[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
Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent...Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.展开更多
Biomass-derived hard carbons,usually prepared by pyrolysis,are widely considered the most promising anode materials for sodium-ion bat-teries(SIBs)due to their high capacity,low poten-tial,sustainability,cost-effectiv...Biomass-derived hard carbons,usually prepared by pyrolysis,are widely considered the most promising anode materials for sodium-ion bat-teries(SIBs)due to their high capacity,low poten-tial,sustainability,cost-effectiveness,and environ-mental friendliness.The pyrolysis method affects the microstructure of the material,and ultimately its so-dium storage performance.Our previous work has shown that pyrolysis in a sealed graphite vessel im-proved the sodium storage performance of the car-bon,however the changes in its microstructure and the way this influences the sodium storage are still unclear.A series of hard carbon materials derived from corncobs(CCG-T,where T is the pyrolysis temperature)were pyrolyzed in a sealed graphite vessel at different temperatures.As the pyrolysis temperature increased from 1000 to 1400℃ small carbon domains gradually transformed into long and curved domains.At the same time,a greater number of large open pores with uniform apertures,as well as more closed pores,were formed.With the further increase of pyrolysis temperature to 1600℃,the long and curved domains became longer and straighter,and some closed pores gradually became open.CCG-1400,with abundant closed pores,had a superior SIB performance,with an initial reversible ca-pacity of 320.73 mAh g^(-1) at a current density of 30 mA g^(-1),an initial Coulomb efficiency(ICE)of 84.34%,and a capacity re-tention of 96.70%after 100 cycles.This study provides a method for the precise regulation of the microcrystalline and pore structures of hard carbon materials.展开更多
Changes to the microstructure of a hard carbon(HC)and its solid electrolyte interface(SEI)can be effective in improving the electrode kinetics.However,achieving fast charging using a simple and inexpensive strategy wi...Changes to the microstructure of a hard carbon(HC)and its solid electrolyte interface(SEI)can be effective in improving the electrode kinetics.However,achieving fast charging using a simple and inexpensive strategy without sacrificing its initial Coulombic efficiency remains a challenge in sodium ion batteries.A simple liquid-phase coating approach has been used to generate a pitch-derived soft carbon layer on the HC surface,and its effect on the porosity of HC and SEI chemistry has been studied.A variety of structural characterizations show a soft carbon coating can increase the defect and ultra-micropore contents.The increase in ultra-micropore comes from both the soft carbon coatings and the larger pores within the HC that are partially filled by pitch,which provides more Na+storage sites.In-situ FTIR/EIS and ex-situ XPS showed that the soft carbon coating induced the formation of thinner SEI that is richer in NaF from the electrolyte,which stabilized the interface and promoted the charge transfer process.As a result,the anode produced fastcharging(329.8 mAh g^(−1)at 30 mA g^(−1)and 198.6 mAh g^(−1)at 300 mA g^(−1))and had a better cycling performance(a high capacity retention of 81.4%after 100 cycles at 150 mA g^(−1)).This work reveals the critical role of coating layer in changing the pore structure,SEI chemistry and diffusion kinetics of hard carbon,which enables rational design of sodium-ion battery anode with enhanced fast charging capability.展开更多
The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structur...The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structure,environmental friendliness,and cost-effectiveness.Recent advances in controlling the pore structure of these carbons and its relationship between to is energy storage performance are discussed,emphasizing the critical role of a balanced distribution of micropores,mesopores and macropores in determining electrochemical behavior.Particular attention is given to how the intrinsic components of biomass precursors(lignin,cellulose,and hemicellulose)influence pore formation during carbonization.Carbonization and activation strategies to precisely control the pore structure are introduced.Finally,key challenges in the industrial production of these carbons are outlined,and future research directions are proposed.These include the establishment of a database of biomass intrinsic structures and machine learning-assisted pore structure engineering,aimed at providing guidance for the design of high-performance carbon materials for next-generation energy storage devices.展开更多
