Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground,independent of theoretical hypotheses and engineering experience.A series of field tests was...This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground,independent of theoretical hypotheses and engineering experience.A series of field tests was first performed to investigate the characteristics of the shaft resistance of cast-in-site piles on reclaimed ground.Then,an intelligent approach based on the long short term memory deep-learning technique was proposed to calculate the shaft resistance of the cast-in-site pile.The proposed method allows accurate estimation of the shaft resistance of cast-in-site piles,not only under the ultimate load but also under the working load.Comparisons with empirical methods confirmed the effectiveness of the proposed method for the shaft resistance estimation of cast-in-site piles on reclaimed ground in offshore areas.展开更多
The number of films is numerous and the film contents are complex over the Internet and multimedia sources. It is time consuming for a viewer to select a favorite film. This paper presents an automatic recognition sys...The number of films is numerous and the film contents are complex over the Internet and multimedia sources. It is time consuming for a viewer to select a favorite film. This paper presents an automatic recognition system of film types. Initially, a film is firstly sampled as frame sequences. The color space, including hue, saturation,and brightness value(HSV), is analyzed for each sampled frame by computing the deviation and mean of HSV for each film. These features are utilized as inputs to a deep-learning neural network(DNN) for the recognition of film types. One hundred films are utilized to train and validate the model parameters of DNN. In the testing phase, a film is recognized as one of the five categories, including action, comedy, horror thriller, romance, and science fiction, by the trained DNN. The experimental results reveal that the film types can be effectively recognized by the proposed approach, enabling the viewer to select an interesting film accurately and quickly.展开更多
Using first-principles-based machine-learning potential,molecular dynamics(MD)simulations are performed to investigate the micro-mechanism in phase transition of NbO_(2).Treating the DFT results of the low-and interme...Using first-principles-based machine-learning potential,molecular dynamics(MD)simulations are performed to investigate the micro-mechanism in phase transition of NbO_(2).Treating the DFT results of the low-and intermediate-temperature phases of NbO_(2)as training data in the deep-learning model,we successfully constructed an interatomic potential capable of accurately reproducing the phase transitions from low-temperature(pressure)to high-temperature(pressure)regimes.Notably,our simulations predict a high-pressure monoclinic phase(>14 GPa)without treating its information in the training set,consistent with previous experimental findings,demonstrating the reliability of the constructed interatomic potential.We identified the Nb-dimers as the key structural motif governing the phase transitions.At low temperatures,the displacements of the Nb-dimers drive the transition between the I41=a(α-NbO_(2))and I41(β-NbO_(2))phases,while at high temperatures,Nb ions are prone to being equally distributed and the disappearance of Nb-dimers leads to the stabilization of a high-symmetry P4_(2)=mnm phase.These findings elucidate the structural and dynamical mechanisms underlying the structural properties of NbO_(2)and highlight the utility of combining DFT and deep potential MD methods for studying complex phase transitions in transition metal oxides.展开更多
Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we ...Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.展开更多
Dear Editor,Insulin is important for body metabolism regulation and glucose homeostasis,and its dysregulation often leads to metabolic syndrome(MS)and diabetes.Insulin is normally stored in large dense-core vesicles(L...Dear Editor,Insulin is important for body metabolism regulation and glucose homeostasis,and its dysregulation often leads to metabolic syndrome(MS)and diabetes.Insulin is normally stored in large dense-core vesicles(LDCVs)in pancreatic beta cells,and significant reductions in the number,size,gray level and density of insulin granules confer diabetes both in mice(Xue et al.,2012)and humans(Masini et al.,2012).展开更多
Accurately predicting the concentration of fine particulate matter(PM_(2.5))is crucial for evaluating air pollution levels and public exposure.Recent advancements have seen a significant rise in using deep learning(DL...Accurately predicting the concentration of fine particulate matter(PM_(2.5))is crucial for evaluating air pollution levels and public exposure.Recent advancements have seen a significant rise in using deep learning(DL)models for forecasting PM_(2.5) concentrations.Nonetheless,there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM_(2.5) prediction models.Here we extensively reviewed those DL-based hybrid models for forecasting PM_(2.5) levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines.We examined the similarities and differences among various DL models in predicting PM_(2.5) by comparing their complexity and effectiveness.We categorized PM_(2.5) DL methodologies into seven types based on performance and application conditions,including four types of DL-based models and three types of hybrid learning models.Our