The usage of mobile-phone among children increased significantly. Children are in their growing phase and cells of their body are rapidly dividing, therefore propagation of electro-magnetic (EM) radiation occurs quick...The usage of mobile-phone among children increased significantly. Children are in their growing phase and cells of their body are rapidly dividing, therefore propagation of electro-magnetic (EM) radiation occurs quickly in children. The aim of the present study was to evaluate the extent of mobile-phone usage as well as its possible health effect. A total number of 455 (398 children and 57 adults, 396 urban and 59 rural) students of age group ranging from 10-29 years participated in this study. An “Information Gathering Chronological (IGC) model” was used for the collection and evaluation of information. The four major parameters, i.e. demographic and public uniqueness, mobile-phone consumption patterns, grievance of the “forgetfulness” symptom to the subjects and awareness about the safety measures were included to get the concise information from participants. We have observed that the prevalence of “forgetfulness” was 23.95% among mobile-phone users. The incidence of overall “forgetfulness” symptoms was 23.59%, 17.46%, 25.00% and 37.50% in low (LU), normal (NU), moderate (MU) and heavy (HU) mobile-phone users respectively. A trend for risk for “forgetfulness” was observed in HU as compared to LU in overall mobile-phone users. Three folds and nearly five folds increased risk for “forgetfulness” was found among HU as compared to LU in children (p ≤ 0.0210) and urban area mobile-phone users respectively. No significant difference for “forgetfulness” symptoms was found in other categories (i.e. adult and rural mobile-phone users). These results suggested that the incidences of “forgetfulness” among children from urban area mobile-phone users were significantly increased.展开更多
President Xi Jinping’s New Year message calls for creating a better future for the world where every effort counts.IN his New Year message,President Xi Jinping called 2024 an extraordinary year with unforgettable mom...President Xi Jinping’s New Year message calls for creating a better future for the world where every effort counts.IN his New Year message,President Xi Jinping called 2024 an extraordinary year with unforgettable moments.China saw rainbows despite“winds and rains.”展开更多
The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of d...The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.展开更多
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satel...To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is proposed.The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors.The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more adaptability.The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues.Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively.Stability analysis of the proposed method is also conducted.The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments.The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.展开更多
The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms wit...The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.展开更多
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible ...The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.展开更多
Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In...Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.展开更多
"Ware very honored to be here,discussing the prospect of global supply chain cooperation with distinguished changemakers from all around the world.""This is such a valuable opportunity to meet so many f..."Ware very honored to be here,discussing the prospect of global supply chain cooperation with distinguished changemakers from all around the world.""This is such a valuable opportunity to meet so many friends and share our plans for the coming Expo."What an inspiring and unforgettable evening!"展开更多
Harbin is where winter goes to show off.Known as the“Ice City”,Harbin,located in Heilongjiang Province in northeastern China,is a frosty paradise that feels like stepping into a magical wonderland.The cold here is n...Harbin is where winter goes to show off.Known as the“Ice City”,Harbin,located in Heilongjiang Province in northeastern China,is a frosty paradise that feels like stepping into a magical wonderland.The cold here is no joke,especially if you forget your gloves.However,once you brave the chill,you’re rewarded with the breathtaking Ice and Snow Festival,where ice sculptures look like something out of a frosty dream.Every year,it attracts countless adventurers eager to experience its iconic ice sculptures,European-inspired architecture,and unique local cuisine.展开更多
