The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyunhus...The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyunhushuo Hydrologic Station, which is located in the middle reaches of the Huolin River, were analyzed by using wavelet analysis. Main objective was to discuss the periodic characteristics of the runoff, and examine the temporal patterns of the Huolin River recharging to the floodplain wetlands in the lower reaches of the river, and the corresponding effects of recharging variation on the environmental evolution of the wetlands. The results show that the annual runoff varied mainly at three time scales. The intensities of periodical signals at different time scales were strongly characterized by local distribution in its time frequency domain. The interdecadal variation at a scale of more than 30yr played a leading role in the temporal pattern ofrnnoffvariation, and at this scale, the runoffat Baiyunhushuo Hydrologic Station varied in turn of flood, draught and flood. Accordingly, the landscape of the floodplain wetlands presented periodic features, especially prominent before the 1990s. Compared with intense human activities, the runoff periodic pattern at middle （10-20yr） and small （1-10yr） scales, which has relatively low energy, exerted unobvious effects on the environmental evolution of the floodplain wetlands, especially after the 1990s.展开更多
Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term ...Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory(LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error(MAPE)curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.展开更多
The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, a...The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, and metabolome (multi-layered omics data). In Systems Biology, we try to elucidate various dynamical characteristics of biological functions with applying the omics data to detailed mathematical model based on the central dogma. However, such mathematical models possess multi-time-scale properties which are often accompanied by time-scale differences seen among biological layers. The differences cause time stiff problem, and have a grave influence on numerical calculation stability. In the present conventional method, the time stiff problem remained because the calculation of all layers was implemented by adaptive time step sizes of the smallest time-scale layer to ensure stability and maintain calculation accuracy. In this paper, we designed and developed an effective numerical calculation method to improve the time stiff problem. This method consisted of ahead, backward, and cumulative algorithms. Both ahead and cumulative algorithms enhanced calculation efficiency of numerical calculations via adjustments of step sizes of each layer, and reduced the number of numerical calculations required for multi-time-scale models with the time stiff problem. Backward algorithm ensured calculation accuracy in the multi-time-scale models. In case studies which were focused on three layers system with 60 times difference in time-scale order in between layers, a proposed method had almost the same calculation accuracy compared with the conventional method in spite of a reduction of the total amount of the number of numerical calculations. Accordingly, the proposed method is useful in a numerical analysis of multi-time-scale models with time stiff problem.展开更多
A new measurement-based admission control algorithm to support Quality of Service(QoS) demand is proposed for soft real-time applications. In the algorithm, admission test is performed across Multiple Time-Scales (MTS...A new measurement-based admission control algorithm to support Quality of Service(QoS) demand is proposed for soft real-time applications. In the algorithm, admission test is performed across Multiple Time-Scales (MTS) to accurately capture traffic fluctuation on various time-scales. By applying the QoS requirements directly to admission test, the MTS algorithm can properly meet the QoS target and maximize the bandwidth utilization.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
When modeling the soil/atmosphere interaction,it is of paramount importance to determine the net radiation flux.There are two common calculation methods for this purpose.Method 1 relies on use of air temperature,while...When modeling the soil/atmosphere interaction,it is of paramount importance to determine the net radiation flux.There are two common calculation methods for this purpose.Method 1 relies on use of air temperature,while Method 2 relies on use of both air and soil temperatures.Nowadays,there has been no consensus on the application of these two methods.In this study,the half-hourly data of solar radiation recorded at an experimental embankment are used to calculate the net radiation and long-wave radiation at different time-scales(half-hourly,hourly,and daily) using the two methods.The results show that,compared with Method 2 which has been widely adopted in agronomical,geotechnical and geo-environmental applications.Method 1 is more feasible for its simplicity and accuracy at shorter time-scale.Moreover,in case of longer time-scale,daily for instance,less variations of net radiation and long-wave radiation are obtained,suggesting that no detailed soil temperature variations can be obtained.In other words,shorter time-scales are preferred in determining net radiation flux.展开更多
According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object...According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.展开更多
The variational calculus of time-scale non-shifted systems includes both the traditional continuous and traditional significant discrete variational calculus.Not only can the combination ofand∇derivatives be beneficia...The variational calculus of time-scale non-shifted systems includes both the traditional continuous and traditional significant discrete variational calculus.Not only can the combination ofand∇derivatives be beneficial to obtaining higher convergence order in numerical analysis,but also it prompts the timescale numerical computational scheme to have good properties,for instance,structure-preserving.In this letter,a structure-preserving algorithm for time-scale non-shifted Hamiltonian systems is proposed.By using the time-scale discrete variational method and calculus theory,and taking a discrete time scale in the variational principle of non-shifted Hamiltonian systems,the corresponding discrete Hamiltonian principle can be obtained.Furthermore,the time-scale discrete Hamilton difference equations,Noether theorem,and the symplectic scheme of discrete Hamiltonian systems are obtained.Finally,taking the Kepler problem and damped oscillator for time-scale non-shifted Hamiltonian systems as examples,they show that the time-scale discrete variational method is a structure-preserving algorithm.The new algorithm not only provides a numerical method for solving time-scale non-shifted dynamic equations but can be calculated with variable step sizes to improve the computational speed.展开更多