research indicates that established deep learning architectures are commonly used and respected for their efficiency.However,many of these models often fall short in terms of innovation and interpretability.Conversely,models hybrid with traditional approaches,like deterministic and statistical models,exhibit high interpretability but compromise on accuracy and speed.Besides,hybrid DL models,representing the pinnacle of innovation among the studied models,encounter issues with interpretability.We introduce a novel three-dimensional evaluation framework,i.e.,Dataset-MethodExperiment Standard(DMES)to unify and standardize the evaluation for PM_(2.5) predictions using DL models.This review provides a framework for future evaluations of DL-based models,which could inspire researchers to standardize DL model usage in PM_(2.5) prediction and improve the quality of related studies.展开更多
Gesture recognition utilizing flexible strain sensors is a highly valuable technology widely applied in human-machine interfaces.However,achieving rapid detection of subtle motions and timely processing of dynamic sig...Gesture recognition utilizing flexible strain sensors is a highly valuable technology widely applied in human-machine interfaces.However,achieving rapid detection of subtle motions and timely processing of dynamic signals remain a challenge for sensors.Here,highly resilient and durable ionogels are developed by introducing micro-scale incompatible phases in macroscopic homogeneous polymeric network.The compatible network disperses in conductive ionic liquid to form highly resilient and stretchable skeleton,while incompatible phase forms hydrogen bonds to dissipate energy thus strengthening the ionogels.The ionogels-derived strain sensors show highly sensitivity,fast response time(<10 ms),low detection limit(~50μm),and remarkable durability(>5000 cycles),allowing for precise monitoring of human motions.More importantly,a self-adaptive recognition program empowered by deep-learning algorithms is designed to compensate for sensors,creating a comprehensive system capable of dynamic gesture recognition.This system can comprehensively analyze both the temporal and spatial features of sensor data,enabling deeper understanding of the dynamic process underlying gestures.The system accurately classifies 10 hand gestures across five participants with impressive accuracy of 93.66%.Moreover,it maintains robust recognition performance without the need for further training even when different sensors or subjects are involved.This technological breakthrough paves the way for intuitive and seamless interaction between humans and machines,presenting significant opportunities in diverse applications,such as human-robot interaction,virtual reality control,and assistive devices for the disabled individuals.展开更多
Recently,wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities,which emphasize modularization,simplification,and weight reductio...Recently,wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities,which emphasize modularization,simplification,and weight reduction.Thus,synchronizing the robotic assistive force with that of the user’s leg movements is crucial for usability,which requires accurate recognition of the user’s gait intent.In this study,we propose a deep learning model capable of identifying not only gait mode and gait phase but also phase progression.Utilizing data from five inertial measurement units placed on the body,the proposed two-stage architecture incorporates a bidirectional long short-term memory-based model for robust classification of locomotion modes and phases.Subsequently,phase progression is estimated through 1D convolutional neural network-based regressors,each dedicated to a specific phase.The model was evaluated on a diverse dataset encompassing level walking,stair ascent and descent,and sit-to-stand activities from 10 healthy participants.The results demonstrate its ability to accurately classify locomotion phases and estimate phase progression.Accurate phase progression estimation is essential due to the age-related variability in gait phase durations,particularly evident in older adults,the primary demographic for gait-assist robots.These findings underscore the potential to enhance the assistance,comfort,and safety provided by gait-assist robots.展开更多
With the rapid development of deep learning neural networks,new solutions have emerged for addressing fluid flow problems in porous media.Combining data-driven approaches with physical constraints has become a hot res...With the rapid development of deep learning neural networks,new solutions have emerged for addressing fluid flow problems in porous media.Combining data-driven approaches with physical constraints has become a hot research direction,with physics-informed neural networks(PINNs) being the most popular hybrid model.PINNs have gained widespread attention in subsurface fluid flow simulations due to their low computational resource requirements,fast training speeds,strong generalization capabilities,and broad applicability.Despite success in homogeneous settings,standard PINNs face challenges in accurately calculating flux between irregular Eulerian cells with disparate properties and capturing global field influences on local cells.This limits their suitability for heterogeneous reservoirs and the irregular Eulerian grids frequently used in reservoir.To address these challenges,this study proposes a physics-informed graph neural network(PIGNN) model.The PIGNN model treats the entire