This paper mainly studies the system composed of robot and its safety mechanism.By using functional analysis method,the partial differential equation of the original system is transformed into the abstract Cauchy prob...This paper mainly studies the system composed of robot and its safety mechanism.By using functional analysis method,the partial differential equation of the original system is transformed into the abstract Cauchy problem in Banach space.The transient and steady-state availability of the system can be obtained by algebraic theory and C0 semi-group theory,and the system is proved to be reliable and zero-state controllable by using transformation variables.Finally,Maple software is used to simulate the system transient reliability and steady-state availability.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms d...Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.展开更多
As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as...As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems.This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning,termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation(MAMGBR)model,specifically designed to optimize group-buying recommendations on e-commerce platforms.The core dataset of this study comes from the Chinese maternal and infant e-commerce platform“Beibei,”encompassing approximately 430,000 successful groupbuying actions and over 120,000 users.Themodel focuses on twomain tasks:recommending items for group organizers(Task Ⅰ)and recommending participants for a given group-buying event(Task Ⅱ).In model evaluation,MAMGBR achieves an MRR@10 of 0.7696 for Task I,marking a 20.23%improvement over baseline models.Furthermore,in Task II,where complex interaction patterns prevail,MAMGBR utilizes auxiliary loss functions to effectively model the multifaceted roles of users,items,and participants,leading to a 24.08%increase in MRR@100 under a 1:99 sample ratio.Experimental results show that compared to benchmark models,such as NGCF and EATNN,MAMGBR’s integration ofmulti-head attentionmechanisms,expert networks,and gating mechanisms enables more accurate modeling of user preferences and social associations within group-buying scenarios,significantly enhancing recommendation accuracy and platform group-buying success rates.展开更多
With the continuous development of artificial intelligence technology,large-scale language models have demonstrated significant potential across various fields.In education,an increasing number of methods leverage lar...With the continuous development of artificial intelligence technology,large-scale language models have demonstrated significant potential across various fields.In education,an increasing number of methods leverage large-scale language models to enhance educational quality,introducing new ideas and opportunities for reform.However,training a large language model with substantial professional knowledge to meet teaching needs incurs high labor costs.The fine-tuning approach based on human feedback alignment can significantly lower these model labor costs.Consequently,this article thoroughly investigates the application of this large prediction model method,which is rooted in human feed-back alignment,within the educational reform of algorithm analysis and design courses and examines its impact on teaching effectiveness and students’learning experiences.展开更多
“Algorithm Design and Analysis”is not only one of the important courses in the undergraduate teaching of computer science and technology but also a key part of computer professional skills.In recent years,with the r...“Algorithm Design and Analysis”is not only one of the important courses in the undergraduate teaching of computer science and technology but also a key part of computer professional skills.In recent years,with the rise and widespread application of big language models,many teaching reform plans have been produced to promote the quality and efficiency of teaching.This paper studies how to refer to software development professional skills standards,investigates the knowledge points of“Algorithm Design and Analysis”courses in other educational institutions,uses cutting-edge core technology big language models to drive the improvement of teaching evaluation methods,improves teaching efficiency,and carries out reforms and practices in teaching content for undergraduate students in computer science.展开更多
This paper reforms the shortcomings and difficulties in the teaching process of the“Algorithm Design and Analysis”course.The knowledge graph optimizes the teaching content,abandons the traditional teaching method,ca...This paper reforms the shortcomings and difficulties in the teaching process of the“Algorithm Design and Analysis”course.The knowledge graph optimizes the teaching content,abandons the traditional teaching method,captures the direction of talent demand,adjusts the class time allocation,reorganizes the assessment method,focuses on practical hands-on ability,and designs a multistage teaching quality evaluation system to promote the overall improvement of teaching quality.The practice of course reform has proven that the“Algorithm Design and Analysis”course has achieved good teaching results after a series of teaching reform measures.展开更多
Predictive control(PC)is an advanced control algorithm,which is widely used in industrial process control.Among them,model-based predictive control(MPC)is an important branch of predictive control.Its basic principle ...Predictive control(PC)is an advanced control algorithm,which is widely used in industrial process control.Among them,model-based predictive control(MPC)is an important branch of predictive control.Its basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective function.Based on the algorithm combined with three different sections using deep learning technology to identify vehicles and output the optimal speed limit,to achieve the effect of traffic flow optimization.展开更多
