[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Congestion pricing is an important component of urban intelligent transport system.The efficiency,equity and the environmental impacts associated with road pricing schemes are key issues that should be considered befo...Congestion pricing is an important component of urban intelligent transport system.The efficiency,equity and the environmental impacts associated with road pricing schemes are key issues that should be considered before such schemes are implemented.This paper focuses on the cordon-based pricing with distance tolls,where the tolls are determined by a nonlinear function of a vehicles' travel distance within a cordon,termed as toll charge function.The optimal tolls can give rise to:1) higher total social benefits,2) better levels of equity,and 3) reduced environmental impacts(e.g.,less emission).Firstly,a deterministic equilibrium(DUE) model with elastic demand is presented to evaluate any given toll charge function.The distance tolls are non-additive,thus a modified path-based gradient projection algorithm is developed to solve the DUE model.Then,to quantitatively measure the equity level of each toll charge function,the Gini coefficient is adopted to measure the equity level of the flows in the entire transport network based on equilibrium flows.The total emission level is used to reflect the impacts of distance tolls on the environment.With these two indexes/measurements for the efficiency,equity and environmental issues as well as the DUE model,a multi-objective bi-level programming model is then developed to determine optimal distance tolls.The multi-objective model is converted to a single level model using the goal programming.A genetic algorithm(GA) is adopted to determine solutions.Finally,a numerical example is presented to verify the methodology.展开更多
There were differences between real boundary and blast hole controlling boundary of irregular mined-out area in underground metal mines. There were errors in numerical analysis of stability for goaf, if it was analyze...There were differences between real boundary and blast hole controlling boundary of irregular mined-out area in underground metal mines. There were errors in numerical analysis of stability for goaf, if it was analyzed as regular 3D mined-out area and the influence of coupling stress-seepage-disturbance was not considered adequately. Taking a lead zinc mine as the background, the model was built by the coupling of Surpac and Midas-Gts based on the goaf model precisely measured by CMS.According to seepage stress fundamental equations based on the equivalent continuum mechanical and the theory about equivalent load of dynamic disturbance in deep-hole blasting, the stability of mined-out area under multi-field coupling of stress-seepage-dynamic disturbance was numerically analyzed. The results show that it is more consistent between the numerical analysis model based on the real model of irregular 3D shape goaf and the real situation, which could faithfully reappear the change rule of stress–strain about the surrounding rock under synthetic action of blasting dynamic loading and the seepage pressure. The mined-out area multi-field coupling formed by blasting excavation is stable. Based on combination of the advantages of the CMS,Surpac and Midas-Gts, and fully consideration of the effects of multi-field coupling, the accurate and effective way could be provided for numerical analysis of stability for mined-out area.展开更多
The temporal variation of ventilation coefficient was estimated and a simple model for the prediction of urban ventilation coefficient in Changsha was developed. Firstly, Pearson correlation analysis was used to inves...The temporal variation of ventilation coefficient was estimated and a simple model for the prediction of urban ventilation coefficient in Changsha was developed. Firstly, Pearson correlation analysis was used to investigate the relationship between meteorological parameters and mixing layer height during 2005-2009 in Changsha, China. Secondly, the multi-linear regression model between daytime and nighttime was adopted to predict the temporal ventilation coefficient. Thirdly, the validation of the model between the predicted and observed ventilation coefficient in 2010 was conducted. The results showed that ventilation coefficient significantly varied and remained high during daytime, while it stayed relatively constant and low during nighttime. In addition, the diurnal ventilation coefficient was distinctly negatively correlated with PM10 (particle with the diameter less than 10 μm) concentration in Changsha, China. The predicted ventilation coefficient agreed well with the observed values based on the multi-linear regression models during daytime and nighttime. The urban temporal ventilation coefficient could be accurately predicted by some simple meteorological parameters during daytime and nighttime. The ventilation coefficient played an important role in the PM10 concentration level.展开更多
To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was establis...To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.展开更多
Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approa...Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.展开更多
In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler...In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler were integrated synthetically. A set of practical multi-scale monitoring system on settlement of super-large pile-group foundation in deep water was put forward. The reliable settlement results are obtained by means of multi-sensor data fusion. Finite element model of pile-group foundation is established. By analysis of finite element simulated calculation of pile-group foundation, rules of settlement and uneven settlement obtained by monitoring and calculation results are coincident and the absolute error of settlement between them is 4.7 mm. The research shows that it is reasonable and feasible to monitor settlement of pile-group foundation with the system, and it can provide a method for the same type pile-group foundation in deep water.展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金Projects (61304198,61374195) supported by the National Natural Science Foundation of ChinaProjects (2013M530159,2014T70351) supported by the China Postdoctoral Science Foundation
文摘Congestion pricing is an important component of urban intelligent transport system.The efficiency,equity and the environmental impacts associated with road pricing schemes are key issues that should be considered before such schemes are implemented.This paper focuses on the cordon-based pricing with distance tolls,where the tolls are determined by a nonlinear function of a vehicles' travel distance within a cordon,termed as toll charge function.The optimal tolls can give rise to:1) higher total social benefits,2) better levels of equity,and 3) reduced environmental impacts(e.g.,less emission).Firstly,a deterministic equilibrium(DUE) model with elastic demand is presented to evaluate any given toll charge function.The distance tolls are non-additive,thus a modified path-based gradient projection algorithm is developed to solve the DUE model.Then,to quantitatively measure the equity level of each toll charge function,the Gini coefficient is adopted to measure the equity level of the flows in the entire transport network based on equilibrium flows.The total emission level is used to reflect the impacts of distance tolls on the environment.With these two indexes/measurements for the efficiency,equity and environmental issues as well as the DUE model,a multi-objective bi-level programming model is then developed to determine optimal distance tolls.The multi-objective model is converted to a single level model using the goal programming.A genetic algorithm(GA) is adopted to determine solutions.Finally,a numerical example is presented to verify the methodology.
