Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structur...Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structure that pulls the testis into the scrotum.However,the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated.To identify the androgen-regulated genes,we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout(Lhcgr KO)mice,an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency.We found that the expression of secreted protein acidic and rich in cysteine(SPARC)-related modular calcium binding 1(Smoc1)was the most severely suppressed at both the transcript and protein levels,while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice.The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist,flutamide.In addition,in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells.In the cultures of myogenic differentiation medium,both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7(Pax7)and myogenic factor 5(Myf5).After short-interfering RNA-mediated knocking down of Smoc1,the expression of Pax7 and Myf5 diminished,and testosterone alone did not recover,but additional SMOC1 did.These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller ob...The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller objective function values than the K-means algorithm.However,CDKM is sensitive to initialization,which makes the K-means objective function values not small enough.Since selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of CDKM.The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value further.The split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains unchanged.The algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition matrix.Experiments on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional datasets.The proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.展开更多
Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for ga...Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.展开更多
We prove,under mild conditions,the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model,both in batch gradient descent and stochastic gradient descent.We also discuss a ...We prove,under mild conditions,the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model,both in batch gradient descent and stochastic gradient descent.We also discuss a Riemannian version of the Adam algorithm.We show numerical simulations of these algorithms on various benchmarks.展开更多
With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rej...With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.展开更多
The current work aims at employing a gradient descent algorithm for optimizing the thrust of a flapping wing. An in-house solver has been employed, along with mesh movement methodologies to capture the dynamics of flo...The current work aims at employing a gradient descent algorithm for optimizing the thrust of a flapping wing. An in-house solver has been employed, along with mesh movement methodologies to capture the dynamics of flow around the airfoil. An efficient framework for implementing the coupled solver and optimization in a multicore environment has been implemented for the generation of optimized solutionsmaximizing thrust performance & computational speed.展开更多
In [7], Cross showed that the spectrum of a linear relation T on a normed space satisfies the spectral mapping theorem. In this paper, we extend the notion of essential ascent and descent for an operator acting on a v...In [7], Cross showed that the spectrum of a linear relation T on a normed space satisfies the spectral mapping theorem. In this paper, we extend the notion of essential ascent and descent for an operator acting on a vector space to linear relations acting on Banach spaces. We focus to define and study the descent, essential descent, ascent and essential ascent spectrum of a linear relation everywhere defined on a Banach space X. In particular, we show that the corresponding spectrum satisfy the polynomial version of the spectral mapping theorem.展开更多
In order to obtain a high-quality weld during the laser welding process, extracting the characteristic parameters of weld pool is an important issue for automated welding. In this paper, the type 304 austenitic stainl...In order to obtain a high-quality weld during the laser welding process, extracting the characteristic parameters of weld pool is an important issue for automated welding. In this paper, the type 304 austenitic stainless steel is welded by a 5 kW high-power fiber laser and a high-speed camera is employed to capture the topside images of weld pools. Then we propose a robust visual-detection approach for the molten pool based on the supervised descent method. It provides an elegant framework for representing the outline of a weld pool and is especially efficient for weld pool detection in the presence of strong uncertainties and disturbances. Finally, welding experimental results verified that the proposed approach can extract the weld pool boundary accurately, which will lay a solid foundation for controlling the weld quality of fiber laser welding process.展开更多
This paper presents a coordinate gradient descent approach for minimizing the sum of a smooth function and a nonseparable convex function.We find a search direction by solving a subproblem obtained by a second-order a...This paper presents a coordinate gradient descent approach for minimizing the sum of a smooth function and a nonseparable convex function.We find a search direction by solving a subproblem obtained by a second-order approximation of the smooth function and adding a separable convex function.Under a local Lipschitzian error bound assumption,we show that the algorithm possesses global and local linear convergence properties.We also give some numerical tests(including image recovery examples) to illustrate the efficiency of the proposed method.展开更多
