The Informer model leverages its innovative ProbSparse self-attention mechanism to demonstrate significant performance advantages in long-sequence time-series forecasting tasks.However,when confronted with time-series...The Informer model leverages its innovative ProbSparse self-attention mechanism to demonstrate significant performance advantages in long-sequence time-series forecasting tasks.However,when confronted with time-series data exhibiting multi-scale characteristics and substantial noise,the model’s attention mechanism reveals inherent limitations.Specifically,the model is susceptible to interference from local noise or irrelevant patterns,leading to diminished focus on globally critical information and consequently impairing forecasting accuracy.To address this challenge,this study proposes an enhanced architecture that integrates a Gated Attention mechanism into the original Informer framework.This mechanism employs learnable gating functions to dynamically and selectively impose differentiated weighting on crucial temporal segments and discriminative feature dimensions within the input sequence.This adaptive weighting strategy is designed to effectively suppress noise interference while amplifying the capture of core dynamic patterns.Consequently,it substantially strengthens the model’s capability to represent complex temporal dynamics and ultimately elevates its predictive performance.展开更多
The flux of dissolved inorganic nitrogen(DIN),predominantly nitrate(NO_(3)^(-))and ammonium(NH_(4)^(+)),from land to coastal waters via rivers is commonly estimated simply by multiplying water flux with nitrogen conce...The flux of dissolved inorganic nitrogen(DIN),predominantly nitrate(NO_(3)^(-))and ammonium(NH_(4)^(+)),from land to coastal waters via rivers is commonly estimated simply by multiplying water flux with nitrogen concentration.Understanding DIN fluxes in gated estuaries is critical as these systems often serve as hotspots for nutrient transformations,influencing coastal water quality and ecosystem health.However,the subsequent interactions involving NO_(3)^(-)and NH_(4)^(+)adsorption or desorption on suspended sediments are often overlooked.To better understand the impact of these interactions on the overall NO_(3)^(-)and NH_(4)^(+)sorption or desorption and subsequently,the mobility and transport to the coastal zone,we conducted a series of NO_(3)^(-)and NH_(4)^(+)adsorption and desorption experiments.These experiments involved varying suspended sediment concentrations,particle sizes,salinities,and sea-salt ions to assess their potential effects.Results indicate that desorption of NO_(3)^(-)and NH_(4)^(+)from suspended sediments is more prominent than adsorption,with NH_(4)^(+)desorption being particularly significant.Notably,at low suspended particle concentrations and high salinity,NH_(4)^(+)desorption from sediments increased markedly,which further amplified in polyhaline conditions.This effect could result from ion pairing between NH_(4)^(+)and seawater anions,along with competition from seawater cations for sediment cation exchange sites,enhancing NH_(4)^(+)diffusion from estuarine sediments,and the elevated NH_(4)^(+)release could promote DIN transport to nearshore waters,especially in gated estuaries where sediment resuspension occurs.Given the critical role of NH_(4)^(+)in estuarine nitrogen cycling,ignoring these dynamics could lead to underestimations of DIN transport in river-estuary systems.Therefore,incorporating sediment dynamics into DIN flux estimations is crucial for accurately assessing nitrogen transport in gated estuaries.展开更多
With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extract...With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.展开更多
By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of...By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of irrelevant data and boost prediction accuracy,an attention mechanism was integrated into the point model to concen-trate on important input sequence information.Based on the point predictions,the lower upper bound estimation(LUBE)method was used,providing a range for the bus interval times predicted by the model.The model was vali-dated using data from 169 bus routes in Nanchang,Jiangxi Province.The results indicated that the attention-GRU model outperformed neural network,long short-term memory and GRU models.Compared with the Bootstrap method,the LUBE method has a narrower average interval width.The coverage width-based criterion(CWC)was reduced by 8.1%,2.2%,and 5.7%at confidence levels of 85%,90%,and 95%,respectively,during the off-peak period,and by 23.2%,26.9%,and 27.3%at confidence levels of 85%,90%,and 95%,respectively,during the peak period.Therefore,it can accurately describe the fluctuation range in bus arrival times with higher accuracy and stability.展开更多
Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many comp...Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.展开更多