As a typical sedimentary soft rock,mudstone has the characteristics of being easily softened and disintegrated under the effect of wetting and drying(WD).The first cycle of WD plays an important role in the entire WD ...As a typical sedimentary soft rock,mudstone has the characteristics of being easily softened and disintegrated under the effect of wetting and drying(WD).The first cycle of WD plays an important role in the entire WD cycles.X-ray micro-computed tomography(micro-CT)was used as a non-destructive tool to quantitatively analyze microstructural changes of the mudstone due to the first cycle of WD.The test results show that WD leads to an increase of pore volume and pore connectivity in the mudstone.The porosity and fractal dimension of each slice of mudstone not only increase in value,but also in fluctuation amplitude.The pattern of variation in the frequency distribution of the equivalent radii of connected,isolated pores and pore throats in mudstone under WD effect satisfies the Gaussian distribution.Under the effect of WD,pores and pore throats with relatively small sizes increase the most.The sphericity of the pores in mudstones is positively correlated with the pore radius.The WD effect transforms the originally angular and flat pores into round and regular pores.This paper can provide a reference for the study of the deterioration and catastrophic mechanisms of mudstone under wetting and drying cycles.展开更多
Because of actual requirement,shield machine always excavates with an inclined angle in longitudinal direction.Since many previous studies mainly focus on the face stability of the horizontal shield tunnel,the effects...Because of actual requirement,shield machine always excavates with an inclined angle in longitudinal direction.Since many previous studies mainly focus on the face stability of the horizontal shield tunnel,the effects of tensile strength cut-off and pore water pressure on the face stability of the longitudinally inclined shield tunnel are not well investigated.A failure mechanism of a longitudinally inclined shield tunnel face is constructed based on the spatial discretization technique and the tensile strength cut-off criterion is introduced to modify the constructed failure mechanism.The pore water pressure is introduced as an external force into the equation of virtual work and the objective function of the chamber pressure of the shield machine is obtained.Moreover,the critical chamber pressure of the longitudinally inclined shield tunnel is computed by optimal calculation.Parametric analysis indicates that both tensile strength cut-off and pore water pressure have a significant impact on the chamber pressure and the range of the collapse block.Finally,the theoretical results are compared with the numerical results calculated by FLAC3D software which proves that the proposed approach is effective.展开更多
A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody s...A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.展开更多
A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-sp...A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration.展开更多
Deep multi-modal learning,a rapidly growing field with a wide range of practical applications,aims to effectively utilize and integrate information from multiple sources,known as modalities.Despite its impressive empi...Deep multi-modal learning,a rapidly growing field with a wide range of practical applications,aims to effectively utilize and integrate information from multiple sources,known as modalities.Despite its impressive empirical performance,the theoretical foundations of deep multi-modal learning have yet to be fully explored.In this paper,we will undertake a comprehensive survey of recent developments in multi-modal learning theories,focusing on the fundamental properties that govern this field.Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learning,to clarify their implications for practitioners,and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning.展开更多
Laser cleaning is a highly nonlinear physical process for solving poor single-modal(e.g., acoustic or vision)detection performance and low inter-information utilization. In this study, a multi-modal feature fusion net...Laser cleaning is a highly nonlinear physical process for solving poor single-modal(e.g., acoustic or vision)detection performance and low inter-information utilization. In this study, a multi-modal feature fusion network model was constructed based on a laser paint removal experiment. The alignment of heterogeneous data under different modals was solved by combining the piecewise aggregate approximation and gramian angular field. Moreover, the attention mechanism was introduced to optimize the dual-path network and dense connection network, enabling the sampling characteristics to be extracted and integrated. Consequently, the multi-modal discriminant detection of laser paint removal was realized. According to the experimental results, the verification accuracy of the constructed model on the experimental dataset was 99.17%, which is 5.77% higher than the optimal single-modal detection results of the laser paint removal. The feature extraction network was optimized by the attention mechanism, and the model accuracy was increased by 3.3%. Results verify the improved classification performance of the constructed multi-modal feature fusion model in detecting laser paint removal, the effective integration of acoustic data and visual image data, and the accurate detection of laser paint removal.展开更多
Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,...Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.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Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
文摘Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.