field as a whole,integrating information from neighboring grids and physical laws into the solution for the target grid,thereby improving the accuracy of solving partial differential equations in heterogeneous and Eulerian irregular grids.The optimized model was applied to pressure field prediction in a spatially heterogeneous reservoir,achieving an average L_(2) error and R_(2) score of 6.710×10^(-4)and 0.998,respectively,which confirms the effectiveness of model.Compared to the conventional PINN model,the average L_(2) error was reduced by 76.93%,the average R_(2) score increased by 3.56%.Moreover,evaluating robustness,training the PIGNN model using only 54% and 76% of the original data yielded average relative L_(2) error reductions of 58.63% and 56.22%,respectively,compared to the PINN model.These results confirm the superior performance of this approach compared to PINN.展开更多
The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typic...The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.展开更多
Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the nee...Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.展开更多
Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the p...Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the personal experience of pharmaceutical experts andtrial-and-error in the laboratory, which is inefficient and time-consuming. The aim of cur-rent research was to establish the prediction model of ODT formulations with direct com-pression process by artificial neural network(ANN) and deep neural network(DNN) tech-niques. 145 formulation data were extracted from Web of Science. All datasets were dividedinto three parts: training set(105 data), validation set(20) and testing set(20). ANN andDNN were compared for the prediction of the disintegrating time. The accuracy of the ANNmodel have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testingset respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respec-tively. Compared with the ANN, DNN showed the better prediction for ODT formulations.It is the first time that deep neural network with the improved dataset selection algorithmis applied to formulation prediction on small data. The proposed predictive approach couldevaluate the critical parameters about quality control of formulation, and guide researchand process development. The implementation of this prediction model could effectivelyreduce drug product development timeline and material usage, and proactively facilitatethe development of a robust drug product.展开更多
Prevention is the most effective way to reduce dental caries.In order to provide a simple way to achieve oral healthcare direction in daily life,dual Channel,portable dental Imaging system that combine white light wit...Prevention is the most effective way to reduce dental caries.In order to provide a simple way to achieve oral healthcare direction in daily life,dual Channel,portable dental Imaging system that combine white light with autofluorescence techniques was established,and then,a group of volunteers were recruited,7200 tooth pictures of different dental caries stage and dental plaque were taken and collected.In this work,a customized Convolutional Neural Networks(CNNs)have been designed to classify dental image with early stage caries and dental plaque.Eighty percentage(n=6000)of the pictures taken were used to supervised training of the CNNs based on the experienced dentists'advice and the rest 20%(n=1200)were used to a test dataset to test the trained CNNs.The accuracy,sensitivity and specificity were calculated to evaluate perfor-mance of the CNNs.The accuracy for the early stage caries and dental plaque were 95.3%and 95.9%,respectively.These results shown that the designed image system combined the cus-tomized CNNs that could automatically and efficiently find early caries and dental plaque on occlusal,lingual and buccal surfaces.Therefore,this will provide a novel approach to dental caries prevention for everyone in daily life.展开更多
The representation of spatial variation of soil properties in the form of random fields permits advanced probabilistic assessment of slope stability.In many studies,the safety margin of the system is typically charact...The representation of spatial variation of soil properties in the form of random fields permits advanced probabilistic assessment of slope stability.In many studies,the safety margin of the system is typically characterized by the term“probability of failure(Pfailure)”.As the intensity and spatial distribution of soil properties vary in different random field realizations,the failure mechanism and deformation field of a slope can vary as well.Not only can the location of the failure surfaces vary,but the mode of failure also changes.Such information is equally valuable to engineering practitioners.In this paper,two slope examples that are modified from a real case study are presented.The first example pertains to the stability analysis of a multi-layer-slope while the second example deals with the serviceability analysis of a multi-layer c-φslope.In addition,due to the large number of simulations needed to reveal the full picture of the failure mechanism,Convolutional Neural Networks(CNNs)that adopt a U-Net architecture is proposed to offer a soft computing strategy to facilitate the investigation.The spatial distribution of the failure surfaces,the statistics of the sliding volume,and the statistics of the deformation field are presented.The results also show that the proposed deep-learning model is effective in predicting the failure mechanism and deformation field of slopes in spatially variable soils;therefore encouraging probabilistic study of slopes in practical scenarios.展开更多
