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ENTROPY IMMUNITY OF FEEDFORWARD NETWORKS 认领 引用 被引量：1
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作者 杨义先 胡正名 《Journal of Electronics(China)》 1991年第4期297-306,共10页
Siegenthaler’s“Correlation Immunity”concept has been improved in this paper.From practical points of view,the new concept is more powerful than the original one in avoid-ing the trade-off between“the order of corr... Siegenthaler’s“Correlation Immunity”concept has been improved in this paper.From practical points of view,the new concept is more powerful than the original one in avoid-ing the trade-off between“the order of correlation immunity”and“the linear complexity”of keystreams in cipher system.Bent functions are also introduced into the studies of linear approxima-tion and entropy immunity for feedforward networks.New results and new methods are presentedalso. 展开更多
关键词 Cryptography Feedforward Networks Correlation Immunity Entropy Immunity
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Application of feedforward and recurrent neural networks for model-based control systems 认领 引用 被引量：1
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作者 Marek Krok Wojciech P.Hunek +2 位作者 Szymon Mielczarek Filip Buchwald Adam Kolender 《Control Theory and Technology》 EI CSCD 2025年第1期91-104,共14页
In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the l... In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application. 展开更多
关键词 Predictive control Linear-quadratic control Inverse problems Feedforward network Recurrent neural network Optimization
Extraction of fissile isotope antineutrino spectra using feedforward neural network 认领 引用 被引量：1
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作者 Jian Chen Jun Wang +1 位作者 Wei Wang Yue-Huan Wei 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第10期13-23,共11页
The precise measurement of the antineutrino spectra produced by isotope fission in reactors is of great significance for studying neutrino oscillations,refining nuclear databases,and addressing the reactor antineutrin... The precise measurement of the antineutrino spectra produced by isotope fission in reactors is of great significance for studying neutrino oscillations,refining nuclear databases,and addressing the reactor antineutrino anomaly.In this paper,we report a method that utilizes a feedforward neural network(FNN)model to decompose the prompt energy spectrum observed in a short-baseline reactor neutrino experiment and extract the antineutrino spectra produced by the fission of major isotopes such as235U,238U,239Pu,and241Pu in the nuclear reactor.We present two training strategies for the model and compare them with the traditional X2 minimization method by applying them to the same set of pseudo-data corresponding to a total exposure of(2.9×5×1800)GWth·tons·days.The results show that the FNN model not only converges faster and better during the fitting process but also achieves relative errors of less than 1%in the 2−8 MeV range in the extracted spectra,outperforming the X2 minimization method.The feasibility and superiority of this method were validated in the study. 展开更多
关键词 Reactor neutrinos Isotope antineutrino spectra Feedforward neural network
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Predicting Concrete Strength Using Data Augmentation Coupled with Multiple Optimizers in Feedforward Neural Networks 认领 引用 被引量：1
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作者 Sandeerah Choudhary Qaisar Abbas +3 位作者 Tallha Akram Irshad Qureshi Mutlaq B.Aldajani Hammad Salahuddin 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期1755-1787,共33页
The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive st... The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters.This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks(FFNN),Random Forest(RF),and XGBoost.A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:Bayesian Regularization,Levenberg-Marquardt,and three conjugate gradient variants—Powell/Beale Restarts,Fletcher-Powell,and Polak-Ribiere.Hyperparameter tuning,dropout regularization,and early stopping were employed to enhance generalization.Comparative analysis revealed that FFNN outperformed RF and XGBoost,achieving an R2 of 0.9669.To ensure interpretability,accumulated local effects(ALE)along with partial dependence plots(PDP)were utilized.This revealed trends consistent with the pre-existent domain knowledge.This allows estimation of strength from the properties of the mix without extensive lab testing,permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization. 展开更多
关键词 Feedforward neural networks recycled aggregates compressive strength prediction optimization techniques data augmentation grid search