This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using A...This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.展开更多
S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact o...S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and e...Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.展开更多
Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feat...Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.展开更多
Correction to:Nuclear Science and Techniques(2025)36:100 https://doi.org/10.1007/s41365-025-01692-6 In this article,Fig.9 appeared incorrectly and have now been corrected in the original publication.For completeness a...Correction to:Nuclear Science and Techniques(2025)36:100 https://doi.org/10.1007/s41365-025-01692-6 In this article,Fig.9 appeared incorrectly and have now been corrected in the original publication.For completeness and transparency,both correct and incorrect versions are displayed below.展开更多
To overcome external environmental disturbances,inertial parameter uncertainties and vibration of flexible modes in the process of attitude tracking,a comprehensively effective predefined-time guaranteed performance c...To overcome external environmental disturbances,inertial parameter uncertainties and vibration of flexible modes in the process of attitude tracking,a comprehensively effective predefined-time guaranteed performance controller based on multi⁃observers for flexible spacecraft is proposed.First,to prevent unwinding phenomenon in attitude description,the rotation matrix is used to represent the spacecraft’s attitude.Second,the flexible modes observer which can guarantee predefined⁃time convergence is designed,for the case where flexible vibrations are unmeasurable in practice.What’s more,the disturbance observer is applied to estimate and compensate the lumped disturbances to improve the robustness of attitude control.A predefined-time controller is proposed to satisfy the prescribed performance and stabilize the attitude tracking system via barrier Lyapunov function.Finally,through comparative numerical simulations,the proposed controller can achieve high-precision convergence compared with the existing finite-time attitude tracking controller.This paper provides certain references for the high-precision predefined-time prescribed performance attitude tracking of flexible spacecraft with multi-disturbance.展开更多
基金Under the auspices of Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX3-SW-332-01)
文摘The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyunhushuo Hydrologic Station, which is located in the middle reaches of the Huolin River, were analyzed by using wavelet analysis. Main objective was to discuss the periodic characteristics of the runoff, and examine the temporal patterns of the Huolin River recharging to the floodplain wetlands in the lower reaches of the river, and the corresponding effects of recharging variation on the environmental evolution of the wetlands. The results show that the annual runoff varied mainly at three time scales. The intensities of periodical signals at different time scales were strongly characterized by local distribution in its time frequency domain. The interdecadal variation at a scale of more than 30yr played a leading role in the temporal pattern ofrnnoffvariation, and at this scale, the runoffat Baiyunhushuo Hydrologic Station varied in turn of flood, draught and flood. Accordingly, the landscape of the floodplain wetlands presented periodic features, especially prominent before the 1990s. Compared with intense human activities, the runoff periodic pattern at middle （10-20yr） and small （1-10yr） scales, which has relatively low energy, exerted unobvious effects on the environmental evolution of the floodplain wetlands, especially after the 1990s.
基金supported by the Fundamental Research Funds for the Central Universities(No.2017MS093)
文摘Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory(LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error(MAPE)curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.
文摘The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, and metabolome (multi-layered omics data). In Systems Biology, we try to elucidate various dynamical characteristics of biological functions with applying the omics data to detailed mathematical model based on the central dogma. However, such mathematical models possess multi-time-scale properties which are often accompanied by time-scale differences seen among biological layers. The differences cause time stiff problem, and have a grave influence on numerical calculation stability. In the present conventional method, the time stiff problem remained because the calculation of all layers was implemented by adaptive time step sizes of the smallest time-scale layer to ensure stability and maintain calculation accuracy. In this paper, we designed and developed an effective numerical calculation method to improve the time stiff problem. This method consisted of ahead, backward, and cumulative algorithms. Both ahead and cumulative algorithms enhanced calculation efficiency of numerical calculations via adjustments of step sizes of each layer, and reduced the number of numerical calculations required for multi-time-scale models with the time stiff problem. Backward algorithm ensured calculation accuracy in the multi-time-scale models. In case studies which were focused on three layers system with 60 times difference in time-scale order in between layers, a proposed method had almost the same calculation accuracy compared with the conventional method in spite of a reduction of the total amount of the number of numerical calculations. Accordingly, the proposed method is useful in a numerical analysis of multi-time-scale models with time stiff problem.
