-range dependencies and thus have the potentiality to outperform these methods in segmentation tasks.To address these issues,we propose a novel hybrid network based on self-supervised pre-training for deep gray matter nuclei segmentation.Specifically,we present a CNN-Transformer hybrid network(CTNet),whose encoder consists of 3D CNN and ViT to learn local spatial-detailed features and global semantic information.A self-supervised learning(SSL)approach that integrates rotation prediction and masked feature reconstruction is proposed to pre-train the CTNet,enabling the model to learn valuable visual representations from unlabeled data.We evaluate the effectiveness of our method on 3T and 7T human brain MRI datasets.The results demonstrate that our CTNet achieves better performance than other comparison models and our pre-training strategy outperforms other advanced self-supervised methods.When the training set has only one sample,our pre-trained CTNet enhances segmentation performance,showing an 8.4%improvement in Dice similarity coefficient(DSC)compared to the randomly initialized CTNet.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
In order to improve the design results for the reconfigurable frequency response masking FRM filters an improved design method based on second-order cone programming SOCP is proposed.Unlike traditional methods that se...In order to improve the design results for the reconfigurable frequency response masking FRM filters an improved design method based on second-order cone programming SOCP is proposed.Unlike traditional methods that separately design the proposed method takes all the desired designing modes into consideration when designing all the subfilters. First an initial solution is obtained by separately designing the subfilters and then the initial solution is updated by iteratively solving a SOCP problem. The proposed method is evaluated on a design example and simulation results demonstrate that jointly designing all the subfilters can obtain significantly lower minimax approximation errors compared to the conventional design method.展开更多
Speech perception is essential for daily communication.Background noise or concurrent talkers,on the other hand,can make it challenging for listeners to track the target speech(i.e.,cocktail party problem).The present...Speech perception is essential for daily communication.Background noise or concurrent talkers,on the other hand,can make it challenging for listeners to track the target speech(i.e.,cocktail party problem).The present study reviews and compares existing findings on speech perception and unmasking in cocktail party listening environments in English and Mandarin Chinese.The review starts with an introduction section followed by related concepts of auditory masking.The next two sections review factors that release speech perception from masking in English and Mandarin Chinese,respectively.The last section presents an overall summary of the findings with comparisons between the two languages.Future research directions with respect to the difference in literature on the reviewed topic between the two languages are also discussed.展开更多
The aim of the study was to taste mask ciprofloxacin(CP)by using ion-exchange resins(IERs)followed by sustain release of CP by forming interpenetrating polymer network(IPN).IERs based on the copolymerization of acryli...The aim of the study was to taste mask ciprofloxacin(CP)by using ion-exchange resins(IERs)followed by sustain release of CP by forming interpenetrating polymer network(IPN).IERs based on the copolymerization of acrylic acid with different cross linking agents were synthesised.Drug-resin complexes(DRCs)with three different ratios of drug to IERs(1:1,1:2,1:4)were prepared&evaluated for taste masking by following in vivo and in vitro methods.Human volunteers graded ADC 1:4,acrylic acid-divinyl benzene(ADC-3)resin as tasteless.Characterization studies such as FTIR,SEM,DSC,P-XRD differentiated ADC 1:4,from physical mixture(PM 1:4)and confirmed the formation of complex.In vitro drug release of ADC 1:4 showed complete release of CP within 60 min at simulated gastric fluid(SGF)i.e.pH 1.2.IPN beads were prepared with ADC 1:4 by using sodium alginate(AL)and sodium alginate-chitosan(AL-CS)for sustain release of CP at SGF pH and followed by simulated intestinal fluid(SIF i.e.pH 7.4).FTIR spectra confirmed the formation of IPN beads.The release of CP was sustain at SGF pH(<20%)whereas in SIF media it was more(>75%).The kinetic model of IPN beads showed the release of CP was non-Fickian diffusion type.展开更多
Typical masking techniques adopted in the conventional secure communication schemes are the additive masking and modulation by multiplication. In order to enhance security, this paper presents a nonlinear masking meth...Typical masking techniques adopted in the conventional secure communication schemes are the additive masking and modulation by multiplication. In order to enhance security, this paper presents a nonlinear masking methodology, applicable to the conventional schemes. In the proposed cryptographic scheme, the plaintext spans over a pre-specified finite-time interval, which is modulated through parameter modulation, and masked chaotically by a nonlinear mechanism. An efficient iterative learning algorithm is exploited for decryption, and the sufficient condition for convergence is derived, by which the learning gain can be chosen. Case studies are conducted to demonstrate the effectiveness of the proposed masking method.展开更多
Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering....Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering.Furthermore,a general maskedbased additive non-homogeneous Poisson process(NHPP) model was considered to analyze component reliability.However,the problem of masked-based additive model lies in the difficulty of estimating parameters.The maximum likelihood estimation procedure was derived to estimate parameters.Finally,a numerical example was given to illustrate the applicability of proposed model,and the immune particle swarm optimization(IPSO) algorithm was used in maximize log-likelihood function.展开更多
