The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to ...To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.展开更多
In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS f...In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS factors. In this paper, we develop a novel vertical handoff algorithm in an integrated 3G cellular and Wireless LAN networks. The proposed algorithm can adjust the weight of each QoS attribute dynamically as the networks change, trace the network condition and choose the optimal access point at transient regions. Simulation results show that this algorithm is able to provide accurate handoff decision, resulting in small unnecessary handoff numbers, good performance of throughput and handoff delay in heterogeneous environments.展开更多
The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the ...The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.展开更多
Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight d...Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight distribution for operational evaluation was developed, and multiple-forecaster synchronous forecasting was realized while avoiding the instability cased by only one forecaster. Weights were distributed to the forecasters according to each one＇s forecast precision. An evaluation criterion for the professional level of the forecasters was also built. The eligibility rates of forecast results demonstrate the skill of the forecasters and the stability of their forecasts. With the developed tide forecasting method, the precision and reasonableness of tide forecasting are improved. The application of the present method to tide forecasting at the Huangpu Park tidal station demonstrates the validity of the method.展开更多
Using the RFM(Recency,Frequency,Monetary value)model can provide valuable insights about customer clusterswhich is the core of customer relationship management.Due to accurate customer segment coming from dynamic weig...Using the RFM(Recency,Frequency,Monetary value)model can provide valuable insights about customer clusterswhich is the core of customer relationship management.Due to accurate customer segment coming from dynamic weighted applications,in-depth targeted marketing may also use type of dynamic weight of R,F and M as factors.In this paper,we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights.Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set.Firstly,Analytic Hierarchy Process is used to calculate the subjective weight,then the entropy method is applied to calculate the objective weight.Finally,we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference.The experiment shows that the dynamic weight we used in RFM model is vital,affects the customer segmentation performance positively.Also,this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior.At last,we discuss the limitations of RFM analysis.展开更多
Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
Accurately diagnosing skin lesion disease is a challenging task.Although present methods often use the multi-branch structure to get more clues,the rigescent methods of cropping zone and fusing branch results fail to ...Accurately diagnosing skin lesion disease is a challenging task.Although present methods often use the multi-branch structure to get more clues,the rigescent methods of cropping zone and fusing branch results fail to handle the instability of the disease zone and the difference in branch results,which leads to improper cropping and degrades Deep Convolutional Neural Networks(DCNN)’s performance.To address these problems,we propose a Multi-scale DCNN with Dynamic weight and Part cross-entropy loss model(namely MDP-DCNN)to bootstrap skin lesion diagnosis.Inspired by the object detection method,the multi-scale structure adjusts the cropping position based on the Gradient-weighted Class Activation Mapping(Grad-CAM)center.It enables the model to adapt to the disease zone variety in position and size.The dynamic weight structure alleviates the negative influence of branch differences by comparing the grey-cropped zone and its CAM.Moreover,we also propose the part cross-entropy loss to deal with the over-fitting problem.This optimizes the non-targeted label to decrease the influence on other labels’stability when the prediction is wrong.We conduct our model on the ISIC-2017 and ISIC-2018 datasets.Experiments demonstrate that MDP-DCNN achieves excellent results in skin lesion classification without external data.Multi-scale DCNN with dynamic weight and part loss function verifies its advantages in enhancing diagnosis accuracy.展开更多
In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical m...In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.展开更多
A novel scheme to construct a hash function based on a weighted complex dynamical network （WCDN） generated from an original message is proposed in this paper. First, the original message is divided into blocks. Then...A novel scheme to construct a hash function based on a weighted complex dynamical network （WCDN） generated from an original message is proposed in this paper. First, the original message is divided into blocks. Then, each block is divided into components, and the nodes and weighted edges are well defined from these components and their relations. Namely, the WCDN closely related to the original message is established. Furthermore, the node dynamics of the WCDN are chosen as a chaotic map. After chaotic iterations, quantization and exclusive-or operations, the fixed-length hash value is obtained. This scheme has the property that any tiny change in message can be diffused rapidly through the WCDN, leading to very different hash values. Analysis and simulation show that the scheme possesses good statistical properties, excellent confusion and diffusion, strong collision resistance and high efficiency.展开更多