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Real-Time Ship Roll Prediction via a Novel Stochastic Trainer-Based Feedforward Neural Network 认领 引用
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作者 XU Dong-xing YIN Jian-chuan 《China Ocean Engineering》 SCIE EI CSCD 2025年第4期608-620,共13页
Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dyn... Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy. 展开更多
关键词 ship roll prediction data preprocessing strategy sliding data widow improved black widow optimization algorithm stochastic trainer feedforward neural network
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Mechanism for propagation of rate signals through a 10-layer feedforward neuronal network 认领 引用 被引量：5
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作者 李捷 于婉卿 +2 位作者 徐定 刘锋 王炜 《Chinese Physics B》 SCIE EI CAS 2009年第12期5560-5565,共6页
Using numerical simulations, we explore the mechanism for propagation of rate signals through a 10-layer feed-forward network composed of Hodgkin-Huxley （HH） neurons with sparse connectivity. When white noise is aff... Using numerical simulations, we explore the mechanism for propagation of rate signals through a 10-layer feed-forward network composed of Hodgkin-Huxley （HH） neurons with sparse connectivity. When white noise is afferent to the input layer, neuronal firing becomes progressively more synchronous in successive layers and synchrony is well developed in deeper layers owing to the feedforward connections between neighboring layers. The synchrony ensures the successful propagation of rate signals through the network when the synaptic conductance is weak. As the synaptic time constant Tsyn varies, coherence resonance is observed in the network activity due to the intrinsic property of HH neurons. This makes the output firing rate single-peaked as a function of Tsyn, suggesting that the signal propagation can be modulated by the synaptic time constant. These results are consistent with experimental results and advance our understanding of how information is processed in feedforward networks. 展开更多
关键词 feedforward network synchrony rate coding Hodgkin-Huxley model
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Development of a Novel Feedforward Neural Network Model Based on Controllable Parameters for Predicting Effluent Total Nitrogen 认领 引用 被引量：6
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作者 Zihao Zhao Zihao Wang +5 位作者 Jialuo Yuan Jun Ma Zheling He Yilan Xu Xiaojia Shen Liang Zhu 《Engineering》 SCIE EI CAS CSCD 2021年第2期195-202,共8页
The problem of effluent total nitrogen(TN)at most of the wastewater treatment plants(WWTPs)in China is important for meeting the related water quality standards,even under the condition of high energy consumption.To a... The problem of effluent total nitrogen(TN)at most of the wastewater treatment plants(WWTPs)in China is important for meeting the related water quality standards,even under the condition of high energy consumption.To achieve better prediction and control of effluent TN concentration,an efficient prediction model,based on controllable operation parameters,was constructed in a sequencing batch reactor process.Compared with previous models,this model has two main characteristics:①Superficial gas velocity and anoxic time are controllable operation parameters and are selected as the main input parameters instead of dissolved oxygen to improve the model controllability,and②the model prediction accuracy is improved on the basis of a feedforward neural network(FFNN)with algorithm optimization.The results demonstrated that the FFNN model was efficiently optimized by scaled conjugate gradient,and the performance was excellent compared with other models in terms of the correlation coefficient(R).The optimized FFNN model could provide an accurate prediction of effluent TN based on influent water parameters and key control parameters.This study revealed the possible application of the optimized FFNN model for the efficient removal of pollutants and lower energy consumption at most of the WWTPs. 展开更多
关键词 Feedforward neural network(FFNN) Algorithms Controllable operation parameters Sequencing batch reactor(SBR) Total nitrogen(TN)
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A Novel Evolutionary Feedforward Neural Network with Artificial Immunology 认领 引用
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作者 宫新保 臧小刚 周希朗 《Journal of Shanghai Jiaotong university(Science)》 2003年第1期40-42,共3页