Quantum key distribution(QKD)achieves information-theoretic security based on quantum mechanics principles,where single-photon detectors(SPDs)serve as critical components.This study focuses on the sinusoidal gated SPD...Quantum key distribution(QKD)achieves information-theoretic security based on quantum mechanics principles,where single-photon detectors(SPDs)serve as critical components.This study focuses on the sinusoidal gated SPDs widely used in high-speed QKD systems.We investigate the mechanisms underlying the rising-edge jitter in detection signals,identifying contributions from factors such as the temporal width of injected optical pulses,avalanche generation processes,avalanche signal extraction,and pulse discrimination.To address the issue of excessive jitter-induced bit errors,we propose a retiming scheme that utilizes coincidence signals synchronized with the sinusoidal gating signal.This approach effectively suppresses detection signal jitter and reduces the after-pulse probability of the detector.Experimental validation using a high-precision time-to-digital converter(TDC)demonstrates a significant reduction in the rising-edge jitter distribution after applying the suppression scheme.The proposed method features clear principles and straightforward engineering implementation,avoiding direct interference with the detector’s operational processes.The designed high-speed sinusoidal gated InGaAs/InP SPD operates at 1.25 GHz,achieving a remarkable reduction in after-pulse probability from 10.7%(without jitter suppression)to 0.72%,thereby enhancing the overall performance of QKD systems.展开更多
Existing Transformer-based image captioning models typically rely on the self-attention mechanism to capture long-range dependencies,which effectively extracts and leverages the global correlation of image features.Ho...Existing Transformer-based image captioning models typically rely on the self-attention mechanism to capture long-range dependencies,which effectively extracts and leverages the global correlation of image features.However,these models still face challenges in effectively capturing local associations.Moreover,since the encoder extracts global and local association features that focus on different semantic information,semantic noise may occur during the decoding stage.To address these issues,we propose the Local Relationship Enhanced Gated Transformer(LREGT).In the encoder part,we introduce the Local Relationship Enhanced Encoder(LREE),whose core component is the Local Relationship Enhanced Module(LREM).LREM consists of two novel designs:the Local Correlation Perception Module(LCPM)and the Local-Global Fusion Module(LGFM),which are beneficial for generating a comprehensive feature representation that integrates both global and local information.In the decoder part,we propose the Dual-level Multi-branch Gated Decoder(DMGD).It first creates multiple decoding branches to generate multi-perspective contextual feature representations.Subsequently,it employs the Dual-Level Gating Mechanism(DLGM)to model the multi-level relationships of these multi-perspective contextual features,enhancing their fine-grained semantics and intrinsic relationship representations.This ultimately leads to the generation of high-quality and semantically rich image captions.Experiments on the standard MSCOCO dataset demonstrate that LREGT achieves state-of-the-art performance,with a CIDEr score of 140.8 and BLEU-4 score of 41.3,significantly outperforming existing mainstream methods.These results highlight LREGT’s superiority in capturing complex visual relationships and resolving semantic noise during decoding.展开更多
The front gate interface and oxide traps induced by hot carrier stress in SOI NMOSFETs are studied.Based on a new forward gated diode technique,the R G current originating from the front interface traps is me...The front gate interface and oxide traps induced by hot carrier stress in SOI NMOSFETs are studied.Based on a new forward gated diode technique,the R G current originating from the front interface traps is measured,and then the densities of the interface and oxide traps are separated independently.The experimental results show that the hot carrier stress of front channel not only results in the strong generation of the front interface traps,but also in the significant oxide traps.These two kinds of traps have similar characteristic in increasing with the hot carrier stress time.This analysis allows one to obtain a clear physical picture of the effects of the hot carrier stress on the generating of interface and oxide traps,which help to understand the degradation and reliability of the SOI MOSFETs.展开更多
The channel lateral pocket or halo region of NMOSFET characterized by interface state R G current of a forward gated diode has been investigated numerically for the first time.The result of numerical analysis demons...The channel lateral pocket or halo region of NMOSFET characterized by interface state R G current of a forward gated diode has been investigated numerically for the first time.The result of numerical analysis demonstrates that the effective surface doping concentration and the interface state density of the pocket or halo region are interface states R G current peak position dependent and amplitude dependent,respectively.It can be expressed quantitatively according to the device physics knowledge,thus,the direct characterization of the interface state density and the effective surface doping concentration of the pocket or halo becomes very easy.展开更多