基金Project(2012BAK09B02-05)supported by the National"Twelfth Five"Science and Technology Support Program,ChinaProject(51274250)supported by the National Natural Science Foundation of China+2 种基金Project(2013zzts057)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(11KF02)supported by the Research Fund of the State Key Laboratory of Coal Resources and Mine safety,CUMT,ChinaProject(2012M511417)supported by China Postdoctoral Science Foundation
文摘There were differences between real boundary and blast hole controlling boundary of irregular mined-out area in underground metal mines. There were errors in numerical analysis of stability for goaf, if it was analyzed as regular 3D mined-out area and the influence of coupling stress-seepage-disturbance was not considered adequately. Taking a lead zinc mine as the background, the model was built by the coupling of Surpac and Midas-Gts based on the goaf model precisely measured by CMS.According to seepage stress fundamental equations based on the equivalent continuum mechanical and the theory about equivalent load of dynamic disturbance in deep-hole blasting, the stability of mined-out area under multi-field coupling of stress-seepage-dynamic disturbance was numerically analyzed. The results show that it is more consistent between the numerical analysis model based on the real model of irregular 3D shape goaf and the real situation, which could faithfully reappear the change rule of stress–strain about the surrounding rock under synthetic action of blasting dynamic loading and the seepage pressure. The mined-out area multi-field coupling formed by blasting excavation is stable. Based on combination of the advantages of the CMS,Surpac and Midas-Gts, and fully consideration of the effects of multi-field coupling, the accurate and effective way could be provided for numerical analysis of stability for mined-out area.
基金Project(51178466) supported by the National Natural Science Foundation of ChinaProject(FANEDD200545) supported by Foundation for the Author of National Excellent Doctoral Dissertation of ChinaProject(2011JQ006) supported by Fundamental Research Funds of the Central Universities of China
文摘The temporal variation of ventilation coefficient was estimated and a simple model for the prediction of urban ventilation coefficient in Changsha was developed. Firstly, Pearson correlation analysis was used to investigate the relationship between meteorological parameters and mixing layer height during 2005-2009 in Changsha, China. Secondly, the multi-linear regression model between daytime and nighttime was adopted to predict the temporal ventilation coefficient. Thirdly, the validation of the model between the predicted and observed ventilation coefficient in 2010 was conducted. The results showed that ventilation coefficient significantly varied and remained high during daytime, while it stayed relatively constant and low during nighttime. In addition, the diurnal ventilation coefficient was distinctly negatively correlated with PM10 (particle with the diameter less than 10 μm) concentration in Changsha, China. The predicted ventilation coefficient agreed well with the observed values based on the multi-linear regression models during daytime and nighttime. The urban temporal ventilation coefficient could be accurately predicted by some simple meteorological parameters during daytime and nighttime. The ventilation coefficient played an important role in the PM10 concentration level.
基金Project(2012B091100444)supported by the Production,Education and Research Cooperative Program of Guangdong Province and Ministry of Education,ChinaProject(2013ZM0091)supported by Fundamental Research Funds for the Central Universities of China
文摘To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.
基金Project(61074074) supported by the National Natural Science Foundation,ChinaProject(KT2012C01J0401) supported by the Group Innovative Fund,China
文摘Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.
基金Project(2002CB412707) supported by the National Basic Research Program of ChinaProject(2006BAG04B05) supported by the National Science and Technology Pillar Program during the 11th Five-Year Plan of ChinaProject(2010B14414) supported by the Scientific Research Program of Center University in China
文摘In order to realize information construction on settlement of pile-group foundation of Sutong Bridge, the monitoring instruments of high-precision micro-pressure sensor and hydrostatic leveling and settlement profiler were integrated synthetically. A set of practical multi-scale monitoring system on settlement of super-large pile-group foundation in deep water was put forward. The reliable settlement results are obtained by means of multi-sensor data fusion. Finite element model of pile-group foundation is established. By analysis of finite element simulated calculation of pile-group foundation, rules of settlement and uneven settlement obtained by monitoring and calculation results are coincident and the absolute error of settlement between them is 4.7 mm. The research shows that it is reasonable and feasible to monitor settlement of pile-group foundation with the system, and it can provide a method for the same type pile-group foundation in deep water.