Quanzhou,as the only starting point of the Maritime Silk Road recognized by the United Nations,was praised as"the most prosperous city in the world"by Marco Polo.On July 25th,2021,China's"Quanzhou:E...Quanzhou,as the only starting point of the Maritime Silk Road recognized by the United Nations,was praised as"the most prosperous city in the world"by Marco Polo.On July 25th,2021,China's"Quanzhou:Emporium of the World in Song-Yuan China"was added to the UNESCO World Heritage List as a cultural site,bringing the total number of the country's UNESCO World Heritage sites to 56.The twentytwo world heritage sites in Quanzhou.展开更多
A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy...A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.展开更多
1For the past 33 years,Yue Baozheng has been a beloved tour guide in Xi'an,captivating visitors from around the globe with his unique"Shaanxi-style English"as he introduces them to the Terracotta Army.Th...1For the past 33 years,Yue Baozheng has been a beloved tour guide in Xi'an,captivating visitors from around the globe with his unique"Shaanxi-style English"as he introduces them to the Terracotta Army.This world-famous site,often called the eighth wonder of the world,draws countless tourists every year,and Yue is dedicated to making their experience unforgettable.展开更多
How time flies!My colorful middle school life now comes to an end.Here is what I think about my middle school life.In the past,I was busy with my subjects and worked hard at them.I got on well with my classmates.I was...How time flies!My colorful middle school life now comes to an end.Here is what I think about my middle school life.In the past,I was busy with my subjects and worked hard at them.I got on well with my classmates.I was good at English.I liked playing basketball.But now I am interested in history,because it is very interesting.展开更多
Two hydroelectric power stations built by China have considerably improved the quality of life for Guineans.For Sonkou Condé,28 September is an unforgettable date,because that is when the Kaléta dam,a projec...Two hydroelectric power stations built by China have considerably improved the quality of life for Guineans.For Sonkou Condé,28 September is an unforgettable date,because that is when the Kaléta dam,a project in which he was actively involved as an employee,was inaugurated.On this day,traditional Guinean music resounded over the construction site,accompanied by singing and dancing,which vividly displayed the joy and satisfaction of the local people.展开更多
love collecting,because it can enrich my knowledge,edify my temperament and also make me more careful.I love collecting.When I enjoyed it,I coudld pour all the unhappy things into the deep sea and I couldd even forget...love collecting,because it can enrich my knowledge,edify my temperament and also make me more careful.I love collecting.When I enjoyed it,I coudld pour all the unhappy things into the deep sea and I couldd even forget myself.When I enjoyed it,I was completely free and at that time I think that I was one of the happiest people in the world,as I owned the whole world in my collections.展开更多
A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracki...A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.展开更多
有一句英文"Tell me,and I will forget;show me,and I may remember;involve me,and I can learn"(直译为:告诉我,我会忘记;展示给我看,我也许能记得;让我参与其中,我才能学会)。无论它的来源是谁,这句话阐述了一个简单而深...有一句英文"Tell me,and I will forget;show me,and I may remember;involve me,and I can learn"(直译为:告诉我,我会忘记;展示给我看,我也许能记得;让我参与其中,我才能学会)。无论它的来源是谁,这句话阐述了一个简单而深刻的道理:老师进行完整而高效的知识讲授,和学生真正掌握了这些知识之间,展开更多
文摘The usage of mobile-phone among children increased significantly. Children are in their growing phase and cells of their body are rapidly dividing, therefore propagation of electro-magnetic (EM) radiation occurs quickly in children. The aim of the present study was to evaluate the extent of mobile-phone usage as well as its possible health effect. A total number of 455 (398 children and 57 adults, 396 urban and 59 rural) students of age group ranging from 10-29 years participated in this study. An “Information Gathering Chronological (IGC) model” was used for the collection and evaluation of information. The four major parameters, i.e. demographic and public uniqueness, mobile-phone consumption patterns, grievance of the “forgetfulness” symptom to the subjects and awareness about the safety measures were included to get the concise information from participants. We have observed that the prevalence of “forgetfulness” was 23.95% among mobile-phone users. The incidence of overall “forgetfulness” symptoms was 23.59%, 17.46%, 25.00% and 37.50% in low (LU), normal (NU), moderate (MU) and heavy (HU) mobile-phone users respectively. A trend for risk for “forgetfulness” was observed in HU as compared to LU in overall mobile-phone users. Three folds and nearly five folds increased risk for “forgetfulness” was found among HU as compared to LU in children (p ≤ 0.0210) and urban area mobile-phone users respectively. No significant difference for “forgetfulness” symptoms was found in other categories (i.e. adult and rural mobile-phone users). These results suggested that the incidences of “forgetfulness” among children from urban area mobile-phone users were significantly increased.