A design of low-light-level night vision system is described,which can image objects selectively in the specific space. The system can selectively image some objects in specific distances,meanwhile ignore those shelte...A design of low-light-level night vision system is described,which can image objects selectively in the specific space. The system can selectively image some objects in specific distances,meanwhile ignore those shelters on the way of observation by combining an intensifying charge coupled device(ICCD) with a near infrared laser assisted in vision,whose operation wavelength matches with the photocathode of the image tube,and adopting the gated mode and adjustable time-delay. A semiconductor laser diode of 100 W in peak power is chosen for illumination. The laser and the image tube operate in 150 ns pulse width and 2 kHz repeat frequency. Some images of different objects at the different distances within 100 m can be obtained clearly,and even behind a grove by using a sampling circuit and a delay control device at 100 W in peak power of semiconductor laser diode,150 ns in pulse width of laser and image tube,2 kHz in repeat frequency.展开更多
The low-temperature measurement of Hall effect of the two-dimensional electron system in a double-layered gated Si-δ-doped GaAs is presented.A complex peculiar nonlinear dependence of the depletion on gate voltage i...The low-temperature measurement of Hall effect of the two-dimensional electron system in a double-layered gated Si-δ-doped GaAs is presented.A complex peculiar nonlinear dependence of the depletion on gate voltage is observed.The nonlinearity is also explained on the basis of the assumption that the double-capacity model consists of two δ-doped two-dimensional electron layers and a metallic gate,and the experimental result that the electron mobility is linear with the electron density on a log-log scale.展开更多
The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention R...The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.展开更多
The reverse generation current under high-gate-voltage stress condition in LDD nMOSFET＇s is studied. We find that the generation current peak decreases as the stress time increases. We ascribe this finding to the dom...The reverse generation current under high-gate-voltage stress condition in LDD nMOSFET＇s is studied. We find that the generation current peak decreases as the stress time increases. We ascribe this finding to the dominating oxide trapped electrons that reduce the effective drain bias, lowering the maximal generation rate. The density of the effective trapped electrons affecting the effective drain bias is calculated with our model.展开更多
With the development of laser technologies,nuclear reactions can happen in high-temperature plasma environments induced by lasers and have attracted a lot of attention from different physical disciplines.However,studi...With the development of laser technologies,nuclear reactions can happen in high-temperature plasma environments induced by lasers and have attracted a lot of attention from different physical disciplines.However,studies on nuclear reactions in plasma are still limited by detecting technologies.This is mainly due to the fact that extremely high electromagnetic pulses(EMPs)can also be induced when high-intensity lasers hit targets to induce plasma,and then cause dysfunction of many types of traditional detectors.Therefore,new particle detecting technologies are highly needed.In this paper,we report a recently developed gated fiber detector which can be used in harsh EMP environments.In this prototype detector,scintillating photons are coupled by fiber and then transferred to a gated photomultiplier tube which is located far away from the EMP source and shielded well.With those measures,the EMPs can be avoided which may result that the device has the capability to identify a single event of nuclear reaction products generated in laser-induced plasma from noise EMP backgrounds.This new type of detector can be widely used as a time-of-flight(TOF)detector in high-intensity laser nuclear physics experiments for detecting neutrons,photons,and other charged particles.展开更多
An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated rec...An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.展开更多
Drug delivery systems(DDS) are used to deliver therapeutic drugs to improve selectivity and reduce side effects. With the development of nanotechnology, many nanocarriers have been developed and applied to drug delive...Drug delivery systems(DDS) are used to deliver therapeutic drugs to improve selectivity and reduce side effects. With the development of nanotechnology, many nanocarriers have been developed and applied to drug delivery, including mesoporous silica. Mesoporous silica nanoparticles(MSNs) have attracted a lot of attention for simple synthesis, biocompatibility, high surface area and pore volume. Based on the pore system and surface modification, gated mesoporous silica nanoparticles can be designed to realize on-command drug release, which provides a new approach for selective delivery of antitumor drugs.Herein, this review mainly focuses on the “gate keepers” of mesoporous silica for drug controlled release in nearly few years(2017–2020). We summarize the mechanism of drug controlled release in gated MSNs and different gated materials: inorganic gated materials, organic gated materials, self-gated drug molecules, and biological membranes. The facing challenges and future prospects of gated MSNs are discussed rationally in the end.展开更多
