CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and ...CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.展开更多
The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the proble...The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.展开更多
Dynamic control of the absorption frequency and intensity of metamaterial absorbers has attracted considerable attention,and many kinds of tunable metamaterial absorbers have been proposed.Unfortunately,due to the int...Dynamic control of the absorption frequency and intensity of metamaterial absorbers has attracted considerable attention,and many kinds of tunable metamaterial absorbers have been proposed.Unfortunately,due to the integration of separate resonant unit and tunable unit,these designed metamaterial absorbers suffer from complex structure and low sensitivity.We numerically and experimentally demonstrate a tunable metamaterial absorber composed of artificial dielectric atoms as both resonant and tunable unit arrayed periodically in the background matrix on the metallic plate.Polarization insensitive and wide incident angle absorption band with simulated and experimental absorptivity of 99%and 96%at 9.65 GHz are achieved at room temperature.The absorption frequency can be gradually modulated by temperature,however,the absorption intensity at working frequency remains near unity.The dielectric atoms based tunable metamaterial absorbers with simple structure have potential applications as tempe rature sensors and frequency selective thermal emitters.展开更多
Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distribu...Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.展开更多
On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Mal...On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.展开更多
In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule sampl...In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.展开更多
Objective and Impact Statement:The multi-quantification of the distinct individualized maxillofacial traits,that is,quantifying multiple indices,is vital for diagnosis,decision-making,and prognosis of the maxillofacia...Objective and Impact Statement:The multi-quantification of the distinct individualized maxillofacial traits,that is,quantifying multiple indices,is vital for diagnosis,decision-making,and prognosis of the maxillofacial surgery.Introduction:While the discrete and demographically disproportionate distributions of the multiple indices restrict the generalization ability of artificial intelligence(AI)-based automatic analysis,this study presents a demographic-parity strategy for AI-based multi-quantification.Methods:In the aesthetic-concerning maxillary alveolar basal bone,which requires quantifying a total of 9 indices from length and width dimensional,this study collected a total of 4,000 cone-beam computed tomography(CBCT)sagittal images,and developed a deep learning model composed of a backbone and multiple regression heads with fully shared parameters to intelligently predict these quantitative metrics.Through auditing of the primary generalization result,the sensitive attribute was identified and the dataset was subdivided to train new submodels.Then,submodels trained from respective subsets were ensembled for final generalization.Results:The primary generalization result showed that the AI model underperformed in quantifying major basal bone indices.The sex factor was proved to be the sensitive attribute.The final model was ensembled by the male and female submodels,which yielded equal performance between genders,low error,high consistency,satisfying correlation coefficient,and highly focused attention.The ensemble model exhibited high similarity to clinicians with minor processing time.Conclusion:This work validates that the demographic parity strategy enables the AI algorithm with greater model generalization ability,even for the highly variable traits,which benefits for the appearance-concerning maxillofacial surgery.展开更多
As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since e...As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing DTC.In the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction process.To solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information.An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,respectively.We also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s effectiveness.The results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.展开更多
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncerta...The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.展开更多
Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt t...Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt to changes in assembly situations.To address these issues,a collaborative assembly is proposed.Based on the requirements of collaborative assembly,a colored Petri net(CPN)model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly.Also,an artificial potential field based planning algorithm(AFPA)is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs.Then an adaptive quantum genetic algorithm(AQGA)is developed to optimize the assembly process.Lastly,taking a two-pole circuit-breaker controller with leakage protection(TPCLP)as an assembly instance,comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly.The distribution of resources can also be optimized in the assembly.The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.展开更多
文摘CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.
基金supported by the National Natural Science Foundation of China(61201370)the Special Funding Project for Independent Innovation Achievement Transform of Shandong Province(2012CX30202)the Natural Science Foundation of Shandong Province(ZR2014FM039)
文摘The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.
