The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consump...The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.展开更多
In urban construction,the“Urban Optimal Map(UOM)”serves as a key tool for integrating social resources and natural elements,and it plays a significant role in promoting sustainable urban development.With the advent ...In urban construction,the“Urban Optimal Map(UOM)”serves as a key tool for integrating social resources and natural elements,and it plays a significant role in promoting sustainable urban development.With the advent of the digital era,various data collection devices deployed in cities have accumulated massive amounts of data,forming multi-source,high-dimensional urban databases.While visualizing these data helps uncover patterns of urban operations,the overlay of large volumes of data also complicates the visualization,making it difficult to interpret.From an animal behavior perspective,this study integrates natural geographical data of London,distribution data of different animal species,urban social information data,and comparative data on animal habits.Through GIS analysis,data visualization,and weight overlay to generate adaptability maps,a digital model is constructed and an animal behavior simulation program is developed.On this basis,multi-criteria analysis(MCA)is employed to comprehensively evaluate the simulation results and optimize decision-making,and planning solutions that balance ecological and social needs are derived.The findings demonstrate that by mining and integrating multi-source data,along with future scenario simulations,the complex relationships among urban environment,society,and sustainable development can be effectively explored.This provides scientific and objective data support for promoting harmonious coexistence between urban development and nature,and can assist decision-makers in formulating more rational urban planning strategies.As urban data continues to be updated,the“UOM”will evolve into a dynamic map system.By incorporating machine learning methods to mine temporal dimension information,it can further achieve predictions of future urban development trends,and offer scientific support for urban resource allocation and planning strategies.展开更多
In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the opti...In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.展开更多
Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual map...Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls.However,indoor and outdoor glass curtain walls may fail to perceive these transparent materials.In this study,a novel indoor glass recognition and map optimization method based on boundary guidance is proposed.First,the status of glass recognition techniques is analyzed comprehensively.Next,a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed.Finally,map optimization and path-planning tests are conducted and compared using different algorithms.The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls.Using the proposed method,the recognition accuracy of a public test set increases to 94.1%.After adding the planning map,incorrect coverage redundancies for two test scenes reduce by 59.84%and 55.7%.Herein,a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.展开更多
Inverse lithography technology(ILT)is intended to achieve optimal mask design to print a lithography target for a given lithography process.Full chip implementation of rigorous inverse lithography remains a challengin...Inverse lithography technology(ILT)is intended to achieve optimal mask design to print a lithography target for a given lithography process.Full chip implementation of rigorous inverse lithography remains a challenging task because of enormous computational resource requirements and long computational time.To achieve full chip ILT solution,attempts have been made by using machine learning techniques based on deep convolution neural network(DCNN).The reported input for such DCNN is the rasterized images of the lithography target;such pure geometrical input requires DCNN to possess considerable number of layers to learn the optical properties of the mask,the nonlinear imaging process,and the rigorous ILT algorithm as well.To alleviate the difficulties,we have proposed the physics based optimal feature vector design for machine learning ILT in our early report.Although physics based feature vector followed by feedforward neural network can provide the solution to machine learning ILT,the feature vector is long and it can consume considerable amount of memory resource in practical implementation.To improve the resource efficiency,we proposed a hybrid approach in this study by combining first few physics based feature maps with a specially designed DCNN structure to learn the rigorous ILT algorithm.Our results show that this approach can make machine learning ILT easy,fast and more accurate.展开更多
This paper is concerned with the topological structure of efficient sets for optimizationproblem of set-valued mapping. It is proved that these sets are closed or. connected under someconditions on cone-continuity, co...This paper is concerned with the topological structure of efficient sets for optimizationproblem of set-valued mapping. It is proved that these sets are closed or. connected under someconditions on cone-continuity, cone-convexity and cone-quasiconvexity.展开更多
We will give a survey on results concerning Girsanov transforma- tions, transportation cost inequalities, convexity of entropy, and optimal transport maps on some infinite dimensional spaces. Some open Problems will b...We will give a survey on results concerning Girsanov transforma- tions, transportation cost inequalities, convexity of entropy, and optimal transport maps on some infinite dimensional spaces. Some open Problems will be arisen.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52375264 and 62376212).
文摘The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.
文摘In urban construction,the“Urban Optimal Map(UOM)”serves as a key tool for integrating social resources and natural elements,and it plays a significant role in promoting sustainable urban development.With the advent of the digital era,various data collection devices deployed in cities have accumulated massive amounts of data,forming multi-source,high-dimensional urban databases.While visualizing these data helps uncover patterns of urban operations,the overlay of large volumes of data also complicates the visualization,making it difficult to interpret.From an animal behavior perspective,this study integrates natural geographical data of London,distribution data of different animal species,urban social information data,and comparative data on animal habits.Through GIS analysis,data visualization,and weight overlay to generate adaptability maps,a digital model is constructed and an animal behavior simulation program is developed.On this basis,multi-criteria analysis(MCA)is employed to comprehensively evaluate the simulation results and optimize decision-making,and planning solutions that balance ecological and social needs are derived.The findings demonstrate that by mining and integrating multi-source data,along with future scenario simulations,the complex relationships among urban environment,society,and sustainable development can be effectively explored.This provides scientific and objective data support for promoting harmonious coexistence between urban development and nature,and can assist decision-makers in formulating more rational urban planning strategies.As urban data continues to be updated,the“UOM”will evolve into a dynamic map system.By incorporating machine learning methods to mine temporal dimension information,it can further achieve predictions of future urban development trends,and offer scientific support for urban resource allocation and planning strategies.
文摘In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB4700400).
文摘Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls.However,indoor and outdoor glass curtain walls may fail to perceive these transparent materials.In this study,a novel indoor glass recognition and map optimization method based on boundary guidance is proposed.First,the status of glass recognition techniques is analyzed comprehensively.Next,a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed.Finally,map optimization and path-planning tests are conducted and compared using different algorithms.The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls.Using the proposed method,the recognition accuracy of a public test set increases to 94.1%.After adding the planning map,incorrect coverage redundancies for two test scenes reduce by 59.84%and 55.7%.Herein,a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.
文摘Inverse lithography technology(ILT)is intended to achieve optimal mask design to print a lithography target for a given lithography process.Full chip implementation of rigorous inverse lithography remains a challenging task because of enormous computational resource requirements and long computational time.To achieve full chip ILT solution,attempts have been made by using machine learning techniques based on deep convolution neural network(DCNN).The reported input for such DCNN is the rasterized images of the lithography target;such pure geometrical input requires DCNN to possess considerable number of layers to learn the optical properties of the mask,the nonlinear imaging process,and the rigorous ILT algorithm as well.To alleviate the difficulties,we have proposed the physics based optimal feature vector design for machine learning ILT in our early report.Although physics based feature vector followed by feedforward neural network can provide the solution to machine learning ILT,the feature vector is long and it can consume considerable amount of memory resource in practical implementation.To improve the resource efficiency,we proposed a hybrid approach in this study by combining first few physics based feature maps with a specially designed DCNN structure to learn the rigorous ILT algorithm.Our results show that this approach can make machine learning ILT easy,fast and more accurate.
文摘This paper is concerned with the topological structure of efficient sets for optimizationproblem of set-valued mapping. It is proved that these sets are closed or. connected under someconditions on cone-continuity, cone-convexity and cone-quasiconvexity.
文摘We will give a survey on results concerning Girsanov transforma- tions, transportation cost inequalities, convexity of entropy, and optimal transport maps on some infinite dimensional spaces. Some open Problems will be arisen.