This paper proposes a simple and powerful optimal integration(OPI)method for improving hourly quantitative precipitation forecasts(QPFs,0-24 h)of a single-model by integrating the benefits of different biascorrected m...This paper proposes a simple and powerful optimal integration(OPI)method for improving hourly quantitative precipitation forecasts(QPFs,0-24 h)of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration(CMA).Three techniques are used to generate multi-method calibrated members for OPI:deep neural network(DNN),frequency-matching(FM),and optimal threat score(OTS).The results are as follows:(1)The QPF using DNN follows the basic physical patterns of CMA-GD.Despite providing superior improvements for clear-rainy and weak precipitation,DNN cannot improve the predictions for severe precipitation,while OTS can significantly strengthen these predictions.As a result,DNN and OTS are the optimal members to be incorporated into OPI.(2)Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation.Compared with the CMA-GD,OPI improves the TS by 2.5%,5.4%,7.8%,8.3%,and 6.1%for QPFs from clear-rainy to rainstorms in the verification dataset.Moreover,OPI shows good stability in the test dataset.(3)It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation.Therefore,improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability.展开更多
Automatic cell counting provides an effective tool for medical research and diagnosis.Currently,cell counting can be completed by transmitted-light microscope,however,it requires expert knowledge and the counting accu...Automatic cell counting provides an effective tool for medical research and diagnosis.Currently,cell counting can be completed by transmitted-light microscope,however,it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells.Further,the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope,automatically and effectively.In this work,a new deep-learning(DL)-based two-stage detection method(cGAN-YOLO)is designed to further enhance the performance of cell counting,which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model.The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images.Compared with the previously reported YOLO-based one-stage detection method,high recognition accuracy(RA)is achieved by the cGAN-YOLO method,with an improvement of 29.80%.Furthermore,we can also observe that cGAN-YOLO obtains an improvement of 12.11%in RA compared with the previously reported image-translation-based detection method.In a word,cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance,which extends the applicability in clinical research.展开更多
Device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals,it may empower wireless networks with the augmented s...Device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals,it may empower wireless networks with the augmented sensing ability.Researchers have made great achievements for singleperson device-free gesture recognition.However,when multiple persons conduct gestures simultaneously,the received signals will be mixed together,and thus traditional methods would not work well anymore.Moreover,the anonymity of persons and the change in the surrounding environment would cause feature shift and mismatch,and thus the recognition accuracy would degrade remarkably.To address these problems,we explore and exploit the diversity of spatial information and propose a multidimensional analysis method to separate the gesture feature of each person using a focusing sensing strategy.Meanwhile,we also present a deep-learning based robust device free gesture recognition framework,which leverages an adversarial approach to extract robust gesture feature that is insensitive to the change of persons and environment.Furthermore,we also develop a 77GHz mmWave prototype system and evaluate the proposed methods extensively.Experimental results reveal that the proposed system can achieve average accuracies of 93%and 84%when 10 gestures are conducted in Received:Jun.18,2020 Revised:Aug.06,2020 Editor:Ning Ge different environments by two and four persons simultaneously,respectively.展开更多
Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional ...Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.展开更多
With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own ...With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.展开更多
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金the Research Funding of Shantou University for New Faculty Member(No.NTF19024-2019)the National Nature Science Foundation of China(No.41372283)。
文摘This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground,independent of theoretical hypotheses and engineering experience.A series of field tests was first performed to investigate the characteristics of the shaft resistance of cast-in-site piles on reclaimed ground.Then,an intelligent approach based on the long short term memory deep-learning technique was proposed to calculate the shaft resistance of the cast-in-site pile.The proposed method allows accurate estimation of the shaft resistance of cast-in-site piles,not only under the ultimate load but also under the working load.Comparisons with empirical methods confirmed the effectiveness of the proposed method for the shaft resistance estimation of cast-in-site piles on reclaimed ground in offshore areas.