This paper proposes a multivariable fixed-time leaderfollower formation control method for a group of nonholonomic mobile robots, which has the ability to estimate multiple uncertainties. Firstly, based on the state s...This paper proposes a multivariable fixed-time leaderfollower formation control method for a group of nonholonomic mobile robots, which has the ability to estimate multiple uncertainties. Firstly, based on the state space model of the leader-follower formation, a multivariable fixed-time formation kinematics controller is designed. Secondly, to overcome uncertainties existing in the nonholonomic mobile robot system, such as load change,friction, external disturbance, a multivariable fixed-time torque controller based on the fixed-time disturbance observer at the dynamic level is designed. The designed torque controller is cascaded with the formation controller and finally realizes accurate estimation of the uncertain part of the system, the follower tracking of reference velocity and the desired formation of the leader and the follower in a fixed-time. The fixed-time upper bound is completely determined by the controller parameters, which is independent of the initial state of the system. The multivariable fixed-time control theory and the Lyapunov method are adopted to ensure the system stability.Finally, the effectiveness of the proposed algorithm is verified by the experimental simulation.展开更多
In order to realize safe and accurate homing of parafoil system,a multiphase homing trajectory planning scheme is proposed according to the maneuverability and basic flight characteristics of the vehicle.In this scena...In order to realize safe and accurate homing of parafoil system,a multiphase homing trajectory planning scheme is proposed according to the maneuverability and basic flight characteristics of the vehicle.In this scenario,on the basis of geometric relationship of each phase trajectory,the problem of trajectory planning is transformed to parameter optimizing,and then auxiliary population-based quantum differential evolution algorithm(AP-QDEA)is applied as a tool to optimize the objective function,and the design parameters of the whole homing trajectory are obtained.The proposed AP-QDEA combines the strengths of differential evolution algorithm(DEA)and quantum evolution algorithm(QEA),and the notion of auxiliary population is introduced into the proposed algorithm to improve the searching precision and speed.The simulation results show that the proposed AP-QDEA is proven its superior in both effectiveness and efficiency by solving a set of benchmark problems,and the multiphase homing scheme can fulfill the requirement of fixed-points and upwind landing in the process of homing which is simple in control and facile in practice as well.展开更多
One of the primary difficulties in using powered parafoil(PPF) systems is the lack of effective trajectory tracking controllers since the trajectory tracking control is the essential operation for PPF to accomplish au...One of the primary difficulties in using powered parafoil(PPF) systems is the lack of effective trajectory tracking controllers since the trajectory tracking control is the essential operation for PPF to accomplish autonomous tasks. The characteristic model(CM) based all-coefficient adaptive control(ACAC) designed for PPF systems in horizontal and vertical trajectory control is proposed. The method is easy to use and convenient to adjust and test. Just a few parameters are adapted during the control process. In application, vertical and horizontal CMs are designed and ACAC controllers are constructed to control vertical altitude and horizontal trajectory of PPF based on the proposed CMs, respectively. Result analysis of different simulations shows that the applied ACAC control method is effective for trajectory tracking of the PPF systems and the approach guarantees the transient performance of the PPF systems with better disturbance rejection ability.展开更多
This paper presents a type of vibration energy harvester combining a piezoelectric cantilever and a single degree of freedom (SDOF) elastic system. The main function of the additional SDOF elastic system is to magnify...This paper presents a type of vibration energy harvester combining a piezoelectric cantilever and a single degree of freedom (SDOF) elastic system. The main function of the additional SDOF elastic system is to magnify vibration displacement of the piezoelectric cantilever to improve the power output. A mathematical model of the energy harvester is developed based on Hamilton's principle and Rayleigh-Ritz method. Furthermore, the effects of the structural parameters of the SDOF elastic system on the electromechanical outputs of the energy harvester are analyzed numerically. The accuracy of the output performance in the numerical solution is identified from the finite element method (FEM). A good agreement is found between the numerical results and FEM results. The results show that the power output can be increased and the frequency bandwidth can be improved when the SDOF elastic system has a larger lumped mass and a smaller damping ratio. The numerical results also indicate that a matching load resistance under the short circuit resonance condition can obtain a higher current output, and so is more suitable for application to the piezoelectric energy harvester.展开更多
Tracking control is a very challenging problem in the networked control system(NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-bas...Tracking control is a very challenging problem in the networked control system(NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the networked tracking control system(NTCS). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of fuzzy cluster modelling(FCM)technology. Then, the deduced error and error change in the future are treated as inputs of a fuzzy sliding mode controller(FSMC) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.展开更多