Voltage gated calcium channel(VGCC) antibodies are generally associated with Lambert-Eaton myasthenic syndrome. However the presence of this antibody has been associated with paraneoplastic as well as nonparaneoplasti...Voltage gated calcium channel(VGCC) antibodies are generally associated with Lambert-Eaton myasthenic syndrome. However the presence of this antibody has been associated with paraneoplastic as well as nonparaneoplastic cerebellar degeneration. Most patients with VGCC-antibody-positivity have small cell lung cancer(SCLC). Lambert-Eaton myasthenic syndrome(LEMS)is an autoimmune disease of the presynaptic part of the neuromuscular junction. Its classical clinical triadis proximal muscle weakness, areflexia and autonomic dysfunction. Fifty to sixty percent of LEMS patients have a neoplasia, usually SCLC. The co-occurrence of SCLC and LEMS causes more severe and progressive disease and shorter survival than non-paraneoplastic LEMS. Treatment includes 3,4 diaminopyridine for symptomatic purposes and immunotherapy with prednisolone, azathioprine or intravenous immunoglobulin in patients unresponsive to 3,4 diaminopyridine. Paraneoplastic cerebellar degeneration(PCD) is a syndrome characterized with severe, subacute pancerebellar dysfunction. Serum is positive for VGCC antibody in 41%-44% of patients, usually with the co-occurrence of SCLC. Clinical and electrophysiological features of LEMS are also present in 20%-40% of these patients. Unfortunately, PCD symptoms do not improve with immunotherapy. The role of VGCC antibody in the immunopathogenesis of LEMS is well known whereas its role in PCD is still unclear. All patients presenting with LEMS or PCD must be investigated for SCLC.展开更多
The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). I...The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.展开更多
As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symboli...As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.展开更多
Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties ...Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.展开更多
文摘The Informer model leverages its innovative ProbSparse self-attention mechanism to demonstrate significant performance advantages in long-sequence time-series forecasting tasks.However,when confronted with time-series data exhibiting multi-scale characteristics and substantial noise,the model’s attention mechanism reveals inherent limitations.Specifically,the model is susceptible to interference from local noise or irrelevant patterns,leading to diminished focus on globally critical information and consequently impairing forecasting accuracy.To address this challenge,this study proposes an enhanced architecture that integrates a Gated Attention mechanism into the original Informer framework.This mechanism employs learnable gating functions to dynamically and selectively impose differentiated weighting on crucial temporal segments and discriminative feature dimensions within the input sequence.This adaptive weighting strategy is designed to effectively suppress noise interference while amplifying the capture of core dynamic patterns.Consequently,it substantially strengthens the model’s capability to represent complex temporal dynamics and ultimately elevates its predictive performance.
基金Supported by the Tianjin Key R&D Program(No.21YFSNSN00220)。
文摘The flux of dissolved inorganic nitrogen(DIN),predominantly nitrate(NO_(3)^(-))and ammonium(NH_(4)^(+)),from land to coastal waters via rivers is commonly estimated simply by multiplying water flux with nitrogen concentration.Understanding DIN fluxes in gated estuaries is critical as these systems often serve as hotspots for nutrient transformations,influencing coastal water quality and ecosystem health.However,the subsequent interactions involving NO_(3)^(-)and NH_(4)^(+)adsorption or desorption on suspended sediments are often overlooked.To better understand the impact of these interactions on the overall NO_(3)^(-)and NH_(4)^(+)sorption or desorption and subsequently,the mobility and transport to the coastal zone,we conducted a series of NO_(3)^(-)and NH_(4)^(+)adsorption and desorption experiments.These experiments involved varying suspended sediment concentrations,particle sizes,salinities,and sea-salt ions to assess their potential effects.Results indicate that desorption of NO_(3)^(-)and NH_(4)^(+)from suspended sediments is more prominent than adsorption,with NH_(4)^(+)desorption being particularly significant.Notably,at low suspended particle concentrations and high salinity,NH_(4)^(+)desorption from sediments increased markedly,which further amplified in polyhaline conditions.This effect could result from ion pairing between NH_(4)^(+)and seawater anions,along with competition from seawater cations for sediment cation exchange sites,enhancing NH_(4)^(+)diffusion from estuarine sediments,and the elevated NH_(4)^(+)release could promote DIN transport to nearshore waters,especially in gated estuaries where sediment resuspension occurs.Given the critical role of NH_(4)^(+)in estuarine nitrogen cycling,ignoring these dynamics could lead to underestimations of DIN transport in river-estuary systems.Therefore,incorporating sediment dynamics into DIN flux estimations is crucial for accurately assessing nitrogen transport in gated estuaries.