基金supported by MOST under Grant No.MOST 104-2221-E-468-007。
文摘The number of films is numerous and the film contents are complex over the Internet and multimedia sources. It is time consuming for a viewer to select a favorite film. This paper presents an automatic recognition system of film types. Initially, a film is firstly sampled as frame sequences. The color space, including hue, saturation,and brightness value(HSV), is analyzed for each sampled frame by computing the deviation and mean of HSV for each film. These features are utilized as inputs to a deep-learning neural network(DNN) for the recognition of film types. One hundred films are utilized to train and validate the model parameters of DNN. In the testing phase, a film is recognized as one of the five categories, including action, comedy, horror thriller, romance, and science fiction, by the trained DNN. The experimental results reveal that the film types can be effectively recognized by the proposed approach, enabling the viewer to select an interesting film accurately and quickly.
基金support from the National Natural Science Foundation of China(Grant No.12422407)support from the National Natural Science Foundation of China(Grant No.12204496)+1 种基金the Zhejiang Provincial Natural Science Foundation(Grant No.Q23A040003)Ningbo Nature Science Foundation(No.2023J360)。
文摘Using first-principles-based machine-learning potential,molecular dynamics(MD)simulations are performed to investigate the micro-mechanism in phase transition of NbO_(2).Treating the DFT results of the low-and intermediate-temperature phases of NbO_(2)as training data in the deep-learning model,we successfully constructed an interatomic potential capable of accurately reproducing the phase transitions from low-temperature(pressure)to high-temperature(pressure)regimes.Notably,our simulations predict a high-pressure monoclinic phase(>14 GPa)without treating its information in the training set,consistent with previous experimental findings,demonstrating the reliability of the constructed interatomic potential.We identified the Nb-dimers as the key structural motif governing the phase transitions.At low temperatures,the displacements of the Nb-dimers drive the transition between the I41=a(α-NbO_(2))and I41(β-NbO_(2))phases,while at high temperatures,Nb ions are prone to being equally distributed and the disappearance of Nb-dimers leads to the stabilization of a high-symmetry P4_(2)=mnm phase.These findings elucidate the structural and dynamical mechanisms underlying the structural properties of NbO_(2)and highlight the utility of combining DFT and deep potential MD methods for studying complex phase transitions in transition metal oxides.
基金supported by the Basic Science Center Project of National Natural Science Foundation of China(52388201)the National Natural Science Foundation of China(12334003)+4 种基金the National Science Fund for Distinguished Young Scholars(12025405)the National Key Basic Research and Development Program of China(2023YFA1406400)the Beijing Advanced Innovation Center for Future Chip(ICFC)the Beijing Advanced Innovation Center for Materials Genome Engineeringfunded by the Shuimu Tsinghua Scholar program。
文摘Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.
基金This work was supported by grants from the National Key R&D Program of China(Grant Nos.2017YFA0504700 and 2016YFA0500400)the National Natural Science Foundation of China(Grant Nos.31570839,31661143041,61472395,31327901,31521062 and 31730054)+1 种基金the Beijing Natural Science Foundation(L172003)Joint Program between Chinese Academy of Sciences and Peking University.
文摘Dear Editor,Insulin is important for body metabolism regulation and glucose homeostasis,and its dysregulation often leads to metabolic syndrome(MS)and diabetes.Insulin is normally stored in large dense-core vesicles(LDCVs)in pancreatic beta cells,and significant reductions in the number,size,gray level and density of insulin granules confer diabetes both in mice(Xue et al.,2012)and humans(Masini et al.,2012).
基金supported by the Fundamental Research Funds for the Central Public-interest Scientific Institution(2022YSKY-73).
文摘Accurately predicting the concentration of fine particulate matter(PM_(2.5))is crucial for evaluating air pollution levels and public exposure.Recent advancements have seen a significant rise in using deep learning(DL)models for forecasting PM_(2.5) concentrations.Nonetheless,there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM_(2.5) prediction models.Here we extensively reviewed those DL-based hybrid models for forecasting PM_(2.5) levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines.We examined the similarities and differences among various DL models in predicting PM_(2.5) by comparing their complexity and effectiveness.We categorized PM_(2.5) DL methodologies into seven types based on performance and application conditions,including four types of DL-based models and three types of hybrid learning models.Our research indicates that established deep learning architectures are commonly used and respected for their efficiency.However,many of these models often fall short in terms of innovation and interpretability.Conversely,models hybrid with traditional approaches,like deterministic and statistical models,exhibit high interpretability but compromise on accuracy and speed.Besides,hybrid DL models,representing the pinnacle of innovation among the studied models,encounter issues with interpretability.We introduce a novel three-dimensional evaluation framework,i.e.,Dataset-MethodExperiment Standard(DMES)to unify and standardize the evaluation for PM_(2.5) predictions using DL models.This review provides a framework for future evaluations of DL-based models,which could inspire researchers to standardize DL model usage in PM_(2.5) prediction and improve the quality of related studies.