This paper is aimed at investigating the problem of mixed time/event-triggered finite-time non-fragile filtering for nonlinear networked control systems with delay.First,a fuzzy nonlinear networked control system mode...This paper is aimed at investigating the problem of mixed time/event-triggered finite-time non-fragile filtering for nonlinear networked control systems with delay.First,a fuzzy nonlinear networked control system model is established by interval type-2(IT2)Takagi-Sugeno(T-S)fuzzy model,the designed non-fragile filter resolves the filter parameter uncertainties and uses different membership functions from the IT2 T-S fuzzy model.Second,a novel mixed time/event-triggered transmission mechanism is proposed,which decreases the waste of network resources.Next,Bernoulli random variables are used to describe the cases of random switching mixed time/event-triggered transmission mechanism.Then,the error filtering system is designed by considering a Lyapunov function and a sufficient condition of finite-time boundedness.In addition,the existence conditions for the finite-time non-fragile filter are given by the linear matrix inequalities(LMIs).Finally,two simulation results are presented to prove the effectiveness of the obtained method.展开更多
Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detec- tion, and a novel algorithm of fault detection and estimation is proposed. This algorithm first...Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detec- tion, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the difference- value signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking con- vergent speed than the proportional-type （P-type） algorithm, but also calculates more simply than the proportional-derivative-type （PD-type） algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.展开更多
In recent years,the situation of the Hyphantria cunea(Drury)(Lepidoptera:Erebidae),infestation in China has been serious and has a tendency to continue to spread.A comprehensive analysis was carried out to examine the...In recent years,the situation of the Hyphantria cunea(Drury)(Lepidoptera:Erebidae),infestation in China has been serious and has a tendency to continue to spread.A comprehensive analysis was carried out to examine the spa-tial distribution trends and influencing factors of H.cunea.This analysis involved integrating administrative division and boundary data,distribution data of H.cunea,and envi-ronmental variables for 2021.GeoDetector and gravity analysis techniques were employed for data processing and interpretation.The results show that H.cunea exhibited high aggregation patterns in 2021 and 2022 concentrated mainly in eastern China.During these years,the focal point of the infestation was in Shandong Province with a spread towards the northeast.Conditions such as high vegetation density in eastern China provided favorable situations for growth and development of H.cunea.In China,the spatial distribution of the moth is primarily influenced by two critical factors:precipitation during the driest month and elevation.These play a pivotal role in determining the spread of the species.Based on these results,suggestions are provided for a mul-tifaceted approach to prevention and control of H.cunea infestation.展开更多
Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimoda...Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.展开更多
To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functio...To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.展开更多
This paper investigates the formation control problem for nonlinear multi-agent systems with a virtual leader. A distributed formation control strategy based on linear extended state observer(LESO) is proposed under t...This paper investigates the formation control problem for nonlinear multi-agent systems with a virtual leader. A distributed formation control strategy based on linear extended state observer(LESO) is proposed under the hypothesis that velocity of the agent s neighbors could not be measured. Some sufficient conditions are established to ensure that the nonlinear multi-agent systems form a predefined formation with switching topology when the nonlinear function is known. Moreover, the tracking errors are bounded with external disturbance. Lastly,a numerical example with different scenarios is presented to demonstrate the validity of the obtained results.展开更多
基金supported by the National High Technology Research and Development Program(863 program)of China(2012AA101906-2)the National Natural Science Foundation of China(3140030594)
基金Supported by the National Natural Science Foundation of China(Grant No.61571150)the Natural Science Foundation of Heilongjiang Province(Grant Nos.LH2019F037,F2017029)。
文摘This paper mainly studies the system composed of robot and its safety mechanism.By using functional analysis method,the partial differential equation of the original system is transformed into the abstract Cauchy problem in Banach space.The transient and steady-state availability of the system can be obtained by algebraic theory and C0 semi-group theory,and the system is proved to be reliable and zero-state controllable by using transformation variables.Finally,Maple software is used to simulate the system transient reliability and steady-state availability.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported by the National Natural Science Foundation of China(32171777)the Natural Science Foundation of Heilongjiang for Distinguished Young Scientists(JQ2023F002)the Fundamental Research Funds for Central Universities(2572023CT16).