A hybrid algorithm to design the multi layer feedforward neural network was proposed. Evolutionary programming is used to design the network that makes the training process tending to global optima. Artificial immunol... A hybrid algorithm to design the multi layer feedforward neural network was proposed. Evolutionary programming is used to design the network that makes the training process tending to global optima. Artificial immunology combined with simulated annealing algorithm is used to specify the initial weight vectors, therefore improves the probabiligy of training algorithm to converge to global optima. The applications of the neural network in the modulation style recognition of analog modulated rader signals demonstrate the good performance of the network. 展开更多
关键词 feedforward neural networks evolutionary programming artificial immunology
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IDENTIFICATION OF NONLINEAR TIME VARYING SYSTEM USING FEEDFORWARD NEURAL NETWORKS 认领 引用
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作者 WANG Zheng-ou ZHAO Chang-hai 《Transactions of Tianjin University》 CAS 2000年第1期8-13,共6页
As it is well known,it is difficult to identify a nonlinear time varying system using tradi-tional identification approaches,especially under unknown nonlinear function.Neural net works have recently emerged as a succ... As it is well known,it is difficult to identify a nonlinear time varying system using tradi-tional identification approaches,especially under unknown nonlinear function.Neural net works have recently emerged as a successful tool in the area of identification and control of time invariant nonlinear systems.However,it is still difficult to apply them to complicated time varying system i-dentification.In this paper we present a learning algorithm for identification of the nonlinear time varying system using feedforward neural net works.The main idea of this approach is that we re-gard the weights of the network as a state of a time varying system,then use a Kalman filter to es timate the state.Thus the network implements nonlinear and time varying mapping.We derived both the global and local learning algorithms.Simulation results demonstrate the effectiveness of this approach. 展开更多
关键词 identification nonlinear time varying system feedforward neural network Kalman fil-ter Q and R matrices
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IDENTIFICATION OF NONLINEAR TIME VARYING SYSTEM USING FEEDFORWARD NEURAL NETWORKS 认领 引用 被引量：2
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作者 王正欧 赵长海 《Transactions of Tianjin University》 CAS 2000年第1期8-13,共6页
As it is well known,it is difficult to identify a nonlinear time varying system using traditional identification approaches,especially under unknown nonlinear function.Neural networks have recently emerged as a succes... As it is well known,it is difficult to identify a nonlinear time varying system using traditional identification approaches,especially under unknown nonlinear function.Neural networks have recently emerged as a successful tool in the area of identification and control of time invariant nonlinear systems.However,it is still difficult to apply them to complicated time varying system identification.In this paper we present a learning algorithm for identification of the nonlinear time varying system using feedforward neural networks.The main idea of this approach is that we regard the weights of the network as a state of a time varying system,then use a Kalman filter to estimate the state.Thus the network implements nonlinear and time varying mapping.We derived both the global and local learning algorithms.Simulation results demonstrate the effectiveness of this approach. 展开更多
关键词 identification nonlinear time varying system feedforward neural network Kalman filter Q and R matrices
全文增补中
Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar,Gujarat,India 认领 引用 被引量：2
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作者 Apurwa Yadav Kriti Yadav Anirbid Sircar 《Energy Geoscience》 EI 2021年第3期189-200,共12页
Artificial Neural Networks(ANNs)are used in numerous engineering and scientific disciplines as an automated approach to resolve a number of problems.However,to build an artificial neural network that is prudent enough... Artificial Neural Networks(ANNs)are used in numerous engineering and scientific disciplines as an automated approach to resolve a number of problems.However,to build an artificial neural network that is prudent enough to rely on,vast quantities of relevant data have to be fed.In this study,we analysed the scope of artificial neural networks in geothermal reservoir architecture.In particular,we attempted to solve joint inversion problem through Feedforward Neural Network(FNN)technique.In order to identify geothermal sweet spots in the subsurface,an extensive geophysical studies were