A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and...A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems.Aiming at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training process.With it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality recommendations.Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.展开更多
The original free vortex wake model was used for numerical investigation.Calculation of the aerodynamic characteristics in hover and vertical descent modes in the range of vertical descent speed of 0–30 m/s including...The original free vortex wake model was used for numerical investigation.Calculation of the aerodynamic characteristics in hover and vertical descent modes in the range of vertical descent speed of 0–30 m/s including the Vortex Ring State(VRS)area was performed.The calculations were carried out under the condition of variable blade pitch angle values providing a fixed timeaverage thrust value.Visualization data of free vortex wake shapes,flow structures,and velocity fields were obtained and analyzed.The time-dependences of the rotor’s thrust and torque coefficients were obtained and analyzed.The obtained data allows determining the boundaries of the VRS area by various criteria such as rotor thrust and torque pulsations,growth of rotor power consumption relative to the hover,growth of rotor induced velocities relative to the hover,and growth of the required rotor blade pitch angles values.The results of the study are compared with experimental and calculated data of other authors and can significantly supplement the available results of experimental and computational studies in this area.展开更多
In this paper,we consider a distributed resource allocation problem of minimizing a global convex function formed by a sum of local convex functions with coupling constraints.Based on neighbor communication and stocha...In this paper,we consider a distributed resource allocation problem of minimizing a global convex function formed by a sum of local convex functions with coupling constraints.Based on neighbor communication and stochastic gradient,a distributed stochastic mirror descent algorithm is designed for the distributed resource allocation problem.Sublinear convergence to an optimal solution of the proposed algorithm is given when the second moments of the gradient noises are summable.A numerical example is also given to illustrate the effectiveness of the proposed algorithm.展开更多
Mathematical models for burden descending process have been applied to obtain whole burden structures in blast furnace,whereas the accuracy of those burden descent models has not been sufficiently investigated.Special...Mathematical models for burden descending process have been applied to obtain whole burden structures in blast furnace,whereas the accuracy of those burden descent models has not been sufficiently investigated.Special evaluation method based on timeline burden profiles was established to quantitatively evaluate the error between experimental and modeled burden structures.Four existing burden descent models were utilized to describe the burden structure of a 1/20 scaled warm blast furnace.Input modeling conditions including initial burden profile,descending volumes in each time interval,and normalized descending velocity distribution were determined via special image processing technology.Modeled burden structures were evaluated combined with the published experimental data.It is found that all the models caught the main profile of the burden structure.Furthermore,the improved nonuniform descent model（Model IV）shows the highest level of precision especially when burden descends with unstable velocity distribution tendency.Meanwhile,the traditional nonuniform descent model（Model III）may also be desirable to model the burden descending process when the burden descending velocity presents a linear tendency.Finally,the uniform descent model（Model I）might be the first option for roughly predicting burden structure.展开更多
A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysi...A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysis, it is shown that search directions of the proposed method satisfy the sufficient descent condition, independent of the line search and the objective function convexity. Global convergence of the method is established under an Armijo–type line search condition. Numerical experiments show practical efficiency of the proposed method.展开更多
In this paper, a new method named as the gradually descent method was proposed to solve the discrete global optimization problem. With the aid of an auxiliary function, this method enables to convert the problem of fi...In this paper, a new method named as the gradually descent method was proposed to solve the discrete global optimization problem. With the aid of an auxiliary function, this method enables to convert the problem of finding one discrete minimizer of the objective function f to that of finding another at each cycle. The auxiliary function can ensure that a point, except a prescribed point, is not its integer stationary point if the value of objective function at the point is greater than the scalar which is chosen properly. This property leads to a better minimizer of f found more easily by some classical local search methods. The computational results show that this algorithm is quite efficient and reliable for solving nonlinear integer programming problems.展开更多