基金supported by the National Key Research and Development Program of China(No.2021YFA1401103)the National Natural Science Foundation of China(Nos.61825403,61921005,and 82370520).
文摘Gesture recognition utilizing flexible strain sensors is a highly valuable technology widely applied in human-machine interfaces.However,achieving rapid detection of subtle motions and timely processing of dynamic signals remain a challenge for sensors.Here,highly resilient and durable ionogels are developed by introducing micro-scale incompatible phases in macroscopic homogeneous polymeric network.The compatible network disperses in conductive ionic liquid to form highly resilient and stretchable skeleton,while incompatible phase forms hydrogen bonds to dissipate energy thus strengthening the ionogels.The ionogels-derived strain sensors show highly sensitivity,fast response time(<10 ms),low detection limit(~50μm),and remarkable durability(>5000 cycles),allowing for precise monitoring of human motions.More importantly,a self-adaptive recognition program empowered by deep-learning algorithms is designed to compensate for sensors,creating a comprehensive system capable of dynamic gesture recognition.This system can comprehensively analyze both the temporal and spatial features of sensor data,enabling deeper understanding of the dynamic process underlying gestures.The system accurately classifies 10 hand gestures across five participants with impressive accuracy of 93.66%.Moreover,it maintains robust recognition performance without the need for further training even when different sensors or subjects are involved.This technological breakthrough paves the way for intuitive and seamless interaction between humans and machines,presenting significant opportunities in diverse applications,such as human-robot interaction,virtual reality control,and assistive devices for the disabled individuals.
基金supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:RS-2022-KH129263).
文摘Recently,wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities,which emphasize modularization,simplification,and weight reduction.Thus,synchronizing the robotic assistive force with that of the user’s leg movements is crucial for usability,which requires accurate recognition of the user’s gait intent.In this study,we propose a deep learning model capable of identifying not only gait mode and gait phase but also phase progression.Utilizing data from five inertial measurement units placed on the body,the proposed two-stage architecture incorporates a bidirectional long short-term memory-based model for robust classification of locomotion modes and phases.Subsequently,phase progression is estimated through 1D convolutional neural network-based regressors,each dedicated to a specific phase.The model was evaluated on a diverse dataset encompassing level walking,stair ascent and descent,and sit-to-stand activities from 10 healthy participants.The results demonstrate its ability to accurately classify locomotion phases and estimate phase progression.Accurate phase progression estimation is essential due to the age-related variability in gait phase durations,particularly evident in older adults,the primary demographic for gait-assist robots.These findings underscore the potential to enhance the assistance,comfort,and safety provided by gait-assist robots.
基金supported by the National Natural Science Foundation of China (No. 52274048)Beijing Natural Science Foundation (No. 3222037)。
文摘With the rapid development of deep learning neural networks,new solutions have emerged for addressing fluid flow problems in porous media.Combining data-driven approaches with physical constraints has become a hot research direction,with physics-informed neural networks(PINNs) being the most popular hybrid model.PINNs have gained widespread attention in subsurface fluid flow simulations due to their low computational resource requirements,fast training speeds,strong generalization capabilities,and broad applicability.Despite success in homogeneous settings,standard PINNs face challenges in accurately calculating flux between irregular Eulerian cells with disparate properties and capturing global field influences on local cells.This limits their suitability for heterogeneous reservoirs and the irregular Eulerian grids frequently used in reservoir.To address these challenges,this study proposes a physics-informed graph neural network(PIGNN) model.The PIGNN model treats the entire field as a whole,integrating information from neighboring grids and physical laws into the solution for the target grid,thereby improving the accuracy of solving partial differential equations in heterogeneous and Eulerian irregular grids.The optimized model was applied to pressure field prediction in a spatially heterogeneous reservoir,achieving an average L_(2) error and R_(2) score of 6.710×10^(-4)and 0.998,respectively,which confirms the effectiveness of model.Compared to the conventional PINN model,the average L_(2) error was reduced by 76.93%,the average R_(2) score increased by 3.56%.Moreover,evaluating robustness,training the PIGNN model using only 54% and 76% of the original data yielded average relative L_(2) error reductions of 58.63% and 56.22%,respectively,compared to the PINN model.These results confirm the superior performance of this approach compared to PINN.