In recent years,diffusion models have achieved remarkable progress in image generation.However,extending them to text-to-video(T2V)generation remains challenging,particularly in maintaining semantic consistency and vi...In recent years,diffusion models have achieved remarkable progress in image generation.However,extending them to text-to-video(T2V)generation remains challenging,particularly in maintaining semantic consistency and visual quality across frames.Existing approaches often overlook the synergy between high-level semantics and low-level texture information,resulting in blurry or temporally inconsistent outputs.To address these issues,we propose Dual Consistency Training(DCT),a novel framework designed to jointly optimize semantic and texture consistency in video generation.Specifically,we introduce a multi-scale spatial adapter to enhance spatial feature extraction,and leverage the complementary strengths of CLIP and VGG—where CLIP focuses on high-level semantics and VGG captures fine-grained texture and detail.During training,a stepwise strategy is adopted to impose semantic and texture losses,constraining discrepancies between generated and ground-truth frames.Furthermore,we propose CLWS,which dynamically adjusts the balance between semantic and texture losses to facilitate more stable and effective optimization.Remarkably,DCT achieves high-quality video generation using only a single training video on a single NVIDIA A6000 GPU.Extensive experiments demonstrate that our method significantly improves temporal coherence and visual fidelity across various video generation tasks,verifying its effectiveness and generalizability.展开更多
Grasping is one of the most fundamental operations in modern robotics applications.While deep rein-forcement learning(DRL)has demonstrated strong potential in robotics,there is too much emphasis on maximizing the cumu...Grasping is one of the most fundamental operations in modern robotics applications.While deep rein-forcement learning(DRL)has demonstrated strong potential in robotics,there is too much emphasis on maximizing the cumulative reward in executing tasks,and the potential safety risks are often ignored.In this paper,an optimization method based on safe reinforcement learning(Safe RL)is proposed to address the robotic grasping problem under safety constraints.Specifically,considering the obstacle avoidance constraints of the system,the grasping problem of the manipulator is modeled as a Constrained Markov Decision Process(CMDP).The Lagrange multiplier and a dynamic weighted mechanism are introduced into the Proximal Policy Optimization(PPO)framework,leading to the development of the dynamic weighted Lagrange PPO(DWL-PPO)algorithm.The behavior of violating safety constraints is punished while the policy is optimized in this proposed method.In addition,the orientation control of the end-effector is included in the reward function,and a compound reward function adapted to changes in pose is designed.Ultimately,the efficacy and advantages of the suggested method are proved by extensive training and testing in the Pybullet simulator.The results of grasping experiments reveal that the recommended approach provides superior safety and efficiency compared with other advanced RL methods and achieves a good trade-off between model learning and risk aversion.展开更多
A dynamic weight function method is presented for dynamic stress intensity factors of circular disk with a radial edge crack under external impulsive pressure. The dynamic stresses in a circular disk are solved under ...A dynamic weight function method is presented for dynamic stress intensity factors of circular disk with a radial edge crack under external impulsive pressure. The dynamic stresses in a circular disk are solved under abrupt step external pressure using the eigenfunction method. The solution consists of a quasi-static solution satisfying inhomogeneous boundary conditions and a dynamic solution satisfying homogeneous boundary conditions. By making use of Fourier- Bessel series expansion, the history and distribution of dynamic stresses in the circular disk are derived. Furthermore, the equation for stress intensity factors under uniform pressure is used as the reference case, the weight function equation for the circular disk containing an edge crack is worked out, and the dynamic stress intensity factor equation for the circular disk containing a radial edge crack can be given. The results indicate that the stress intensity factors under sudden step external pressure vary periodically with time, and the ratio of the maximum value of dynamic stress intensity factors to the corresponding static value is about 2.0.展开更多
Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and...Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and quantity of the sources are commonly unknown.Existing multi-source search methods fail to accurately estimate the source term,primarily due to the inefficient utilization of concentration information.This limitation results in sub-optimal drone movement strategies.To address these issues,we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis(DLW-CI)approach.The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism.Specifically,we devise a novel source term estimation method that leverages multiple parallel particle filters,with each filter estimating the parameters of a potentially unknown source in scenarios.Subsequently,we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source.The results show that the success rate for the localization of 2-4 diffusion sources reaches 90%,78%,and 42% respectively when employing the DLW-CI approach,achieving a 37%average improvement over baseline methods.Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search,making a valuable contribution to environmental safety monitoring applications.展开更多
Title:A dual-parameter method for seismic resilience assessment of buildings Authors:LI Shuang;HU Binbin;LIU Wen;ZHAI Changhai Abstract:To quantify the seismic resilience of buildings,a method for evaluating functiona...Title:A dual-parameter method for seismic resilience assessment of buildings Authors:LI Shuang;HU Binbin;LIU Wen;ZHAI Changhai Abstract:To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on postearthquake loss and recovery time is improved.A three-level function tree model is established,which can consider the dynamic changes in weight coefficients of different category of components relative to their functional losses.Bayesian networks are utilized to quantify the impact of weather conditions,construction technology levels,and worker skill levels on component repair time.A method for determining the real-time functional recovery curve of buildings based on the component repair process is proposed.Taking a three-story teaching building as an example,the seismic resilience indices under basic earthquakes and rare earthquakes are calculated.The results show that the seismic resilience grade of the teaching building is comprehensively judged as GradeⅢ,and its resilience grade is more significantly affected by postearthquake loss.The proposed method can be used to predict the seismic resilience of buildings prior to earthquakes,identify weak components within buildings,and provide guidance for taking measures to enhance the seismic resilience of buildings.展开更多
Dynamic stress intensity factors are evaluated for thick-walled cylinder with a radial edge crack under internal impulsive pressure. Firstly, the equation for stress intensity factors under static uniform pressure is ...Dynamic stress intensity factors are evaluated for thick-walled cylinder with a radial edge crack under internal impulsive pressure. Firstly, the equation for stress intensity factors under static uniform pressure is used as the reference case, and then the weight function for a thick-walled cylinder containing a radial edge crack can be worked out. Secondly, the dynamic stresses in uncracked thick-walled cylinders are solved under internal impulsive pressure by using mode shape function method. The solution consists of a quasi-static solution satisfying inhomogeneous boundary conditions and a dynamic solution satisfying homogeneous boundary condi- tions, and the history and distribution of dynamic stresses in thick-walled cylinders are derived in terms of Fourier-Bessel series. Finally, the dynamic stress intensity factor equations for thick-walled cylinder containing a radial edge crack sub- jected to internal impulsive pressure are given by dynamic weight function method. The finite element method is utilized to verify the results of numerical examples, showing the validity and feasibility of the proposed method.展开更多
Static strength finite element analysis was conducted to decrease the weight of a skeleton vehicle＇s frame. Results indicated that the maximum stress occurs on the front beam ＇s variable section area. Dynamic sensit...Static strength finite element analysis was conducted to decrease the weight of a skeleton vehicle＇s frame. Results indicated that the maximum stress occurs on the front beam ＇s variable section area. Dynamic sensitivity analysis elucidated the relationship between the maximum stress and the thickness of a particular beam,e. g.,top,middle,and bottom beam. Displacement was analyzed by the key part that influenced the maximum stress. Finally,the new plan using BS960 super-high-strength beam steel and the preferred beam thickness was compared with the original plan. New combinations of beam thickness were introduced on the basis of different purposes; the maximum responding light w eight ratio was 21%.展开更多
Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships,controlling redundant information,and improving model robustness.In this study,...Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships,controlling redundant information,and improving model robustness.In this study,we propose a Dynamic Conditional Feature Screening(DCFS)method tailored for high-dimensional economic forecasting tasks.Our goal is to accurately identify key variables,enhance predictive performance,and provide both theoretical foundations and practical tools for macroeconomic modeling.The DCFS method constructs a comprehensive test statistic by integrating conditional mutual information with conditional regression error differences.By introducing a dynamic weighting mechanism,DCFS adaptively balances the linear and nonlinear contributions of features during the screening process.In addition,a dynamic thresholding mechanism is designed to effectively control the false discovery rate(FDR),thereby improving the stability and reliability of the screening results.On the theoretical front,we rigorously prove that the proposed method satisfies the sure screening property and rank consistency,ensuring accurate identification of the truly important feature set in high-dimensional settings.Simulation results demonstrate that under purely linear,purely nonlinear,and mixed dependency structures,DCFS consistently outperforms classical screening methods such as SIS,CSIS,and IG-SIS in terms of true positive rate(TPR),false discovery rate(FDR),and rank correlation.These results highlight the superior accuracy,robustness,and stability of our method.Furthermore,an empirical analysis based on the U.S.FRED-MD macroeconomic dataset confirms the practical value of DCFS in real-world forecasting tasks.The experimental results show that DCFS achieves lower prediction errors(RMSE and MAE)and higher R2 values in forecasting GDP growth.The selected key variables-including the Industrial Production Index(IP),Federal Funds Rate,Consumer Price Index(CPI),and Money Supply(M2)-possess clear economic interpretability,offering reliable support for economic forecasting and policy formulation.展开更多
Automatic gauge control(AGC in the article)is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill.Most traditional AGC control algorithms need stable external system condi...Automatic gauge control(AGC in the article)is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill.Most traditional AGC control algorithms need stable external system conditions and hard to stabilize under complex interference that meets coverage requirements.This paper presents a new anti-interference strategy for AGC control of 20-Hi cold reversing mill.The proposed algorithm introduces a united dynamic weights algorithm of feed forward-mass flow to avoid the complex interference problem in AGC control,the relevant control strategy is provided to eliminate the adverse effects.At the same time,the D-value between feed forward-mass flow pre-computation and thickness measurement deviation is dynamic compared,the final gap position regulation is calculated by developing a set of united dynamic weights between feed forward control and mass flow control.Finally,the output of controllers is sent to actuator though a constant rate smoothing.The proposed strategy is compared with conventional AGC control on Experimental platform and project application,the results show that the proposed strategy is more stable than comparison method and majority of system uncertainty produced by mentioned interference is significantly eliminated.展开更多
Summary： The purpose of this study was to quantitatively analyze the relationship between three di- mensional arterial spin labeling （3D-ASL） and dynamic susceptibility contrast-enhanced perfusion weighted imaging ...Summary： The purpose of this study was to quantitatively analyze the relationship between three di- mensional arterial spin labeling （3D-ASL） and dynamic susceptibility contrast-enhanced perfusion weighted imaging （DSC-PWI） in ischemic stroke patients. Thirty patients with ischemic stroke were in- cluded in this study. All subjects underwent routine magnetic resonance imaging scanning, diffusion weighted imaging （DWI）, magnetic resonance angiography （MRA）, 3D-ASL and DSC-PWI on a 3.0T MR scanner. Regions of interest （ROIs） were drawn on the cerebral blood flow （CBF） maps （derived from ASL） and multi-parametric DSC perfusion maps, and then, the absolute and relative values of ASL-CBF, DSC-derived CBF, and DSC-derived mean transit time （MTT） were calculated. The rela- tionships between ASL and DSC parameters were analyzed using Pearson＇s correlation analysis. Re- ceiver operative characteristic （ROC） curves were performed to define the thresholds of relative value of ASL-CBF （rASL） that could best predict DSC-CBF reduction and MTT prolongation. Relative ASL better correlated with CBF and MTT in the anterior circulation with the Pearson correlation coefficients （R） values being 0.611 （P〈0.001） and-0.610 （P〈0.001） respectively. ROC curves demonstrated that when rASL 〈0.585, the sensitivity, specificity and accuracy for predicting ROIs with rCBF〈0.9 were 92.3%, 63.6% and 76.6% respectively. When rASL 〈0.952, the sensitivity, specificity and accuracy for predicting ROIs rMTT〉I.0 were 75.7%, 89.2% and 87.8% respectively. ASL-CBF map has better linear correlations with DSC-derived parameters （DSC-CBF and MTT） in anterior circulation in ischemic stroke patients. Additionally, when rASL is lower than 0.585, it could predict DSC-CBF decrease with moderate accuracy. IfrASL values range from 0.585 to 0.952, we just speculate the prolonged MTT.展开更多
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
基金This project was supported by the National Basic Research Programof China (2001CB309403)
文摘To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.