文摘A new measurement-based admission control algorithm to support Quality of Service(QoS) demand is proposed for soft real-time applications. In the algorithm, admission test is performed across Multiple Time-Scales (MTS) to accurately capture traffic fluctuation on various time-scales. By applying the QoS requirements directly to admission test, the MTS algorithm can properly meet the QoS target and maximize the bandwidth utilization.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
基金support of the European Commission by the Marie Curie IRSES Project GREAT-Geotechnical and Geological Responses to Climate Change:Exchanging Approaches and Technologies on a World-wide Scale(FP7-PEOPLE2013-IRSES-612665)the China Scholarship Council(CSC)Ecole des Ponts Paris Tech for their financial supports
文摘When modeling the soil/atmosphere interaction,it is of paramount importance to determine the net radiation flux.There are two common calculation methods for this purpose.Method 1 relies on use of air temperature,while Method 2 relies on use of both air and soil temperatures.Nowadays,there has been no consensus on the application of these two methods.In this study,the half-hourly data of solar radiation recorded at an experimental embankment are used to calculate the net radiation and long-wave radiation at different time-scales(half-hourly,hourly,and daily) using the two methods.The results show that,compared with Method 2 which has been widely adopted in agronomical,geotechnical and geo-environmental applications.Method 1 is more feasible for its simplicity and accuracy at shorter time-scale.Moreover,in case of longer time-scale,daily for instance,less variations of net radiation and long-wave radiation are obtained,suggesting that no detailed soil temperature variations can be obtained.In other words,shorter time-scales are preferred in determining net radiation flux.
基金Supported by National Natural Science Foundation of China(Grant Nos.11701144,11971149)Henan Province Key and Promotion Special(Science and Technology)Project(Grant No.212102310305).
文摘According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.
基金This work was supported by the National Natural Science Foundation of China(Nos.11972241,11572212)the Natural Science Foundation of Jiangsu Province(No.BK20191454)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX20_0251).
文摘The variational calculus of time-scale non-shifted systems includes both the traditional continuous and traditional significant discrete variational calculus.Not only can the combination ofand∇derivatives be beneficial to obtaining higher convergence order in numerical analysis,but also it prompts the timescale numerical computational scheme to have good properties,for instance,structure-preserving.In this letter,a structure-preserving algorithm for time-scale non-shifted Hamiltonian systems is proposed.By using the time-scale discrete variational method and calculus theory,and taking a discrete time scale in the variational principle of non-shifted Hamiltonian systems,the corresponding discrete Hamiltonian principle can be obtained.Furthermore,the time-scale discrete Hamilton difference equations,Noether theorem,and the symplectic scheme of discrete Hamiltonian systems are obtained.Finally,taking the Kepler problem and damped oscillator for time-scale non-shifted Hamiltonian systems as examples,they show that the time-scale discrete variational method is a structure-preserving algorithm.The new algorithm not only provides a numerical method for solving time-scale non-shifted dynamic equations but can be calculated with variable step sizes to improve the computational speed.
基金Supported by the National Natural Science Foundation of China (No. 60872105)the Program for Science & Technology Innovative Research Team of Qing Lan Project in Higher Educational Institutions of Jiangsuthe Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.
文摘S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0134200)the National Natural Science Foundation of China(Grant No.12204214)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.GK202207012)QCYRCXM-2022-241。
文摘Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.
基金supported by the National Natural Science Foundation of China(62302167,62477013)Natural Science Foundation of Shanghai(No.24ZR1456100)+1 种基金Science and Technology Commission of Shanghai Municipality(No.24DZ2305900)the Shanghai Municipal Special Fund for Promoting High-Quality Development of Industries(2211106).
文摘Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.
文摘Correction to:Nuclear Science and Techniques(2025)36:100 https://doi.org/10.1007/s41365-025-01692-6 In this article,Fig.9 appeared incorrectly and have now been corrected in the original publication.For completeness and transparency,both correct and incorrect versions are displayed below.
基金supported by the National Natural Science Foundation of China(No.12472045)the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2022-036)。
文摘To overcome external environmental disturbances,inertial parameter uncertainties and vibration of flexible modes in the process of attitude tracking,a comprehensively effective predefined-time guaranteed performance controller based on multi⁃observers for flexible spacecraft is proposed.First,to prevent unwinding phenomenon in attitude description,the rotation matrix is used to represent the spacecraft’s attitude.Second,the flexible modes observer which can guarantee predefined⁃time convergence is designed,for the case where flexible vibrations are unmeasurable in practice.What’s more,the disturbance observer is applied to estimate and compensate the lumped disturbances to improve the robustness of attitude control.A predefined-time controller is proposed to satisfy the prescribed performance and stabilize the attitude tracking system via barrier Lyapunov function.Finally,through comparative numerical simulations,the proposed controller can achieve high-precision convergence compared with the existing finite-time attitude tracking controller.This paper provides certain references for the high-precision predefined-time prescribed performance attitude tracking of flexible spacecraft with multi-disturbance.