文摘Biomass-derived hard carbons,usually prepared by pyrolysis,are widely considered the most promising anode materials for sodium-ion bat-teries(SIBs)due to their high capacity,low poten-tial,sustainability,cost-effectiveness,and environ-mental friendliness.The pyrolysis method affects the microstructure of the material,and ultimately its so-dium storage performance.Our previous work has shown that pyrolysis in a sealed graphite vessel im-proved the sodium storage performance of the car-bon,however the changes in its microstructure and the way this influences the sodium storage are still unclear.A series of hard carbon materials derived from corncobs(CCG-T,where T is the pyrolysis temperature)were pyrolyzed in a sealed graphite vessel at different temperatures.As the pyrolysis temperature increased from 1000 to 1400℃ small carbon domains gradually transformed into long and curved domains.At the same time,a greater number of large open pores with uniform apertures,as well as more closed pores,were formed.With the further increase of pyrolysis temperature to 1600℃,the long and curved domains became longer and straighter,and some closed pores gradually became open.CCG-1400,with abundant closed pores,had a superior SIB performance,with an initial reversible ca-pacity of 320.73 mAh g^(-1) at a current density of 30 mA g^(-1),an initial Coulomb efficiency(ICE)of 84.34%,and a capacity re-tention of 96.70%after 100 cycles.This study provides a method for the precise regulation of the microcrystalline and pore structures of hard carbon materials.
基金National Key Research and Development Program of China(2022YFE0206300)National Natural Science Foundation of China(U21A2081,22075074,22209047)+2 种基金Guangdong Basic and Applied Basic Research Foundation(2024A1515011620)Hunan Provincial Natural Science Foundation of China(2024JJ5068)Foundation of Yuelushan Center for Industrial Innovation(2023YCII0119)。
文摘Changes to the microstructure of a hard carbon(HC)and its solid electrolyte interface(SEI)can be effective in improving the electrode kinetics.However,achieving fast charging using a simple and inexpensive strategy without sacrificing its initial Coulombic efficiency remains a challenge in sodium ion batteries.A simple liquid-phase coating approach has been used to generate a pitch-derived soft carbon layer on the HC surface,and its effect on the porosity of HC and SEI chemistry has been studied.A variety of structural characterizations show a soft carbon coating can increase the defect and ultra-micropore contents.The increase in ultra-micropore comes from both the soft carbon coatings and the larger pores within the HC that are partially filled by pitch,which provides more Na+storage sites.In-situ FTIR/EIS and ex-situ XPS showed that the soft carbon coating induced the formation of thinner SEI that is richer in NaF from the electrolyte,which stabilized the interface and promoted the charge transfer process.As a result,the anode produced fastcharging(329.8 mAh g^(−1)at 30 mA g^(−1)and 198.6 mAh g^(−1)at 300 mA g^(−1))and had a better cycling performance(a high capacity retention of 81.4%after 100 cycles at 150 mA g^(−1)).This work reveals the critical role of coating layer in changing the pore structure,SEI chemistry and diffusion kinetics of hard carbon,which enables rational design of sodium-ion battery anode with enhanced fast charging capability.
文摘The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structure,environmental friendliness,and cost-effectiveness.Recent advances in controlling the pore structure of these carbons and its relationship between to is energy storage performance are discussed,emphasizing the critical role of a balanced distribution of micropores,mesopores and macropores in determining electrochemical behavior.Particular attention is given to how the intrinsic components of biomass precursors(lignin,cellulose,and hemicellulose)influence pore formation during carbonization.Carbonization and activation strategies to precisely control the pore structure are introduced.Finally,key challenges in the industrial production of these carbons are outlined,and future research directions are proposed.These include the establishment of a database of biomass intrinsic structures and machine learning-assisted pore structure engineering,aimed at providing guidance for the design of high-performance carbon materials for next-generation energy storage devices.