文摘Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.
基金supported by the Key Research and Development Program of Heilongjiang Province(No.2022ZX01A35).
文摘As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems.This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning,termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation(MAMGBR)model,specifically designed to optimize group-buying recommendations on e-commerce platforms.The core dataset of this study comes from the Chinese maternal and infant e-commerce platform“Beibei,”encompassing approximately 430,000 successful groupbuying actions and over 120,000 users.Themodel focuses on twomain tasks:recommending items for group organizers(Task Ⅰ)and recommending participants for a given group-buying event(Task Ⅱ).In model evaluation,MAMGBR achieves an MRR@10 of 0.7696 for Task I,marking a 20.23%improvement over baseline models.Furthermore,in Task II,where complex interaction patterns prevail,MAMGBR utilizes auxiliary loss functions to effectively model the multifaceted roles of users,items,and participants,leading to a 24.08%increase in MRR@100 under a 1:99 sample ratio.Experimental results show that compared to benchmark models,such as NGCF and EATNN,MAMGBR’s integration ofmulti-head attentionmechanisms,expert networks,and gating mechanisms enables more accurate modeling of user preferences and social associations within group-buying scenarios,significantly enhancing recommendation accuracy and platform group-buying success rates.
文摘With the continuous development of artificial intelligence technology,large-scale language models have demonstrated significant potential across various fields.In education,an increasing number of methods leverage large-scale language models to enhance educational quality,introducing new ideas and opportunities for reform.However,training a large language model with substantial professional knowledge to meet teaching needs incurs high labor costs.The fine-tuning approach based on human feedback alignment can significantly lower these model labor costs.Consequently,this article thoroughly investigates the application of this large prediction model method,which is rooted in human feed-back alignment,within the educational reform of algorithm analysis and design courses and examines its impact on teaching effectiveness and students’learning experiences.
基金supported by Harbin Engineering University’s 2021 Education Reform Project“How to Make Computer Theory Teaching Serve Employment”(Grant No.JG2021B0609).
文摘“Algorithm Design and Analysis”is not only one of the important courses in the undergraduate teaching of computer science and technology but also a key part of computer professional skills.In recent years,with the rise and widespread application of big language models,many teaching reform plans have been produced to promote the quality and efficiency of teaching.This paper studies how to refer to software development professional skills standards,investigates the knowledge points of“Algorithm Design and Analysis”courses in other educational institutions,uses cutting-edge core technology big language models to drive the improvement of teaching evaluation methods,improves teaching efficiency,and carries out reforms and practices in teaching content for undergraduate students in computer science.
基金supported by Harbin Engineering University’s 2021 Education Reform Project“How to Make Computer Theory Teaching Serve Employment”(Grant No.JG2021B0609).
文摘This paper reforms the shortcomings and difficulties in the teaching process of the“Algorithm Design and Analysis”course.The knowledge graph optimizes the teaching content,abandons the traditional teaching method,captures the direction of talent demand,adjusts the class time allocation,reorganizes the assessment method,focuses on practical hands-on ability,and designs a multistage teaching quality evaluation system to promote the overall improvement of teaching quality.The practice of course reform has proven that the“Algorithm Design and Analysis”course has achieved good teaching results after a series of teaching reform measures.
文摘Predictive control(PC)is an advanced control algorithm,which is widely used in industrial process control.Among them,model-based predictive control(MPC)is an important branch of predictive control.Its basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective function.Based on the algorithm combined with three different sections using deep learning technology to identify vehicles and output the optimal speed limit,to achieve the effect of traffic flow optimization.
