The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it i...The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.展开更多
in this paper, a fast hidden-line removal method for 3D buildings , which is based on thesubtraction of a convex polygon from another one in linear time, is presented. Also, the facetscontaining holes are quickly divi...in this paper, a fast hidden-line removal method for 3D buildings , which is based on thesubtraction of a convex polygon from another one in linear time, is presented. Also, the facetscontaining holes are quickly divided into a lot of convex polygons, say, triangles and convexquadrilaterals. The algorithm for the polygon division runs in O( (k+1) (n-3)) , where n is thetotal number of the vertices of the merged loop of the facet, and k is the number of the concavevertices of the merged one.展开更多
Selective harmonic elimination(SHE) in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective fun...Selective harmonic elimination(SHE) in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective function with proper weighting is proposed and also its efficiency is compared with the objective function which is more similar to the proposed one. To enhance the ability of the SHE in eliminating high number of selected harmonics, at each level of the output voltage, one slot is created. The SHE problem is solved by imperialist competitive algorithm(ICA). The conventional SHE methods cannot eliminate the selected harmonics and satisfy the fundamental component in some ranges of modulation indexes. So, to surmount the SHE defect, a DC-DC converter is applied. Theoretical results are substantiated by simulations and experimental results for a 9-level multilevel inverter. The obtained results illustrate that the proposed method successfully minimizes a large number of identified harmonics which consequences very low total harmonic distortion of output voltage.展开更多
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result...In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.展开更多
With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study...With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.展开更多
Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, ...Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, we propose a safety-critical control scheme for robotic systems using an adaptive error elimination algorithm and optimization-based nonlinear optimal predictive control(NOPC) framework. The novelty of the proposed work lies in that an adaptive error elimination controller is designed to deal with the problem of stabilization of walking gait, which ensures that robot joint trajectory can compensate for the limitation of the template model. In order to be independent of system parameters and disturbances, a sliding mode controller is further designed under an uncertain environment. This approach takes into account simultaneously with foot position and orientation based on NOPC optimization. It tracks the modified trajectories constrained with the centroidal momentum dynamics. Finally, simulations is utilized to verify the efectiveness of the mentioned methods. The results indicate that the tracking efect of joint trajectory is better safety-critical nonlinear optimal predictive control with adaptive error elimination algorithm.展开更多
In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors vi...In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.展开更多
文摘The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.
文摘in this paper, a fast hidden-line removal method for 3D buildings , which is based on thesubtraction of a convex polygon from another one in linear time, is presented. Also, the facetscontaining holes are quickly divided into a lot of convex polygons, say, triangles and convexquadrilaterals. The algorithm for the polygon division runs in O( (k+1) (n-3)) , where n is thetotal number of the vertices of the merged loop of the facet, and k is the number of the concavevertices of the merged one.
文摘Selective harmonic elimination(SHE) in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective function with proper weighting is proposed and also its efficiency is compared with the objective function which is more similar to the proposed one. To enhance the ability of the SHE in eliminating high number of selected harmonics, at each level of the output voltage, one slot is created. The SHE problem is solved by imperialist competitive algorithm(ICA). The conventional SHE methods cannot eliminate the selected harmonics and satisfy the fundamental component in some ranges of modulation indexes. So, to surmount the SHE defect, a DC-DC converter is applied. Theoretical results are substantiated by simulations and experimental results for a 9-level multilevel inverter. The obtained results illustrate that the proposed method successfully minimizes a large number of identified harmonics which consequences very low total harmonic distortion of output voltage.
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312200) and the Center for Bioinformatics Pro-gram Grant of Harvard Center of Neurodegeneration and Repair,Harvard Medical School, Harvard University, Boston, USA
文摘In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.
基金supported by the National Natural Science Foundation of China(Nos.41374130 and 41604154)
文摘With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.
基金supported by the National Natural Science Foundation of China (No. 61573260,No. 62073245,No. U1713211)
文摘Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, we propose a safety-critical control scheme for robotic systems using an adaptive error elimination algorithm and optimization-based nonlinear optimal predictive control(NOPC) framework. The novelty of the proposed work lies in that an adaptive error elimination controller is designed to deal with the problem of stabilization of walking gait, which ensures that robot joint trajectory can compensate for the limitation of the template model. In order to be independent of system parameters and disturbances, a sliding mode controller is further designed under an uncertain environment. This approach takes into account simultaneously with foot position and orientation based on NOPC optimization. It tracks the modified trajectories constrained with the centroidal momentum dynamics. Finally, simulations is utilized to verify the efectiveness of the mentioned methods. The results indicate that the tracking efect of joint trajectory is better safety-critical nonlinear optimal predictive control with adaptive error elimination algorithm.
文摘In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.