基金supported by the Natural Science Foundation of Henan under Grant 242300421220the Henan Provincial Science and Technology Research Project under Grants 252102211047 and 252102211062+3 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126.
文摘With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.
基金The National Natural Science Foundation of China(No.52162042)General Science and Technology Project of Jiangxi Provincial Department of Transportation(No.2024YB039).
文摘By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of irrelevant data and boost prediction accuracy,an attention mechanism was integrated into the point model to concen-trate on important input sequence information.Based on the point predictions,the lower upper bound estimation(LUBE)method was used,providing a range for the bus interval times predicted by the model.The model was vali-dated using data from 169 bus routes in Nanchang,Jiangxi Province.The results indicated that the attention-GRU model outperformed neural network,long short-term memory and GRU models.Compared with the Bootstrap method,the LUBE method has a narrower average interval width.The coverage width-based criterion(CWC)was reduced by 8.1%,2.2%,and 5.7%at confidence levels of 85%,90%,and 95%,respectively,during the off-peak period,and by 23.2%,26.9%,and 27.3%at confidence levels of 85%,90%,and 95%,respectively,during the peak period.Therefore,it can accurately describe the fluctuation range in bus arrival times with higher accuracy and stability.
基金supported by National Natural Science Foundation of China(Nos.61422203 and 61333014)National Key Basic Research Program of China(No.2014CB340501)
文摘Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.
基金supported by the Major Scientific and Technological Special Project of Anhui Province(202103a13010004)the Major Scientific and Technological Special Project of Hefei City(2021DX007)the Manufacturing Industry Project of Attracting Talents and Wisdom of Anhui Province(JB24179).
文摘Quantum key distribution(QKD)achieves information-theoretic security based on quantum mechanics principles,where single-photon detectors(SPDs)serve as critical components.This study focuses on the sinusoidal gated SPDs widely used in high-speed QKD systems.We investigate the mechanisms underlying the rising-edge jitter in detection signals,identifying contributions from factors such as the temporal width of injected optical pulses,avalanche generation processes,avalanche signal extraction,and pulse discrimination.To address the issue of excessive jitter-induced bit errors,we propose a retiming scheme that utilizes coincidence signals synchronized with the sinusoidal gating signal.This approach effectively suppresses detection signal jitter and reduces the after-pulse probability of the detector.Experimental validation using a high-precision time-to-digital converter(TDC)demonstrates a significant reduction in the rising-edge jitter distribution after applying the suppression scheme.The proposed method features clear principles and straightforward engineering implementation,avoiding direct interference with the detector’s operational processes.The designed high-speed sinusoidal gated InGaAs/InP SPD operates at 1.25 GHz,achieving a remarkable reduction in after-pulse probability from 10.7%(without jitter suppression)to 0.72%,thereby enhancing the overall performance of QKD systems.
基金supported by the Natural Science Foundation of China(62473105,62172118)Nature Science Key Foundation of Guangxi(2021GXNSFDA196002)+1 种基金in part by the Guangxi Key Laboratory of Image and Graphic Intelligent Processing under Grants(GIIP2302,GIIP2303,GIIP2304)Innovation Project of Guang Xi Graduate Education(2024YCXB09,2024YCXS039).