基金supported by the National Natural Science Foundation of China(61872204)the Natural Science Foundation of Heilongjiang Province of China(F2015025)。
文摘This paper proposes a multivariable fixed-time leaderfollower formation control method for a group of nonholonomic mobile robots, which has the ability to estimate multiple uncertainties. Firstly, based on the state space model of the leader-follower formation, a multivariable fixed-time formation kinematics controller is designed. Secondly, to overcome uncertainties existing in the nonholonomic mobile robot system, such as load change,friction, external disturbance, a multivariable fixed-time torque controller based on the fixed-time disturbance observer at the dynamic level is designed. The designed torque controller is cascaded with the formation controller and finally realizes accurate estimation of the uncertain part of the system, the follower tracking of reference velocity and the desired formation of the leader and the follower in a fixed-time. The fixed-time upper bound is completely determined by the controller parameters, which is independent of the initial state of the system. The multivariable fixed-time control theory and the Lyapunov method are adopted to ensure the system stability.Finally, the effectiveness of the proposed algorithm is verified by the experimental simulation.
基金Project(61273138) supported by the National Natural Science Foundation of ChinaProjects(KJ2016A169,KJ2015A242) supported by the University Natural Science Research Key Project of Anhui Province,ChinaProject(ZRC2014444) supported by the Talents Program of Anhui Science and Technology University,China
文摘In order to realize safe and accurate homing of parafoil system,a multiphase homing trajectory planning scheme is proposed according to the maneuverability and basic flight characteristics of the vehicle.In this scenario,on the basis of geometric relationship of each phase trajectory,the problem of trajectory planning is transformed to parameter optimizing,and then auxiliary population-based quantum differential evolution algorithm(AP-QDEA)is applied as a tool to optimize the objective function,and the design parameters of the whole homing trajectory are obtained.The proposed AP-QDEA combines the strengths of differential evolution algorithm(DEA)and quantum evolution algorithm(QEA),and the notion of auxiliary population is introduced into the proposed algorithm to improve the searching precision and speed.The simulation results show that the proposed AP-QDEA is proven its superior in both effectiveness and efficiency by solving a set of benchmark problems,and the multiphase homing scheme can fulfill the requirement of fixed-points and upwind landing in the process of homing which is simple in control and facile in practice as well.
基金Project(61273138)supported by the National Natural Science Foundation of ChinaProject(14JCZDJC39300)supported by the Key Fund of Tianjin,China
文摘One of the primary difficulties in using powered parafoil(PPF) systems is the lack of effective trajectory tracking controllers since the trajectory tracking control is the essential operation for PPF to accomplish autonomous tasks. The characteristic model(CM) based all-coefficient adaptive control(ACAC) designed for PPF systems in horizontal and vertical trajectory control is proposed. The method is easy to use and convenient to adjust and test. Just a few parameters are adapted during the control process. In application, vertical and horizontal CMs are designed and ACAC controllers are constructed to control vertical altitude and horizontal trajectory of PPF based on the proposed CMs, respectively. Result analysis of different simulations shows that the applied ACAC control method is effective for trajectory tracking of the PPF systems and the approach guarantees the transient performance of the PPF systems with better disturbance rejection ability.
基金Project supported by the National Natural Science Foundation of China (No. 51077018)the Science and Technology Planning Project of Qiqihar (No. GYGG2010-02-1), China
文摘This paper presents a type of vibration energy harvester combining a piezoelectric cantilever and a single degree of freedom (SDOF) elastic system. The main function of the additional SDOF elastic system is to magnify vibration displacement of the piezoelectric cantilever to improve the power output. A mathematical model of the energy harvester is developed based on Hamilton's principle and Rayleigh-Ritz method. Furthermore, the effects of the structural parameters of the SDOF elastic system on the electromechanical outputs of the energy harvester are analyzed numerically. The accuracy of the output performance in the numerical solution is identified from the finite element method (FEM). A good agreement is found between the numerical results and FEM results. The results show that the power output can be increased and the frequency bandwidth can be improved when the SDOF elastic system has a larger lumped mass and a smaller damping ratio. The numerical results also indicate that a matching load resistance under the short circuit resonance condition can obtain a higher current output, and so is more suitable for application to the piezoelectric energy harvester.