基金supported by the National Natural Science Foundation of China (Grant No. 42288101, 42375062, 42476192, 42275158)the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)the GHfund C (202407036001)
文摘The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.
文摘Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
基金financially supported by the Universityof Macao Research Grant (MYRG2016-00038-ICMS-QRCM &MYRG2016-00040-ICMS-QRCM)Macao Science and Technology Development Fund (FDCT) (Grant No. 103/2015/A3)the National Natural Science Foundation of China (Grant No. 61562011 )
文摘Oral disintegrating tablets(ODTs) are a novel dosage form that can be dissolved on thetongue within 3 min or less especially for geriatric and pediatric patients. Current ODT for-mulation studies usually rely on the personal experience of pharmaceutical experts andtrial-and-error in the laboratory, which is inefficient and time-consuming. The aim of cur-rent research was to establish the prediction model of ODT formulations with direct com-pression process by artificial neural network(ANN) and deep neural network(DNN) tech-niques. 145 formulation data were extracted from Web of Science. All datasets were dividedinto three parts: training set(105 data), validation set(20) and testing set(20). ANN andDNN were compared for the prediction of the disintegrating time. The accuracy of the ANNmodel have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testingset respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respec-tively. Compared with the ANN, DNN showed the better prediction for ODT formulations.It is the first time that deep neural network with the improved dataset selection algorithmis applied to formulation prediction on small data. The proposed predictive approach couldevaluate the critical parameters about quality control of formulation, and guide researchand process development. The implementation of this prediction model could effectivelyreduce drug product development timeline and material usage, and proactively facilitatethe development of a robust drug product.
基金supported by National Natural Science Foundation of China 61775140
文摘Prevention is the most effective way to reduce dental caries.In order to provide a simple way to achieve oral healthcare direction in daily life,dual Channel,portable dental Imaging system that combine white light with autofluorescence techniques was established,and then,a group of volunteers were recruited,7200 tooth pictures of different dental caries stage and dental plaque were taken and collected.In this work,a customized Convolutional Neural Networks(CNNs)have been designed to classify dental image with early stage caries and dental plaque.Eighty percentage(n=6000)of the pictures taken were used to supervised training of the CNNs based on the experienced dentists'advice and the rest 20%(n=1200)were used to a test dataset to test the trained CNNs.The accuracy,sensitivity and specificity were calculated to evaluate perfor-mance of the CNNs.The accuracy for the early stage caries and dental plaque were 95.3%and 95.9%,respectively.These results shown that the designed image system combined the cus-tomized CNNs that could automatically and efficiently find early caries and dental plaque on occlusal,lingual and buccal surfaces.Therefore,this will provide a novel approach to dental caries prevention for everyone in daily life.
基金supported by the National Natural Science Foundation of China (grant Nos.52130805)China National Postdoctoral Program for Innovative Talents (BX20220234)Shanghai Science and Technology Committee Program (20dz1202200)。
文摘The representation of spatial variation of soil properties in the form of random fields permits advanced probabilistic assessment of slope stability.In many studies,the safety margin of the system is typically characterized by the term“probability of failure(Pfailure)”.As the intensity and spatial distribution of soil properties vary in different random field realizations,the failure mechanism and deformation field of a slope can vary as well.Not only can the location of the failure surfaces vary,but the mode of failure also changes.Such information is equally valuable to engineering practitioners.In this paper,two slope examples that are modified from a real case study are presented.The first example pertains to the stability analysis of a multi-layer-slope while the second example deals with the serviceability analysis of a multi-layer c-φslope.In addition,due to the large number of simulations needed to reveal the full picture of the failure mechanism,Convolutional Neural Networks(CNNs)that adopt a U-Net architecture is proposed to offer a soft computing strategy to facilitate the investigation.The spatial distribution of the failure surfaces,the statistics of the sliding volume,and the statistics of the deformation field are presented.The results also show that the proposed deep-learning model is effective in predicting the failure mechanism and deformation field of slopes in spatially variable soils;therefore encouraging probabilistic study of slopes in practical scenarios.