conducted in Gandhar area of Gujarat,India.The data were acquired along six profile lines for gravity,magnetics and magnetotellurics.Initially low velocity zone was identified using refraction seismic technique in order to set a common datum level for other potential data.The depth of low velocity zone in Gandhar was identified at 11 m.The FNN backpropagation method was applied to gain the global minima of the data space and model space as desired.The input dataset fed to the inversion algorithm in the form of gravity,magnetic susceptibility and resistivity helped to predict the suitable model after network training in multiple steps.The joint inversion of data is conducive to understanding the subsurface geological and lithological features along with probable geothermal sweet spots.The results of this study show the geothermal sweet spots at depth ranging from 200 m to 300 m.The results from our study can be used for targeted zones for geothermal water exploitation. 展开更多
关键词 Artificial neural network(ANN) Geotherm Feedforward neural network(FNN) Geophysics Machine learning(ML)
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Fully Connected Feedforward Neural Networks Based CSI Feedback Algorithm 认领 引用 被引量：1
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作者 Ming Gao Tanming Liao Yubin Lu 《China Communications》 SCIE EI CSCD 2021年第1期43-48,共6页
In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of... In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity. 展开更多
关键词 massive MIMO CSI feedback deep learning fully connected feedforward neural network
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Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model 认领 引用 被引量：1
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作者 Yiwen Zhang Wei Zheng Zongqiang Liu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第2期271-284,共14页
Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at... Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry. 展开更多
关键词 GNSS-R satellite constellations Sea surface altimetric precision Underwater navigation Multilayer feedforward neural network
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Application of a multilayer feedforward network to voiced-unvoiced-silence classifications of speech 认领 引用
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作者 QI Yingyong 《Chinese Journal of Acoustics》 1992年第2期167-178,共12页
A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net -work. Speech signals were analyzed sequentially and a feature vector was obtaine... A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net -work. Speech signals were analyzed sequentially and a feature vector was obtained for each segment . The feature vector served as input to a 3-layer feedforward network in which voiced, unvoiced, and silence classification was made. The network had a 6-12-3 node architecture and was trained using the generalized delta rule for back propagation of error . The performance of the network was evaluated using speech samples from 3 male and 3 female speakers . A speaker-dependent classification rate of 94.7% and speaker-independent classification rate of 94.3% were obtained. It is concluded that the voiced, unvoiced , and silence classification of speech can be effectively accomplished using a multilayer feedforward network. 展开更多
关键词 work IEEE net Application of a multilayer feedforward network to voiced-unvoiced-silence classifications of speech
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Generalization Capabilities of Feedforward Neural Networks for Pattern Recognition 认领 引用
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作者 黄德双 《Journal of Beijing Institute of Technology》 EI CAS 1996年第2期192+184-192,共193页
This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that th... This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs. 展开更多
关键词 feedforward neural networks radial basis function networks multilayer perceptronnetworks, generalization capability radar target classification
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Relations Between Wavelet Network and Feedforward Neural Network 认领 引用 被引量：1
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作者 刘志刚 何正友 钱清泉 《Journal of Southwest Jiaotong University(English Edition)》 2002年第2期179-184,共6页
A comparison of construction forms and base functions is made between feedforward neural network and wavelet network. The relations between them are studied from the constructions of wavelet functions or dilation func... A comparison of construction forms and base functions is made between feedforward neural network and wavelet network. The relations between them are studied from the constructions of wavelet functions or dilation functions in wavelet network by different activation functions in feedforward neural network. It is concluded that some wavelet function is equal to the linear combination of several neurons in feedforward neural network. 展开更多
