Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the comm...Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the community of fluid mechanics,especially in the field of active flow control(AFC).This article surveys recent successful applications of machine learning in AFC,highlights general ideas,and aims at offering a basic outline for those who are interested in this specific topic.In this short review,we focus on two methodologies,i.e.,genetic programming(GP)and deep reinforcement learning(DRL),both having been proven effective,efficient,and robust in certain AFC problems,and outline some future prospects that might shed some light for relevant studies.展开更多
Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations betw...Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.展开更多
Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such ...Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.展开更多
In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential param...In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters.The data used for model development was recorded by an in-situ experiment.The image processing technology is used to quantify several tree canopy parameters.Based on four accuracy metrics,i.e.root mean square error(RMSE),mean absolute percentage error(MAPE),coefficient of determination(R2),and relative error,the performance of the proposed GP model was evaluated.The results indicate that the model can give a reasonable estimation for the spatiotemporal variations of soil suction around a tree with acceptable errors.Global sensitivity analysis for the statistical model obtained using limited data of a specific region demonstrates the drying time as the most influential variable and the initial soil suction as the second most influential variable for the soil suction variations.A case study was conducted using a set of assumed input variable values and validated that the simplified GP model can be used to estimate and predict the spatiotemporal variations of soil suction in rooted soil at a certain range.展开更多
In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellu...In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.展开更多
Researchers in the past had noticed that application of Artificial Neural Networks （ANN） in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predict...Researchers in the past had noticed that application of Artificial Neural Networks （ANN） in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming （GP） has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.展开更多
基金This work was support by the Research Grants Council of Hong Kong under General Research Fund(Grant Nos.15249316,15214418)the Departmental General Research Fund(Grant No.G-YBXQ).
文摘Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the community of fluid mechanics,especially in the field of active flow control(AFC).This article surveys recent successful applications of machine learning in AFC,highlights general ideas,and aims at offering a basic outline for those who are interested in this specific topic.In this short review,we focus on two methodologies,i.e.,genetic programming(GP)and deep reinforcement learning(DRL),both having been proven effective,efficient,and robust in certain AFC problems,and outline some future prospects that might shed some light for relevant studies.
文摘Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.
基金supported by JKW Program(No.M102-03)National Program(No.E0F80246).
文摘Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.
基金the National Key R&D Program of China(No.2019YFB1600700)the Science and Technology Development Fund of Macao(Nos.SKL-IOTSC-2018-2020 and 0193/2017/A3)the University of Macao Research Fund(No.MYRG2018-00173-FST),China。
文摘In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters.The data used for model development was recorded by an in-situ experiment.The image processing technology is used to quantify several tree canopy parameters.Based on four accuracy metrics,i.e.root mean square error(RMSE),mean absolute percentage error(MAPE),coefficient of determination(R2),and relative error,the performance of the proposed GP model was evaluated.The results indicate that the model can give a reasonable estimation for the spatiotemporal variations of soil suction around a tree with acceptable errors.Global sensitivity analysis for the statistical model obtained using limited data of a specific region demonstrates the drying time as the most influential variable and the initial soil suction as the second most influential variable for the soil suction variations.A case study was conducted using a set of assumed input variable values and validated that the simplified GP model can be used to estimate and predict the spatiotemporal variations of soil suction in rooted soil at a certain range.
基金supported by National Natural Science Foundation of China (No.61501028)Beijing Institute of Technology Research Fund Program for Young Scholars
文摘In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.
基金University Sains Malaysia for funding a short term grant (304.PREDAC.6035262) to conduct this on-going research
文摘Researchers in the past had noticed that application of Artificial Neural Networks （ANN） in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming （GP） has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.
