Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear an...Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.展开更多
This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the ra...This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the rack and pinion contact with wear being predicted by four differently derived adaptive-network-based fuzzy inference system(s)(ANFIS)models.Using both model-agnostic and model-specific parameters integrated in a weighted evaluation framework,the models are evaluated with respect to the effectiveness of explanations.This framework is based on the observation of the outputs of the individual layers of ANFIS,also focusing on aspects of two multivalued logics,namely fuzzy logic and support logic.The results show that the introduced weighted evaluation framework makes it possible to quantify the explainability of the individual models in terms of XAI and TAI.Finally,a preselection of a model for predicting the wear of the rack and pinion contact can be made.展开更多
Owing to process conditions such as uneven clearance of base metal assembly and welding deformation,it is difficult to obtain well-formed structural welds with robot constant specification parameters welding.Determini...Owing to process conditions such as uneven clearance of base metal assembly and welding deformation,it is difficult to obtain well-formed structural welds with robot constant specification parameters welding.Determining how to extract a structured,anti-interference,concise,and dynamic knowledge model from measurable data,and then adjust the welding parameters with corresponding control methods in real time is a central problem to be solved in welding formation control.Hence,this paper proposes a welding penetration control method based on a Neighborhood Rough Set-Adaptive Neuro-Fuzzy Inference System(NRS-ANFIS)to achieve effective penetration control for the GMAW welding process.In orthogonal experiments,the NRS algorithm,which is based on visual sensing to obtain the properties of the weld pool and gap changes,is used to reduce the established frontal weld pool feature information decision table,and the minimum feature set of the weld pool tail width WTand the tail area coefficient CTSis obtained.The minimum feature set of the effective frontal weld pool,real-time line laser distance change,and real-time current information are used as the input for the ANFIS control system.The experimental results for the two groups of time-varying gaps demonstrate that under the condition of no preheating of the base metal,the complete welding penetration rate of the adjusted welding process parameters output by the trained ANFIS model reaches 87%,and the backside melting width is uniform and consistent,which meets the welding specification requirements.展开更多
State this study looks at how well a three-phase bidirectional converter works for Vehicle-to-Grid(V2G)services by using both Adaptive Neuro-Fuzzy Inference System(ANFIS)and Proportional-Integral(PI)controllers.When c...State this study looks at how well a three-phase bidirectional converter works for Vehicle-to-Grid(V2G)services by using both Adaptive Neuro-Fuzzy Inference System(ANFIS)and Proportional-Integral(PI)controllers.When compared with ANFIS controllers,traditional controllers such as PI and PID show challenges.They may not sufficiently react to changing conditions or non-linearity’s and use fixed gain values requiring hand tuning.By means of learning,ANFIS controllers can thus dynamically change their parameters,so providing enhanced accuracy and flexibility in real-time control.The main objectives are to control the DC link voltage,lower total harmonic distortion(THD),and lower the errors.The Synchronous Reference Frame(SRF)transformation changes three-phase AC into a two-axis(d-q)system,making it easier to control active and reactive power separately.We developed a thorough Simulink model in MATLAB 2023a to model the bidirectional off-board fast charger at a power level of 60 kW.After validation,a 5-kW hardware prototype was built in the lab.The main platform is an AC-DC converter,followed by a DC-DC converter.A programmable DC power supply,Chroma 62050H-600S,connected to the DC-DC converter,mimics the dynamic characteristics of a battery.The control algorithm,deployed on a Spartan-6 LX9 FPGA,manages both voltage and current,maintaining a stable DC link voltage of 800 V.The results obtained indicate that the ANFIS controller outperforms a conventional PI controller when handling dynamic load variations.展开更多
基金supported by the National Natural Science Foundation of China(52350410465)the General Projects of Guangdong Natural Science Research Projects(2023A1515011520).
文摘Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.