This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the ps...This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.展开更多
With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so ...With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.展开更多
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金supported by the National Natural Science Foundation of China under Grant No.12301590Natural Science Foundation of Hebei Province under Grant No.A2022210002。
文摘Quantum information masking(QIM)is a crucial technique for protecting quantum data from being accessed by local subsystems.In this paper,we introduce a novel method for achieving1-uniform QIM in multipartite systems utilizing a Fourier matrix.We further extend this approach to construct an orthogonal array with the aid of a Hadamard matrix,which is a specific type of Fourier matrix.This allows us to explore the relationship between 2-uniform QIM and orthogonal arrays.Through this framework,we derive two distinct 2-uniform quantum states,enabling the 2-uniform masking of original information within multipartite systems.Furthermore,we prove that the maximum number of quantum bits required for achieving a2-uniformly masked state is 2^(n)-1,and the minimum is 2^(n-1)+3.Moreover,our scheme effectively demonstrates the rich quantum correlations between multipartite systems and has potential application value in quantum secret sharing.
文摘The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections.During the COVID-19 pandemic,the use of protective face masks was essential to reduce the risk of infection and spread of SARS-CoV-2.The face mask is able to significantly reduce the saliva droplet emission in front of the person.However,the use of masks also produces a particle leakage towards the back of the person,which could increase the infection risk of people behind the subject.Most of the experimental investigations applied invasive and/or complex experimental techniques to evaluate the face masks leakage.The primary objective of this study is to develop a novel,non-invasive methodology for assessing rearward droplet emission associated with the use of protective face masks.Specifically,a thermographic analysis of the thermal footprint released during ordinary and extraordinary respiratory activities is presented,evaluating the maximum temperature,the detection time,and the spread area of the thermal footprint.Both surgical and FFP2 face masks were tested.Two different subjects were involved in the experimentation to evaluate the influence of face conformation.The findings indicate that the area influenced by droplet dispersion is larger when wearing a surgical mask compared to an FFP2 mask,with the highest recorded temperatures observed for the surgical mask.The thermal footprint was found to be strongly dependent on individual facial morphology and mask fit.Notably,the FFP2 mask also altered the position of the thermal footprint,which was primarily confined to the region near the neck.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported in part by the National Natural Science Foundation of China under Grant 62071405the National Natural Science Foundation of China under Grant 12175189.
文摘The accurate segmentation of deep gray matter nuclei is critical for neuropathological research,disease diagnosis and treatment.Existing methods employ the supervised learning training approach,which requires large labeled datasets.It is challenging and time-consuming to obtain such datasets for medical image analysis.In addition,these methods based on convolutional neural networks(CNNs)only achieve suboptimal performance due to the locality of convolutional operations.Vision Transformers(ViTs)efficiently model long-range dependencies and thus have the potentiality to outperform these methods in segmentation tasks.To address these issues,we propose a novel hybrid network based on self-supervised pre-training for deep gray matter nuclei segmentation.Specifically,we present a CNN-Transformer hybrid network(CTNet),whose encoder consists of 3D CNN and ViT to learn local spatial-detailed features and global semantic information.A self-supervised learning(SSL)approach that integrates rotation prediction and masked feature reconstruction is proposed to pre-train the CTNet,enabling the model to learn valuable visual representations from unlabeled data.We evaluate the effectiveness of our method on 3T and 7T human brain MRI datasets.The results demonstrate that our CTNet achieves better performance than other comparison models and our pre-training strategy outperforms other advanced self-supervised methods.When the training set has only one sample,our pre-trained CTNet enhances segmentation performance,showing an 8.4%improvement in Dice similarity coefficient(DSC)compared to the randomly initialized CTNet.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金The National Natural Science Foundation of China(No.61231002,61273266,61375028)the Ph.D.Programs Foundation of Ministry of Education of China(No.20110092130004)
文摘In order to improve the design results for the reconfigurable frequency response masking FRM filters an improved design method based on second-order cone programming SOCP is proposed.Unlike traditional methods that separately design the proposed method takes all the desired designing modes into consideration when designing all the subfilters. First an initial solution is obtained by separately designing the subfilters and then the initial solution is updated by iteratively solving a SOCP problem. The proposed method is evaluated on a design example and simulation results demonstrate that jointly designing all the subfilters can obtain significantly lower minimax approximation errors compared to the conventional design method.