基金supported by the National Natural Science Foundation of China(51205025,51775048,61602041)the Science and Technology Program of Beijing Municipal Education Commission(KM201611417009,KM201811417001)+6 种基金the Premium Funding Project(BPHR2017CZ08)for Academic Human Resources Development in Beijing Union University(BUU)the Beijing Natural Science FoundationBeijing Municipal Education Commission Joint Fund(KZ201811417048)the Project of 2018-2019 Basic Research Fund of BUUthe Beijing Advanced Innovation Center for Intelligent Robots and Systems Open Fund(2018I RS17)the 2016 Beijing High Level Personnel Cross Training Program “Practical Training Plan”the Project of Beijing Municipal Natural Science Foundation(4142018)the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(CIT&TCD20150314)
文摘Tracking control is a very challenging problem in the networked control system(NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the networked tracking control system(NTCS). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of fuzzy cluster modelling(FCM)technology. Then, the deduced error and error change in the future are treated as inputs of a fuzzy sliding mode controller(FSMC) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.
基金supported by in part by the Science and Technology projects of the State Grid Heilongjiang Electric Power Co.,Ltd.(No.52243718001b)the Fundamental Research Funds in Heilongjiang Provincial Universities(No.135309372).
文摘This paper is aimed at investigating the problem of mixed time/event-triggered finite-time non-fragile filtering for nonlinear networked control systems with delay.First,a fuzzy nonlinear networked control system model is established by interval type-2(IT2)Takagi-Sugeno(T-S)fuzzy model,the designed non-fragile filter resolves the filter parameter uncertainties and uses different membership functions from the IT2 T-S fuzzy model.Second,a novel mixed time/event-triggered transmission mechanism is proposed,which decreases the waste of network resources.Next,Bernoulli random variables are used to describe the cases of random switching mixed time/event-triggered transmission mechanism.Then,the error filtering system is designed by considering a Lyapunov function and a sufficient condition of finite-time boundedness.In addition,the existence conditions for the finite-time non-fragile filter are given by the linear matrix inequalities(LMIs).Finally,two simulation results are presented to prove the effectiveness of the obtained method.
基金supported by the National Natural Science Foundation of China (61100103)
文摘Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detec- tion, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the difference- value signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking con- vergent speed than the proportional-type （P-type） algorithm, but also calculates more simply than the proportional-derivative-type （PD-type） algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.
基金funded by the National Key Research and Development Program of China(2021YFD1400300)the Fundamental Research Funds for the Central Universities(2572022DP04).
文摘In recent years,the situation of the Hyphantria cunea(Drury)(Lepidoptera:Erebidae),infestation in China has been serious and has a tendency to continue to spread.A comprehensive analysis was carried out to examine the spa-tial distribution trends and influencing factors of H.cunea.This analysis involved integrating administrative division and boundary data,distribution data of H.cunea,and envi-ronmental variables for 2021.GeoDetector and gravity analysis techniques were employed for data processing and interpretation.The results show that H.cunea exhibited high aggregation patterns in 2021 and 2022 concentrated mainly in eastern China.During these years,the focal point of the infestation was in Shandong Province with a spread towards the northeast.Conditions such as high vegetation density in eastern China provided favorable situations for growth and development of H.cunea.In China,the spatial distribution of the moth is primarily influenced by two critical factors:precipitation during the driest month and elevation.These play a pivotal role in determining the spread of the species.Based on these results,suggestions are provided for a mul-tifaceted approach to prevention and control of H.cunea infestation.
基金funded by the National Natural Science Foundation of China (Grant No.61872126,No.62273290)supported by the Key project of Natural Science Foundation of Shandong Province (Grant No.ZR2020KF019).
文摘Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.
基金This work was supported by the National Natural Science Foundation of China(31700315 and 61533019)the Natural Science Foundation of Chongqing,China(cstc2018jcyjAX0587)+1 种基金the Chinese Academy of Science(CAS)-Thailand National Science and Technology Development Agency(NSTDA)Joint Research Program(GJHZ2076)The authors thank Wang Qian and Mory Diakite for their assistance in the experiment.
文摘To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.
文摘This paper investigates the formation control problem for nonlinear multi-agent systems with a virtual leader. A distributed formation control strategy based on linear extended state observer(LESO) is proposed under the hypothesis that velocity of the agent s neighbors could not be measured. Some sufficient conditions are established to ensure that the nonlinear multi-agent systems form a predefined formation with switching topology when the nonlinear function is known. Moreover, the tracking errors are bounded with external disturbance. Lastly,a numerical example with different scenarios is presented to demonstrate the validity of the obtained results.