关键词 wavelet transformation feedforward neural network wavelet network
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A Modified Algorithm for Feedforward Neural Networks 认领 引用
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作者 夏战国 管红杰 +1 位作者 李政伟 孟斌 《Journal of China University of Mining and Technology》 2002年第1期103-107,共5页
As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. A... As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a modified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable. 展开更多
关键词 feedforward neural networks BP learning algorithm network complexity learning step size
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A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices:A cross-sectional study 认领 引用 被引量：3
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作者 Miaoran Guo Hu Liu +6 位作者 Long Gao Hongmei Yu Yan Ren Yingmei Li Huaguang Yang Chenghao Cao Guoguang Fan 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第4期1621-1627,共7页
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice... Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease. 展开更多
关键词 amplitude of low-frequency fluctuation degree centrality feedforward neural network freezing of gait machine learning parahippocampal gyrus Parkinson's disease receiver operating characteristic regional homogeneity resting-state functional magnetic resonance imaging
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基于频域Mamba与递归门控Transformer融合的路面裂缝分割方法 认领 引用 被引量：1
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作者 王建新 李林益 +2 位作者 王进 李毅 吕松涛 《中国公路学报》 EI CAS CSCD 北大核心 2026年第4期18-34,共17页
路面裂缝分割是道路养护管理的关键技术,对提升道路安全性与耐久性至关重要。然而,由于路面场景复杂多样、裂缝形态的不规则性以及背景噪声干扰,实施高精度裂缝分割仍然面临诸多挑战。为此,提出一种基于频域Mamba与递归门控Transformer... 路面裂缝分割是道路养护管理的关键技术,对提升道路安全性与耐久性至关重要。然而,由于路面场景复杂多样、裂缝形态的不规则性以及背景噪声干扰,实施高精度裂缝分割仍然面临诸多挑战。为此,提出一种基于频域Mamba与递归门控Transformer融合的路面裂缝分割方法,通过深度融合频域建模与空间特征提取,有效提升了模型对裂缝边缘特征和全局上下文信息的捕获能力。首先,采用大核递归注意机制构建Transformer架构作为U型网络骨干,通过增强空间特征交互,实现模块内局部细节与全局上下文特征的有效融合。其次,在前馈网络(Feedforward Network,FFN)中集成Sobel算子与门控机制,动态调节信息流,显著提升了边缘特征提取能力及噪声抑制效果。最后,提出一种嵌入跳跃连接的频域Mamba增强模块,通过在小波变换的频域分解中引入状态空间模型(State Space Model,SSM),旨在增强频域和空间域的特征融合,以补偿下采样的细节信息损失。为验证所提方法的有效性,在DeepCrack537、CrackLS315和YCD数据集上进行了系统的试验与对比分析。试验结果表明:该方法在视觉效果与定量指标上均优于当前主流的分割方法,展现了良好的鲁棒性与实用价值,为路面裂缝的智能化检测提供了新的解决方案。 展开更多
关键词 路面工程 路面裂缝 频域Mamba 递归门控Transformer 前馈网络 状态空间模型
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A Novel Control-Protection Coordination Strategy of Power Lines Connected with Inverter-Based Resources 认领 引用
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作者 Sumei Liu Jizeng Wu +1 位作者 Yinghong Zhang Ran Zheng 《Energy Engineering》 EI 2026年第6期29-57,共29页
The unique fault current characteristics of inverter-based resources(IBRs)result in a variable angle of the additional impedance for conventional distance protection,causing failure to operate for internal faults and ... The unique fault current characteristics of inverter-based resources(IBRs)result in a variable angle of the additional impedance for conventional distance protection,causing failure to operate for internal faults and maloperation for external faults.To address this issue,a novel control-protection coordinated strategy is proposed.The positive-sequence current references of IBRs’inverter control strategy are reset,based on the relationship between the current on the high-voltage side of the transformer and that of the fault-point current.Under such control,the additional impedance angle can be effectively maintained at a constant value.On this basis,a single-ended protection principle immune to the additional impedance is developed.The proposed control-protection coordinated strategy can effectively identify internal and external faults under high-penetration IBRs integration,without substantially modifying the existing inverter control and protection framework.Compared with existing control-protection coordinated strategies,the proposed improved control strategy will not affect the stability of inverter control,nor will it generate additional harmonics.The proposed protection strategy still adopts conventional power-frequency voltage and current quantities for fault identification,facilitating its implementation. 展开更多
关键词 Mining microgrids energy management system long short-term memory feedforward neural networks safety constraints renewable energy integration predictive control convex optimization
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