基金financially supported by the Basic Science Center Project of NSFC(No.51788104)the National Natural Science Foundation of China(Nos.51532004,51425401 and 51690161)+3 种基金the Fundamental Research Funds for the Central Universities(Nos.N180903008 and N180912004)the Liaoning PhD start-up Foundation(No.20180540058)the Postdoctoral Science Foundation of China(No.2019M651130)State Key Laboratory of New Ceramic and Fine Processing Tsinghua University(No.KF201804)。
文摘Dynamic control of the absorption frequency and intensity of metamaterial absorbers has attracted considerable attention,and many kinds of tunable metamaterial absorbers have been proposed.Unfortunately,due to the integration of separate resonant unit and tunable unit,these designed metamaterial absorbers suffer from complex structure and low sensitivity.We numerically and experimentally demonstrate a tunable metamaterial absorber composed of artificial dielectric atoms as both resonant and tunable unit arrayed periodically in the background matrix on the metallic plate.Polarization insensitive and wide incident angle absorption band with simulated and experimental absorptivity of 99%and 96%at 9.65 GHz are achieved at room temperature.The absorption frequency can be gradually modulated by temperature,however,the absorption intensity at working frequency remains near unity.The dielectric atoms based tunable metamaterial absorbers with simple structure have potential applications as tempe rature sensors and frequency selective thermal emitters.
文摘Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.
基金Supported by Brilliant Youth Fund in Hebei Province
文摘On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
文摘In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.
基金supported by the Guangzhou Science and Technology Project(no.2023B03J1232)National Natural Science Foundation of China(82301036)+1 种基金Special Funds for the Cultivation of Guangdong College Students’Scientific and Technological Innovation(no.pdjh2023b0013)Undergraduate Training Program for Innovation of Sun Yat-sen University(20240518).
文摘Objective and Impact Statement:The multi-quantification of the distinct individualized maxillofacial traits,that is,quantifying multiple indices,is vital for diagnosis,decision-making,and prognosis of the maxillofacial surgery.Introduction:While the discrete and demographically disproportionate distributions of the multiple indices restrict the generalization ability of artificial intelligence(AI)-based automatic analysis,this study presents a demographic-parity strategy for AI-based multi-quantification.Methods:In the aesthetic-concerning maxillary alveolar basal bone,which requires quantifying a total of 9 indices from length and width dimensional,this study collected a total of 4,000 cone-beam computed tomography(CBCT)sagittal images,and developed a deep learning model composed of a backbone and multiple regression heads with fully shared parameters to intelligently predict these quantitative metrics.Through auditing of the primary generalization result,the sensitive attribute was identified and the dataset was subdivided to train new submodels.Then,submodels trained from respective subsets were ensembled for final generalization.Results:The primary generalization result showed that the AI model underperformed in quantifying major basal bone indices.The sex factor was proved to be the sensitive attribute.The final model was ensembled by the male and female submodels,which yielded equal performance between genders,low error,high consistency,satisfying correlation coefficient,and highly focused attention.The ensemble model exhibited high similarity to clinicians with minor processing time.Conclusion:This work validates that the demographic parity strategy enables the AI algorithm with greater model generalization ability,even for the highly variable traits,which benefits for the appearance-concerning maxillofacial surgery.
基金supported by the National Key R&D Program of China(No.2023YFB2904803)the National Natural Science Foundation of China(Nos.62341128,62201087,and 62101069)+2 种基金the National Science Fund for Distinguished Young Scholars,China(No.61925102)the Beijing Natural Science Foundation,China(No.L243002)the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing DTC.In the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction process.To solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information.An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,respectively.We also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s effectiveness.The results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.
文摘Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
基金supported by the Australian Government Department of Industry,Science,Energy,and Resources,and the Department of Climate Change,Energy,the Environment and Water under the International Clean Innovation Researcher Networks(ICIRN)program(grant number:ICIRN000077).
文摘The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.
基金supported by the National Natural Science Foundation of China(No.52175124)the Zhejiang Provincial Natural Science Foundation of China(No.LZ21E050003)the Fundamental Research Funds for Zhejiang Universities,China(No.RF-C2020004)。
文摘Low-voltage electrical apparatuses(LVEAs)have many workpieces and intricate geometric structures,and the assembly process is rigid and labor-intensive,and has little balance.The assembly process cannot readily adapt to changes in assembly situations.To address these issues,a collaborative assembly is proposed.Based on the requirements of collaborative assembly,a colored Petri net(CPN)model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly.Also,an artificial potential field based planning algorithm(AFPA)is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs.Then an adaptive quantum genetic algorithm(AQGA)is developed to optimize the assembly process.Lastly,taking a two-pole circuit-breaker controller with leakage protection(TPCLP)as an assembly instance,comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly.The distribution of resources can also be optimized in the assembly.The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.