The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated...The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm.The deregularization maximized the energy of the solution,which was opposite to the Tikhonov regularization where the energy was minimized.However,combined with box-constraints,the deregularization forced the solution to be close to the binary set.It further exploited the box-constrained dichotomous coordinate descent algorithm and adapted it to the nonstationary iterative Tikhonov regularization to present an efficient detector.As a result,the worst-case and average complexity are reduced down as K2.8 and K2.5 floating point operation per second,respectively.The development improves the "efficient frontier" in multiuser detection,which is illustrated by simulation results.In addition,most operations in the detector are additions and bit-shifts.This makes the proposed technique attractive for fixed-point hardware implementation.展开更多
In this paper,a-Browder’s theorem and a-Weyl’s theorem for bounded linear operators are studied by means of the property of the topological uniform descent.The sufficient and necessary conditions for a bounded linea...In this paper,a-Browder’s theorem and a-Weyl’s theorem for bounded linear operators are studied by means of the property of the topological uniform descent.The sufficient and necessary conditions for a bounded linear operator defined on a Hilbert space holding aBrowder’s theorem and a-Weyl’s theorem are established.As a consequence of the main result,the new judgements of a-Browder’s theorem and a-Weyl’s theorem for operator function are discussed.展开更多
This study aimed to compare gait properties during level walking and during stair ascent and descent with varying loads. Fifteen healthy young men (mean age: 22.1 ± 1.6 years) walked while holding four different ...This study aimed to compare gait properties during level walking and during stair ascent and descent with varying loads. Fifteen healthy young men (mean age: 22.1 ± 1.6 years) walked while holding four different loads relative to each subject’s body mass (0, 20, 40 and 60% of body mass: BM) on their backs. Stance time, swing time, and double support times were selected as gait parameters. All parameters showed a maximal value during stair ascent and a minimum value during level walking. Stance and double support times increased significan- tly with each load during level walking and during stair ascent and descent. In conclusion, st- air ascent and descent creates more unstable movement than level walking regardless of the weight of the load. The effect of loads on gait increases with the weight of the load and becomes obvious once the load exceeds 60% of BM.展开更多
基金supported in part by the Department of OB/GYN research funds(University of Louisville,Louisville,KY,USA)Jilin Province Health Technology Capability Enhancement funds(No.2022JC055).
文摘Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structure that pulls the testis into the scrotum.However,the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated.To identify the androgen-regulated genes,we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout(Lhcgr KO)mice,an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency.We found that the expression of secreted protein acidic and rich in cysteine(SPARC)-related modular calcium binding 1(Smoc1)was the most severely suppressed at both the transcript and protein levels,while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice.The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist,flutamide.In addition,in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells.In the cultures of myogenic differentiation medium,both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7(Pax7)and myogenic factor 5(Myf5).After short-interfering RNA-mediated knocking down of Smoc1,the expression of Pax7 and Myf5 diminished,and testosterone alone did not recover,but additional SMOC1 did.These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
基金funded by National Defense Basic Research Program,grant number JCKY2019411B001funded by National Key Research and Development Program,grant number 2022YFC3601305funded by Key R&D Projects of Jilin Provincial Science and Technology Department,grant number 20210203218SF.
文摘The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller objective function values than the K-means algorithm.However,CDKM is sensitive to initialization,which makes the K-means objective function values not small enough.Since selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of CDKM.The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value further.The split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains unchanged.The algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition matrix.Experiments on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional datasets.The proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.
基金the Natural Science Foundation of Ningxia Province(No.2021AAC03230).
文摘Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.
基金partially supported by NSF Grants DMS-1854434,DMS-1952644,and DMS-2151235 at UC Irvinesupported by NSF Grants DMS-1924935,DMS-1952339,DMS-2110145,DMS-2152762,and DMS-2208361,and DOE Grants DE-SC0021142 and DE-SC0002722.
文摘We prove,under mild conditions,the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model,both in batch gradient descent and stochastic gradient descent.We also discuss a Riemannian version of the Adam algorithm.We show numerical simulations of these algorithms on various benchmarks.