基金Acknowledgements This work is supported by Key Program of National Natural Science Foundation of China Grant No. 60832009.
文摘In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS factors. In this paper, we develop a novel vertical handoff algorithm in an integrated 3G cellular and Wireless LAN networks. The proposed algorithm can adjust the weight of each QoS attribute dynamically as the networks change, trace the network condition and choose the optimal access point at transient regions. Simulation results show that this algorithm is able to provide accurate handoff decision, resulting in small unnecessary handoff numbers, good performance of throughput and handoff delay in heterogeneous environments.
基金Science Research Project of Gansu Provincial Transportation Department(No.2017-012)
文摘The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.
文摘Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight distribution for operational evaluation was developed, and multiple-forecaster synchronous forecasting was realized while avoiding the instability cased by only one forecaster. Weights were distributed to the forecasters according to each one＇s forecast precision. An evaluation criterion for the professional level of the forecasters was also built. The eligibility rates of forecast results demonstrate the skill of the forecasters and the stability of their forecasts. With the developed tide forecasting method, the precision and reasonableness of tide forecasting are improved. The application of the present method to tide forecasting at the Huangpu Park tidal station demonstrates the validity of the method.
基金the National Natural Science Foundation of China(No.72073041)Open Foundation for the University Innovation Platform in Hunan Province(No.18K103)+2 种基金2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project(Nos.20181901CRP03,20181901CRP04,20181901CRP05)2020 Hunan Provincial Higher Education Teaching Reform Research Project(Nos.HNJG-2020-1130,HNJG-2020-1124)2020 General Project of Hunan Social Science Fund(No.20B16).
文摘Using the RFM(Recency,Frequency,Monetary value)model can provide valuable insights about customer clusterswhich is the core of customer relationship management.Due to accurate customer segment coming from dynamic weighted applications,in-depth targeted marketing may also use type of dynamic weight of R,F and M as factors.In this paper,we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights.Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set.Firstly,Analytic Hierarchy Process is used to calculate the subjective weight,then the entropy method is applied to calculate the objective weight.Finally,we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference.The experiment shows that the dynamic weight we used in RFM model is vital,affects the customer segmentation performance positively.Also,this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior.At last,we discuss the limitations of RFM analysis.
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
基金supported by the scholarship from China Scholarship Council(CSC)(No.CSC N°201806280502).
文摘Accurately diagnosing skin lesion disease is a challenging task.Although present methods often use the multi-branch structure to get more clues,the rigescent methods of cropping zone and fusing branch results fail to handle the instability of the disease zone and the difference in branch results,which leads to improper cropping and degrades Deep Convolutional Neural Networks(DCNN)’s performance.To address these problems,we propose a Multi-scale DCNN with Dynamic weight and Part cross-entropy loss model(namely MDP-DCNN)to bootstrap skin lesion diagnosis.Inspired by the object detection method,the multi-scale structure adjusts the cropping position based on the Gradient-weighted Class Activation Mapping(Grad-CAM)center.It enables the model to adapt to the disease zone variety in position and size.The dynamic weight structure alleviates the negative influence of branch differences by comparing the grey-cropped zone and its CAM.Moreover,we also propose the part cross-entropy loss to deal with the over-fitting problem.This optimizes the non-targeted label to decrease the influence on other labels’stability when the prediction is wrong.We conduct our model on the ISIC-2017 and ISIC-2018 datasets.Experiments demonstrate that MDP-DCNN achieves excellent results in skin lesion classification without external data.Multi-scale DCNN with dynamic weight and part loss function verifies its advantages in enhancing diagnosis accuracy.
基金Innovation and Development Project of China Meteorological Administration(CXFZ2023J044)Innovation Foundation of CMA Public Meteorological Service Center(K2023002)+1 种基金“Tianchi Talents”Introduction Plan(2023)Key Innovation Team for Energy and Meteorology of China Meteorological Administration。
文摘In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.