文摘Existing Transformer-based image captioning models typically rely on the self-attention mechanism to capture long-range dependencies,which effectively extracts and leverages the global correlation of image features.However,these models still face challenges in effectively capturing local associations.Moreover,since the encoder extracts global and local association features that focus on different semantic information,semantic noise may occur during the decoding stage.To address these issues,we propose the Local Relationship Enhanced Gated Transformer(LREGT).In the encoder part,we introduce the Local Relationship Enhanced Encoder(LREE),whose core component is the Local Relationship Enhanced Module(LREM).LREM consists of two novel designs:the Local Correlation Perception Module(LCPM)and the Local-Global Fusion Module(LGFM),which are beneficial for generating a comprehensive feature representation that integrates both global and local information.In the decoder part,we propose the Dual-level Multi-branch Gated Decoder(DMGD).It first creates multiple decoding branches to generate multi-perspective contextual feature representations.Subsequently,it employs the Dual-Level Gating Mechanism(DLGM)to model the multi-level relationships of these multi-perspective contextual features,enhancing their fine-grained semantics and intrinsic relationship representations.This ultimately leads to the generation of high-quality and semantically rich image captions.Experiments on the standard MSCOCO dataset demonstrate that LREGT achieves state-of-the-art performance,with a CIDEr score of 140.8 and BLEU-4 score of 41.3,significantly outperforming existing mainstream methods.These results highlight LREGT’s superiority in capturing complex visual relationships and resolving semantic noise during decoding.
文摘The front gate interface and oxide traps induced by hot carrier stress in SOI NMOSFETs are studied.Based on a new forward gated diode technique,the R G current originating from the front interface traps is measured,and then the densities of the interface and oxide traps are separated independently.The experimental results show that the hot carrier stress of front channel not only results in the strong generation of the front interface traps,but also in the significant oxide traps.These two kinds of traps have similar characteristic in increasing with the hot carrier stress time.This analysis allows one to obtain a clear physical picture of the effects of the hot carrier stress on the generating of interface and oxide traps,which help to understand the degradation and reliability of the SOI MOSFETs.
文摘The channel lateral pocket or halo region of NMOSFET characterized by interface state R G current of a forward gated diode has been investigated numerically for the first time.The result of numerical analysis demonstrates that the effective surface doping concentration and the interface state density of the pocket or halo region are interface states R G current peak position dependent and amplitude dependent,respectively.It can be expressed quantitatively according to the device physics knowledge,thus,the direct characterization of the interface state density and the effective surface doping concentration of the pocket or halo becomes very easy.
文摘A design of low-light-level night vision system is described,which can image objects selectively in the specific space. The system can selectively image some objects in specific distances,meanwhile ignore those shelters on the way of observation by combining an intensifying charge coupled device(ICCD) with a near infrared laser assisted in vision,whose operation wavelength matches with the photocathode of the image tube,and adopting the gated mode and adjustable time-delay. A semiconductor laser diode of 100 W in peak power is chosen for illumination. The laser and the image tube operate in 150 ns pulse width and 2 kHz repeat frequency. Some images of different objects at the different distances within 100 m can be obtained clearly,and even behind a grove by using a sampling circuit and a delay control device at 100 W in peak power of semiconductor laser diode,150 ns in pulse width of laser and image tube,2 kHz in repeat frequency.
文摘The low-temperature measurement of Hall effect of the two-dimensional electron system in a double-layered gated Si-δ-doped GaAs is presented.A complex peculiar nonlinear dependence of the depletion on gate voltage is observed.The nonlinearity is also explained on the basis of the assumption that the double-capacity model consists of two δ-doped two-dimensional electron layers and a metallic gate,and the experimental result that the electron mobility is linear with the electron density on a log-log scale.
基金supported by the National Natural Science Foundation of China(Nos.62106283 and 72001214)。
文摘The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.
文摘The reverse generation current under high-gate-voltage stress condition in LDD nMOSFET＇s is studied. We find that the generation current peak decreases as the stress time increases. We ascribe this finding to the dominating oxide trapped electrons that reduce the effective drain bias, lowering the maximal generation rate. The density of the effective trapped electrons affecting the effective drain bias is calculated with our model.
基金supported by the National Nature Science Foundation of China(Nos.11875191,11890714,11925502,11935001,and 11961141003)the Strategic Priority Research Program(No.CAS XDB1602)。
文摘With the development of laser technologies,nuclear reactions can happen in high-temperature plasma environments induced by lasers and have attracted a lot of attention from different physical disciplines.However,studies on nuclear reactions in plasma are still limited by detecting technologies.This is mainly due to the fact that extremely high electromagnetic pulses(EMPs)can also be induced when high-intensity lasers hit targets to induce plasma,and then cause dysfunction of many types of traditional detectors.Therefore,new particle detecting technologies are highly needed.In this paper,we report a recently developed gated fiber detector which can be used in harsh EMP environments.In this prototype detector,scintillating photons are coupled by fiber and then transferred to a gated photomultiplier tube which is located far away from the EMP source and shielded well.With those measures,the EMPs can be avoided which may result that the device has the capability to identify a single event of nuclear reaction products generated in laser-induced plasma from noise EMP backgrounds.This new type of detector can be widely used as a time-of-flight(TOF)detector in high-intensity laser nuclear physics experiments for detecting neutrons,photons,and other charged particles.