文摘President Xi Jinping’s New Year message calls for creating a better future for the world where every effort counts.IN his New Year message,President Xi Jinping called 2024 an extraordinary year with unforgettable moments.China saw rainbows despite“winds and rains.”
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through project number RI-44-0833.
文摘The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
基金Associate Professor Hongzhuan Qiu for his valuable comments and suggestions in formula derivation and proofreading of this paper.
文摘To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is proposed.The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors.The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more adaptability.The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues.Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively.Stability analysis of the proposed method is also conducted.The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments.The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.
基金supported by National Key Research and Development Program of China(2020YFB0505803)National Key Research and Development Program of China(2016YFB0501700)。
文摘The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
文摘The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.
文摘Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.
文摘"Ware very honored to be here,discussing the prospect of global supply chain cooperation with distinguished changemakers from all around the world.""This is such a valuable opportunity to meet so many friends and share our plans for the coming Expo."What an inspiring and unforgettable evening!"
文摘Harbin is where winter goes to show off.Known as the“Ice City”,Harbin,located in Heilongjiang Province in northeastern China,is a frosty paradise that feels like stepping into a magical wonderland.The cold here is no joke,especially if you forget your gloves.However,once you brave the chill,you’re rewarded with the breathtaking Ice and Snow Festival,where ice sculptures look like something out of a frosty dream.Every year,it attracts countless adventurers eager to experience its iconic ice sculptures,European-inspired architecture,and unique local cuisine.
文摘Quanzhou,as the only starting point of the Maritime Silk Road recognized by the United Nations,was praised as"the most prosperous city in the world"by Marco Polo.On July 25th,2021,China's"Quanzhou:Emporium of the World in Song-Yuan China"was added to the UNESCO World Heritage List as a cultural site,bringing the total number of the country's UNESCO World Heritage sites to 56.The twentytwo world heritage sites in Quanzhou.
文摘A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.
文摘1For the past 33 years,Yue Baozheng has been a beloved tour guide in Xi'an,captivating visitors from around the globe with his unique"Shaanxi-style English"as he introduces them to the Terracotta Army.This world-famous site,often called the eighth wonder of the world,draws countless tourists every year,and Yue is dedicated to making their experience unforgettable.
文摘How time flies!My colorful middle school life now comes to an end.Here is what I think about my middle school life.In the past,I was busy with my subjects and worked hard at them.I got on well with my classmates.I was good at English.I liked playing basketball.But now I am interested in history,because it is very interesting.
文摘Two hydroelectric power stations built by China have considerably improved the quality of life for Guineans.For Sonkou Condé,28 September is an unforgettable date,because that is when the Kaléta dam,a project in which he was actively involved as an employee,was inaugurated.On this day,traditional Guinean music resounded over the construction site,accompanied by singing and dancing,which vividly displayed the joy and satisfaction of the local people.
文摘love collecting,because it can enrich my knowledge,edify my temperament and also make me more careful.I love collecting.When I enjoyed it,I coudld pour all the unhappy things into the deep sea and I couldd even forget myself.When I enjoyed it,I was completely free and at that time I think that I was one of the happiest people in the world,as I owned the whole world in my collections.
文摘A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.
文摘有一句英文"Tell me,and I will forget;show me,and I may remember;involve me,and I can learn"(直译为:告诉我,我会忘记;展示给我看,我也许能记得;让我参与其中,我才能学会)。无论它的来源是谁,这句话阐述了一个简单而深刻的道理:老师进行完整而高效的知识讲授,和学生真正掌握了这些知识之间,