文摘Speech perception is essential for daily communication.Background noise or concurrent talkers,on the other hand,can make it challenging for listeners to track the target speech(i.e.,cocktail party problem).The present study reviews and compares existing findings on speech perception and unmasking in cocktail party listening environments in English and Mandarin Chinese.The review starts with an introduction section followed by related concepts of auditory masking.The next two sections review factors that release speech perception from masking in English and Mandarin Chinese,respectively.The last section presents an overall summary of the findings with comparisons between the two languages.Future research directions with respect to the difference in literature on the reviewed topic between the two languages are also discussed.
文摘The aim of the study was to taste mask ciprofloxacin(CP)by using ion-exchange resins(IERs)followed by sustain release of CP by forming interpenetrating polymer network(IPN).IERs based on the copolymerization of acrylic acid with different cross linking agents were synthesised.Drug-resin complexes(DRCs)with three different ratios of drug to IERs(1:1,1:2,1:4)were prepared&evaluated for taste masking by following in vivo and in vitro methods.Human volunteers graded ADC 1:4,acrylic acid-divinyl benzene(ADC-3)resin as tasteless.Characterization studies such as FTIR,SEM,DSC,P-XRD differentiated ADC 1:4,from physical mixture(PM 1:4)and confirmed the formation of complex.In vitro drug release of ADC 1:4 showed complete release of CP within 60 min at simulated gastric fluid(SGF)i.e.pH 1.2.IPN beads were prepared with ADC 1:4 by using sodium alginate(AL)and sodium alginate-chitosan(AL-CS)for sustain release of CP at SGF pH and followed by simulated intestinal fluid(SIF i.e.pH 7.4).FTIR spectra confirmed the formation of IPN beads.The release of CP was sustain at SGF pH(<20%)whereas in SIF media it was more(>75%).The kinetic model of IPN beads showed the release of CP was non-Fickian diffusion type.
基金supported by National Natural Science Foundation of China(No.61174034)
文摘Typical masking techniques adopted in the conventional secure communication schemes are the additive masking and modulation by multiplication. In order to enhance security, this paper presents a nonlinear masking methodology, applicable to the conventional schemes. In the proposed cryptographic scheme, the plaintext spans over a pre-specified finite-time interval, which is modulated through parameter modulation, and masked chaotically by a nonlinear mechanism. An efficient iterative learning algorithm is exploited for decryption, and the sufficient condition for convergence is derived, by which the learning gain can be chosen. Case studies are conducted to demonstrate the effectiveness of the proposed masking method.
基金Technology Foundation of Guizhou Province,China(No.QianKeHeJZi[2015]2064)Scientific Research Foundation for Advanced Talents in Guizhou Institue of Technology and Science,China(No.XJGC20150106)Joint Foundation of Guizhou Province,China(No.QianKeHeLHZi[2015]7105)
文摘Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering.Furthermore,a general maskedbased additive non-homogeneous Poisson process(NHPP) model was considered to analyze component reliability.However,the problem of masked-based additive model lies in the difficulty of estimating parameters.The maximum likelihood estimation procedure was derived to estimate parameters.Finally,a numerical example was given to illustrate the applicability of proposed model,and the immune particle swarm optimization(IPSO) algorithm was used in maximize log-likelihood function.
基金partially supported by the National Natural Science Foundation of China (Nos.11590772, 11590770)the Pre-research Project for Equipment of General Information System (No.JZX2017-0994/Y306)
文摘This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.
基金This research was supported by the National Natural Science Foundation of China under Grant(No.42050102)the Postgraduate Education Reform Project of Jiangsu Province under Grant(No.SJCX22_0343)Also,this research was supported by Dou Wanchun Expert Workstation of Yunnan Province(No.202205AF150013).
文摘With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.