基金Project(41877240)supported by the National Natural Science Foundation of China。
文摘As a typical sedimentary soft rock,mudstone has the characteristics of being easily softened and disintegrated under the effect of wetting and drying(WD).The first cycle of WD plays an important role in the entire WD cycles.X-ray micro-computed tomography(micro-CT)was used as a non-destructive tool to quantitatively analyze microstructural changes of the mudstone due to the first cycle of WD.The test results show that WD leads to an increase of pore volume and pore connectivity in the mudstone.The porosity and fractal dimension of each slice of mudstone not only increase in value,but also in fluctuation amplitude.The pattern of variation in the frequency distribution of the equivalent radii of connected,isolated pores and pore throats in mudstone under WD effect satisfies the Gaussian distribution.Under the effect of WD,pores and pore throats with relatively small sizes increase the most.The sphericity of the pores in mudstones is positively correlated with the pore radius.The WD effect transforms the originally angular and flat pores into round and regular pores.This paper can provide a reference for the study of the deterioration and catastrophic mechanisms of mudstone under wetting and drying cycles.
基金Projects(52278395,52208409) supported by the National Natural Science Foundation of ChinaProject(2022JJ40531) supported by the Natural Science Foundation of Hunan Province,China。
文摘Because of actual requirement,shield machine always excavates with an inclined angle in longitudinal direction.Since many previous studies mainly focus on the face stability of the horizontal shield tunnel,the effects of tensile strength cut-off and pore water pressure on the face stability of the longitudinally inclined shield tunnel are not well investigated.A failure mechanism of a longitudinally inclined shield tunnel face is constructed based on the spatial discretization technique and the tensile strength cut-off criterion is introduced to modify the constructed failure mechanism.The pore water pressure is introduced as an external force into the equation of virtual work and the objective function of the chamber pressure of the shield machine is obtained.Moreover,the critical chamber pressure of the longitudinally inclined shield tunnel is computed by optimal calculation.Parametric analysis indicates that both tensile strength cut-off and pore water pressure have a significant impact on the chamber pressure and the range of the collapse block.Finally,the theoretical results are compared with the numerical results calculated by FLAC3D software which proves that the proposed approach is effective.
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProjects(20040533035, 20070533131) supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.
基金Project(61240010)supported by the National Natural Science Foundation of ChinaProject(20070007070)supported by Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration.
基金Supported by Technology and Innovation Major Project of the Ministry of Science and Technology of China(2020AAA0108400, 2020AAA0108403)Tsinghua Precision Medicine Foundation(10001020109)。
文摘Deep multi-modal learning,a rapidly growing field with a wide range of practical applications,aims to effectively utilize and integrate information from multiple sources,known as modalities.Despite its impressive empirical performance,the theoretical foundations of deep multi-modal learning have yet to be fully explored.In this paper,we will undertake a comprehensive survey of recent developments in multi-modal learning theories,focusing on the fundamental properties that govern this field.Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learning,to clarify their implications for practitioners,and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning.
基金Project(51875491) supported by the National Natural Science Foundation of ChinaProject(2021T3069) supported by the Fujian Science and Technology Plan STS Project,China。
文摘Laser cleaning is a highly nonlinear physical process for solving poor single-modal(e.g., acoustic or vision)detection performance and low inter-information utilization. In this study, a multi-modal feature fusion network model was constructed based on a laser paint removal experiment. The alignment of heterogeneous data under different modals was solved by combining the piecewise aggregate approximation and gramian angular field. Moreover, the attention mechanism was introduced to optimize the dual-path network and dense connection network, enabling the sampling characteristics to be extracted and integrated. Consequently, the multi-modal discriminant detection of laser paint removal was realized. According to the experimental results, the verification accuracy of the constructed model on the experimental dataset was 99.17%, which is 5.77% higher than the optimal single-modal detection results of the laser paint removal. The feature extraction network was optimized by the attention mechanism, and the model accuracy was increased by 3.3%. Results verify the improved classification performance of the constructed multi-modal feature fusion model in detecting laser paint removal, the effective integration of acoustic data and visual image data, and the accurate detection of laser paint removal.
基金Project(202006430012)supported by the China Scholarship Council。
文摘Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.