基金Project supported by the Natural Science Foundation of Jiangsu Province, China (Grant No. BK2010526)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20103223110003)The Ministry of Education Research in the Humanities and Social Sciences Planning Fund, China (Grant No. 12YJAZH120)
文摘A novel scheme to construct a hash function based on a weighted complex dynamical network （WCDN） generated from an original message is proposed in this paper. First, the original message is divided into blocks. Then, each block is divided into components, and the nodes and weighted edges are well defined from these components and their relations. Namely, the WCDN closely related to the original message is established. Furthermore, the node dynamics of the WCDN are chosen as a chaotic map. After chaotic iterations, quantization and exclusive-or operations, the fixed-length hash value is obtained. This scheme has the property that any tiny change in message can be diffused rapidly through the WCDN, leading to very different hash values. Analysis and simulation show that the scheme possesses good statistical properties, excellent confusion and diffusion, strong collision resistance and high efficiency.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901]Graduate Innovation Project Funding of Chongqing University of Technology[Grant number gzlcx20253249].
文摘In recent years,diffusion models have achieved remarkable progress in image generation.However,extending them to text-to-video(T2V)generation remains challenging,particularly in maintaining semantic consistency and visual quality across frames.Existing approaches often overlook the synergy between high-level semantics and low-level texture information,resulting in blurry or temporally inconsistent outputs.To address these issues,we propose Dual Consistency Training(DCT),a novel framework designed to jointly optimize semantic and texture consistency in video generation.Specifically,we introduce a multi-scale spatial adapter to enhance spatial feature extraction,and leverage the complementary strengths of CLIP and VGG—where CLIP focuses on high-level semantics and VGG captures fine-grained texture and detail.During training,a stepwise strategy is adopted to impose semantic and texture losses,constraining discrepancies between generated and ground-truth frames.Furthermore,we propose CLWS,which dynamically adjusts the balance between semantic and texture losses to facilitate more stable and effective optimization.Remarkably,DCT achieves high-quality video generation using only a single training video on a single NVIDIA A6000 GPU.Extensive experiments demonstrate that our method significantly improves temporal coherence and visual fidelity across various video generation tasks,verifying its effectiveness and generalizability.
文摘Grasping is one of the most fundamental operations in modern robotics applications.While deep rein-forcement learning(DRL)has demonstrated strong potential in robotics,there is too much emphasis on maximizing the cumulative reward in executing tasks,and the potential safety risks are often ignored.In this paper,an optimization method based on safe reinforcement learning(Safe RL)is proposed to address the robotic grasping problem under safety constraints.Specifically,considering the obstacle avoidance constraints of the system,the grasping problem of the manipulator is modeled as a Constrained Markov Decision Process(CMDP).The Lagrange multiplier and a dynamic weighted mechanism are introduced into the Proximal Policy Optimization(PPO)framework,leading to the development of the dynamic weighted Lagrange PPO(DWL-PPO)algorithm.The behavior of violating safety constraints is punished while the policy is optimized in this proposed method.In addition,the orientation control of the end-effector is included in the reward function,and a compound reward function adapted to changes in pose is designed.Ultimately,the efficacy and advantages of the suggested method are proved by extensive training and testing in the Pybullet simulator.The results of grasping experiments reveal that the recommended approach provides superior safety and efficiency compared with other advanced RL methods and achieves a good trade-off between model learning and risk aversion.
文摘A dynamic weight function method is presented for dynamic stress intensity factors of circular disk with a radial edge crack under external impulsive pressure. The dynamic stresses in a circular disk are solved under abrupt step external pressure using the eigenfunction method. The solution consists of a quasi-static solution satisfying inhomogeneous boundary conditions and a dynamic solution satisfying homogeneous boundary conditions. By making use of Fourier- Bessel series expansion, the history and distribution of dynamic stresses in the circular disk are derived. Furthermore, the equation for stress intensity factors under uniform pressure is used as the reference case, the weight function equation for the circular disk containing an edge crack is worked out, and the dynamic stress intensity factor equation for the circular disk containing a radial edge crack can be given. The results indicate that the stress intensity factors under sudden step external pressure vary periodically with time, and the ratio of the maximum value of dynamic stress intensity factors to the corresponding static value is about 2.0.