基金This project(ProKInect N°02P20A090)was funded by the German Federal Ministry of Education and Research(BMBF)within the“The Future of Value Creation-Research on Production,Services and Work”program and managed by the Project Management Agency Karlsruhe(PTKA).The support is greatly acknowledged.
文摘This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the rack and pinion contact with wear being predicted by four differently derived adaptive-network-based fuzzy inference system(s)(ANFIS)models.Using both model-agnostic and model-specific parameters integrated in a weighted evaluation framework,the models are evaluated with respect to the effectiveness of explanations.This framework is based on the observation of the outputs of the individual layers of ANFIS,also focusing on aspects of two multivalued logics,namely fuzzy logic and support logic.The results show that the introduced weighted evaluation framework makes it possible to quantify the explainability of the individual models in terms of XAI and TAI.Finally,a preselection of a model for predicting the wear of the rack and pinion contact can be made.
基金Supported by National Natural Science Foundation of China(Grant Nos.52261044,51969001)the Guangxi Provincial Science and Technology Major Project(Grant No.Guike AA23062037)Research Foundation Ability Enhancement Project for Young and Middle Aged Teachers in Guangxi Universities of China(Grant No.2024KY0441)。
文摘Owing to process conditions such as uneven clearance of base metal assembly and welding deformation,it is difficult to obtain well-formed structural welds with robot constant specification parameters welding.Determining how to extract a structured,anti-interference,concise,and dynamic knowledge model from measurable data,and then adjust the welding parameters with corresponding control methods in real time is a central problem to be solved in welding formation control.Hence,this paper proposes a welding penetration control method based on a Neighborhood Rough Set-Adaptive Neuro-Fuzzy Inference System(NRS-ANFIS)to achieve effective penetration control for the GMAW welding process.In orthogonal experiments,the NRS algorithm,which is based on visual sensing to obtain the properties of the weld pool and gap changes,is used to reduce the established frontal weld pool feature information decision table,and the minimum feature set of the weld pool tail width WTand the tail area coefficient CTSis obtained.The minimum feature set of the effective frontal weld pool,real-time line laser distance change,and real-time current information are used as the input for the ANFIS control system.The experimental results for the two groups of time-varying gaps demonstrate that under the condition of no preheating of the base metal,the complete welding penetration rate of the adjusted welding process parameters output by the trained ANFIS model reaches 87%,and the backside melting width is uniform and consistent,which meets the welding specification requirements.
基金the financial support provided by the Royal Academy of Engineering,UK(Project Reference No:TSP-2526-7102),which enabled the successful execution of this research work.
文摘State this study looks at how well a three-phase bidirectional converter works for Vehicle-to-Grid(V2G)services by using both Adaptive Neuro-Fuzzy Inference System(ANFIS)and Proportional-Integral(PI)controllers.When compared with ANFIS controllers,traditional controllers such as PI and PID show challenges.They may not sufficiently react to changing conditions or non-linearity’s and use fixed gain values requiring hand tuning.By means of learning,ANFIS controllers can thus dynamically change their parameters,so providing enhanced accuracy and flexibility in real-time control.The main objectives are to control the DC link voltage,lower total harmonic distortion(THD),and lower the errors.The Synchronous Reference Frame(SRF)transformation changes three-phase AC into a two-axis(d-q)system,making it easier to control active and reactive power separately.We developed a thorough Simulink model in MATLAB 2023a to model the bidirectional off-board fast charger at a power level of 60 kW.After validation,a 5-kW hardware prototype was built in the lab.The main platform is an AC-DC converter,followed by a DC-DC converter.A programmable DC power supply,Chroma 62050H-600S,connected to the DC-DC converter,mimics the dynamic characteristics of a battery.The control algorithm,deployed on a Spartan-6 LX9 FPGA,manages both voltage and current,maintaining a stable DC link voltage of 800 V.The results obtained indicate that the ANFIS controller outperforms a conventional PI controller when handling dynamic load variations.