基金funded by“The Pearl River Talent Recruitment Program”of Guangdong Province in 2019(Grant No.2019CX01G338)the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.
基金the financial support from the National Natural Science Foundation of China (No. 32071342)Guangdong Special Support Program (No. 2019TQ05Y209)+5 种基金Natural Science Foundation of Guangdong Province (No. 2021A1515010431)Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No. SZGSP001)Shenzhen Key Laboratory of Kindey Diseases(No. ZDSYS201504301616234)the Key Project of Basic Research of Shenzhen (No. JCYJ20200109113603854)the International Cooperation Research Project of Shenzhen (No. GJHZ20180418190557102)the Special Funds of Key Disciplines Construction from Guangdong and Zhongshan Cooperating。
文摘Drug delivery systems(DDS) are used to deliver therapeutic drugs to improve selectivity and reduce side effects. With the development of nanotechnology, many nanocarriers have been developed and applied to drug delivery, including mesoporous silica. Mesoporous silica nanoparticles(MSNs) have attracted a lot of attention for simple synthesis, biocompatibility, high surface area and pore volume. Based on the pore system and surface modification, gated mesoporous silica nanoparticles can be designed to realize on-command drug release, which provides a new approach for selective delivery of antitumor drugs.Herein, this review mainly focuses on the “gate keepers” of mesoporous silica for drug controlled release in nearly few years(2017–2020). We summarize the mechanism of drug controlled release in gated MSNs and different gated materials: inorganic gated materials, organic gated materials, self-gated drug molecules, and biological membranes. The facing challenges and future prospects of gated MSNs are discussed rationally in the end.
文摘Voltage gated calcium channel(VGCC) antibodies are generally associated with Lambert-Eaton myasthenic syndrome. However the presence of this antibody has been associated with paraneoplastic as well as nonparaneoplastic cerebellar degeneration. Most patients with VGCC-antibody-positivity have small cell lung cancer(SCLC). Lambert-Eaton myasthenic syndrome(LEMS)is an autoimmune disease of the presynaptic part of the neuromuscular junction. Its classical clinical triadis proximal muscle weakness, areflexia and autonomic dysfunction. Fifty to sixty percent of LEMS patients have a neoplasia, usually SCLC. The co-occurrence of SCLC and LEMS causes more severe and progressive disease and shorter survival than non-paraneoplastic LEMS. Treatment includes 3,4 diaminopyridine for symptomatic purposes and immunotherapy with prednisolone, azathioprine or intravenous immunoglobulin in patients unresponsive to 3,4 diaminopyridine. Paraneoplastic cerebellar degeneration(PCD) is a syndrome characterized with severe, subacute pancerebellar dysfunction. Serum is positive for VGCC antibody in 41%-44% of patients, usually with the co-occurrence of SCLC. Clinical and electrophysiological features of LEMS are also present in 20%-40% of these patients. Unfortunately, PCD symptoms do not improve with immunotherapy. The role of VGCC antibody in the immunopathogenesis of LEMS is well known whereas its role in PCD is still unclear. All patients presenting with LEMS or PCD must be investigated for SCLC.
基金jointly supported by the National Science Foundation of China (Grant Nos. 42275007 and 41865003)Jiangxi Provincial Department of science and technology project (Grant No. 20171BBG70004)。
文摘The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.
基金Supported in part by Natural Science Foundation of China(Grant Nos.51835009,51705398)Shaanxi Province 2020 Natural Science Basic Research Plan(Grant No.2020JQ-042)Aeronautical Science Foundation(Grant No.2019ZB070001).
文摘As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.
基金supported by the National Natural Science Foundation of China (6202201562088101)+1 种基金Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)Shanghai Municip al Commission of Science and Technology Project (19511132101)。
文摘Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.