基金supported by the National Natural Science Foundation of China 62173337Youth Independent Innovation Foundation of NUDT(ZK-2023-21).
文摘Drones have gradually been employed to search for unknown sources during leakage accidents.However,current studies have mainly focused on the single-source search problem,while in practical situations,the location and quantity of the sources are commonly unknown.Existing multi-source search methods fail to accurately estimate the source term,primarily due to the inefficient utilization of concentration information.This limitation results in sub-optimal drone movement strategies.To address these issues,we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis(DLW-CI)approach.The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism.Specifically,we devise a novel source term estimation method that leverages multiple parallel particle filters,with each filter estimating the parameters of a potentially unknown source in scenarios.Subsequently,we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source.The results show that the success rate for the localization of 2-4 diffusion sources reaches 90%,78%,and 42% respectively when employing the DLW-CI approach,achieving a 37%average improvement over baseline methods.Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search,making a valuable contribution to environmental safety monitoring applications.
文摘Title:A dual-parameter method for seismic resilience assessment of buildings Authors:LI Shuang;HU Binbin;LIU Wen;ZHAI Changhai Abstract:To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on postearthquake loss and recovery time is improved.A three-level function tree model is established,which can consider the dynamic changes in weight coefficients of different category of components relative to their functional losses.Bayesian networks are utilized to quantify the impact of weather conditions,construction technology levels,and worker skill levels on component repair time.A method for determining the real-time functional recovery curve of buildings based on the component repair process is proposed.Taking a three-story teaching building as an example,the seismic resilience indices under basic earthquakes and rare earthquakes are calculated.The results show that the seismic resilience grade of the teaching building is comprehensively judged as GradeⅢ,and its resilience grade is more significantly affected by postearthquake loss.The proposed method can be used to predict the seismic resilience of buildings prior to earthquakes,identify weak components within buildings,and provide guidance for taking measures to enhance the seismic resilience of buildings.
基金supported by the China Aviation Industry Corporation I Program (ATPD-1104-02).
文摘Dynamic stress intensity factors are evaluated for thick-walled cylinder with a radial edge crack under internal impulsive pressure. Firstly, the equation for stress intensity factors under static uniform pressure is used as the reference case, and then the weight function for a thick-walled cylinder containing a radial edge crack can be worked out. Secondly, the dynamic stresses in uncracked thick-walled cylinders are solved under internal impulsive pressure by using mode shape function method. The solution consists of a quasi-static solution satisfying inhomogeneous boundary conditions and a dynamic solution satisfying homogeneous boundary condi- tions, and the history and distribution of dynamic stresses in thick-walled cylinders are derived in terms of Fourier-Bessel series. Finally, the dynamic stress intensity factor equations for thick-walled cylinder containing a radial edge crack sub- jected to internal impulsive pressure are given by dynamic weight function method. The finite element method is utilized to verify the results of numerical examples, showing the validity and feasibility of the proposed method.
文摘Static strength finite element analysis was conducted to decrease the weight of a skeleton vehicle＇s frame. Results indicated that the maximum stress occurs on the front beam ＇s variable section area. Dynamic sensitivity analysis elucidated the relationship between the maximum stress and the thickness of a particular beam,e. g.,top,middle,and bottom beam. Displacement was analyzed by the key part that influenced the maximum stress. Finally,the new plan using BS960 super-high-strength beam steel and the preferred beam thickness was compared with the original plan. New combinations of beam thickness were introduced on the basis of different purposes; the maximum responding light w eight ratio was 21%.