基金Open Project Fund of Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,CMA(J202009)Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province(SZKT202005)Innovation and Development Project of China Meteorological Administration(CXFZ2021J020)。
文摘This paper proposes a simple and powerful optimal integration(OPI)method for improving hourly quantitative precipitation forecasts(QPFs,0-24 h)of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration(CMA).Three techniques are used to generate multi-method calibrated members for OPI:deep neural network(DNN),frequency-matching(FM),and optimal threat score(OTS).The results are as follows:(1)The QPF using DNN follows the basic physical patterns of CMA-GD.Despite providing superior improvements for clear-rainy and weak precipitation,DNN cannot improve the predictions for severe precipitation,while OTS can significantly strengthen these predictions.As a result,DNN and OTS are the optimal members to be incorporated into OPI.(2)Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation.Compared with the CMA-GD,OPI improves the TS by 2.5%,5.4%,7.8%,8.3%,and 6.1%for QPFs from clear-rainy to rainstorms in the verification dataset.Moreover,OPI shows good stability in the test dataset.(3)It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation.Therefore,improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability.
基金supported by the National Natural Science Foundation of China under Grant Nos.12274092,61871263,and 12034005partially by the Explorer Program of Shanghai under Grant No.21TS1400200+1 种基金partially by Natural Science Foundation of Shanghai under Grant No.21ZR1405200partially by Medical Engineering Fund of Fudan University under Grant No.YG2022-6.Mengyang Lu and Wei Shi contributed equally to this work.
文摘Automatic cell counting provides an effective tool for medical research and diagnosis.Currently,cell counting can be completed by transmitted-light microscope,however,it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells.Further,the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope,automatically and effectively.In this work,a new deep-learning(DL)-based two-stage detection method(cGAN-YOLO)is designed to further enhance the performance of cell counting,which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model.The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images.Compared with the previously reported YOLO-based one-stage detection method,high recognition accuracy(RA)is achieved by the cGAN-YOLO method,with an improvement of 29.80%.Furthermore,we can also observe that cGAN-YOLO obtains an improvement of 12.11%in RA compared with the previously reported image-translation-based detection method.In a word,cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance,which extends the applicability in clinical research.
基金This work was supported by National Natural Science Foundation of China under grants U1933104 and 62071081LiaoNing Revitalization Talents Program under grant XLYC1807019,Liaoning Province Natural Science Foundation under grants 2019-MS-058+1 种基金Dalian Science and Technology Innovation Foundation under grant 2018J12GX044Fundamental Research Funds for the Central Universities under grants DUT20LAB113 and DUT20JC07,and Cooperative Scientific Research Project of Chunhui Plan of Ministry of Education.
文摘Device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals,it may empower wireless networks with the augmented sensing ability.Researchers have made great achievements for singleperson device-free gesture recognition.However,when multiple persons conduct gestures simultaneously,the received signals will be mixed together,and thus traditional methods would not work well anymore.Moreover,the anonymity of persons and the change in the surrounding environment would cause feature shift and mismatch,and thus the recognition accuracy would degrade remarkably.To address these problems,we explore and exploit the diversity of spatial information and propose a multidimensional analysis method to separate the gesture feature of each person using a focusing sensing strategy.Meanwhile,we also present a deep-learning based robust device free gesture recognition framework,which leverages an adversarial approach to extract robust gesture feature that is insensitive to the change of persons and environment.Furthermore,we also develop a 77GHz mmWave prototype system and evaluate the proposed methods extensively.Experimental results reveal that the proposed system can achieve average accuracies of 93%and 84%when 10 gestures are conducted in Received:Jun.18,2020 Revised:Aug.06,2020 Editor:Ning Ge different environments by two and four persons simultaneously,respectively.
基金supported by the National Natural Science Foundation of China under(Grant No.52175531)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant(Grant Nos.KJQN202000605 and KJZD-M202000602)。
文摘Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1F1A1063319)。
文摘With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.