文摘Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships,controlling redundant information,and improving model robustness.In this study,we propose a Dynamic Conditional Feature Screening(DCFS)method tailored for high-dimensional economic forecasting tasks.Our goal is to accurately identify key variables,enhance predictive performance,and provide both theoretical foundations and practical tools for macroeconomic modeling.The DCFS method constructs a comprehensive test statistic by integrating conditional mutual information with conditional regression error differences.By introducing a dynamic weighting mechanism,DCFS adaptively balances the linear and nonlinear contributions of features during the screening process.In addition,a dynamic thresholding mechanism is designed to effectively control the false discovery rate(FDR),thereby improving the stability and reliability of the screening results.On the theoretical front,we rigorously prove that the proposed method satisfies the sure screening property and rank consistency,ensuring accurate identification of the truly important feature set in high-dimensional settings.Simulation results demonstrate that under purely linear,purely nonlinear,and mixed dependency structures,DCFS consistently outperforms classical screening methods such as SIS,CSIS,and IG-SIS in terms of true positive rate(TPR),false discovery rate(FDR),and rank correlation.These results highlight the superior accuracy,robustness,and stability of our method.Furthermore,an empirical analysis based on the U.S.FRED-MD macroeconomic dataset confirms the practical value of DCFS in real-world forecasting tasks.The experimental results show that DCFS achieves lower prediction errors(RMSE and MAE)and higher R2 values in forecasting GDP growth.The selected key variables-including the Industrial Production Index(IP),Federal Funds Rate,Consumer Price Index(CPI),and Money Supply(M2)-possess clear economic interpretability,offering reliable support for economic forecasting and policy formulation.
文摘Automatic gauge control(AGC in the article)is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill.Most traditional AGC control algorithms need stable external system conditions and hard to stabilize under complex interference that meets coverage requirements.This paper presents a new anti-interference strategy for AGC control of 20-Hi cold reversing mill.The proposed algorithm introduces a united dynamic weights algorithm of feed forward-mass flow to avoid the complex interference problem in AGC control,the relevant control strategy is provided to eliminate the adverse effects.At the same time,the D-value between feed forward-mass flow pre-computation and thickness measurement deviation is dynamic compared,the final gap position regulation is calculated by developing a set of united dynamic weights between feed forward control and mass flow control.Finally,the output of controllers is sent to actuator though a constant rate smoothing.The proposed strategy is compared with conventional AGC control on Experimental platform and project application,the results show that the proposed strategy is more stable than comparison method and majority of system uncertainty produced by mentioned interference is significantly eliminated.
基金supported by grants from the 12th Five-year Science and Technology Support Program of China(No.2011BAI08B10)the National Natural Science Foundation of China(No.81171308,No.81570462)
文摘Summary： The purpose of this study was to quantitatively analyze the relationship between three di- mensional arterial spin labeling （3D-ASL） and dynamic susceptibility contrast-enhanced perfusion weighted imaging （DSC-PWI） in ischemic stroke patients. Thirty patients with ischemic stroke were in- cluded in this study. All subjects underwent routine magnetic resonance imaging scanning, diffusion weighted imaging （DWI）, magnetic resonance angiography （MRA）, 3D-ASL and DSC-PWI on a 3.0T MR scanner. Regions of interest （ROIs） were drawn on the cerebral blood flow （CBF） maps （derived from ASL） and multi-parametric DSC perfusion maps, and then, the absolute and relative values of ASL-CBF, DSC-derived CBF, and DSC-derived mean transit time （MTT） were calculated. The rela- tionships between ASL and DSC parameters were analyzed using Pearson＇s correlation analysis. Re- ceiver operative characteristic （ROC） curves were performed to define the thresholds of relative value of ASL-CBF （rASL） that could best predict DSC-CBF reduction and MTT prolongation. Relative ASL better correlated with CBF and MTT in the anterior circulation with the Pearson correlation coefficients （R） values being 0.611 （P〈0.001） and-0.610 （P〈0.001） respectively. ROC curves demonstrated that when rASL 〈0.585, the sensitivity, specificity and accuracy for predicting ROIs with rCBF〈0.9 were 92.3%, 63.6% and 76.6% respectively. When rASL 〈0.952, the sensitivity, specificity and accuracy for predicting ROIs rMTT〉I.0 were 75.7%, 89.2% and 87.8% respectively. ASL-CBF map has better linear correlations with DSC-derived parameters （DSC-CBF and MTT） in anterior circulation in ischemic stroke patients. Additionally, when rASL is lower than 0.585, it could predict DSC-CBF decrease with moderate accuracy. IfrASL values range from 0.585 to 0.952, we just speculate the prolonged MTT.