基金the 2021 Key Project of Natural Science and Technology of Yangzhou Polytechnic Institute,Active Disturbance Rejection and Fault-Tolerant Control of Multi-Rotor Plant ProtectionUAV Based on QBall-X4(Grant Number 2021xjzk002).
文摘With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.
文摘The current work aims at employing a gradient descent algorithm for optimizing the thrust of a flapping wing. An in-house solver has been employed, along with mesh movement methodologies to capture the dynamics of flow around the airfoil. An efficient framework for implementing the coupled solver and optimization in a multicore environment has been implemented for the generation of optimized solutionsmaximizing thrust performance & computational speed.
文摘In [7], Cross showed that the spectrum of a linear relation T on a normed space satisfies the spectral mapping theorem. In this paper, we extend the notion of essential ascent and descent for an operator acting on a vector space to linear relations acting on Banach spaces. We focus to define and study the descent, essential descent, ascent and essential ascent spectrum of a linear relation everywhere defined on a Banach space X. In particular, we show that the corresponding spectrum satisfy the polynomial version of the spectral mapping theorem.
基金Project was supported by the National Key R&D Program of China(Grant No.2017YFB1104404)
文摘In order to obtain a high-quality weld during the laser welding process, extracting the characteristic parameters of weld pool is an important issue for automated welding. In this paper, the type 304 austenitic stainless steel is welded by a 5 kW high-power fiber laser and a high-speed camera is employed to capture the topside images of weld pools. Then we propose a robust visual-detection approach for the molten pool based on the supervised descent method. It provides an elegant framework for representing the outline of a weld pool and is especially efficient for weld pool detection in the presence of strong uncertainties and disturbances. Finally, welding experimental results verified that the proposed approach can extract the weld pool boundary accurately, which will lay a solid foundation for controlling the weld quality of fiber laser welding process.
基金supported by NSFC Grant 10601043,NCETXMUSRF for ROCS,SEM+2 种基金supported by RGC 201508HKBU FRGssupported by the Hong Kong Research Grant Council
文摘This paper presents a coordinate gradient descent approach for minimizing the sum of a smooth function and a nonseparable convex function.We find a search direction by solving a subproblem obtained by a second-order approximation of the smooth function and adding a separable convex function.Under a local Lipschitzian error bound assumption,we show that the algorithm possesses global and local linear convergence properties.We also give some numerical tests(including image recovery examples) to illustrate the efficiency of the proposed method.
基金supported in part by the National Natural Science Foundation of China(61772493)the Deanship of Scientific Research(DSR)at King Abdulaziz University(RG-48-135-40)+1 种基金Guangdong Province Universities and College Pearl River Scholar Funded Scheme(2019)the Natural Science Foundation of Chongqing(cstc2019jcyjjqX0013)。
文摘A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems.Aiming at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training process.With it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality recommendations.Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.
文摘The original free vortex wake model was used for numerical investigation.Calculation of the aerodynamic characteristics in hover and vertical descent modes in the range of vertical descent speed of 0–30 m/s including the Vortex Ring State(VRS)area was performed.The calculations were carried out under the condition of variable blade pitch angle values providing a fixed timeaverage thrust value.Visualization data of free vortex wake shapes,flow structures,and velocity fields were obtained and analyzed.The time-dependences of the rotor’s thrust and torque coefficients were obtained and analyzed.The obtained data allows determining the boundaries of the VRS area by various criteria such as rotor thrust and torque pulsations,growth of rotor power consumption relative to the hover,growth of rotor induced velocities relative to the hover,and growth of the required rotor blade pitch angles values.The results of the study are compared with experimental and calculated data of other authors and can significantly supplement the available results of experimental and computational studies in this area.
基金the National Key Research and Development Program of China(No.2016YFB0901900)the National Natural Science Foundation of China(No.61733018)the China Special Postdoctoral Science Foundation Funded Project(No.Y990075G21).
文摘In this paper,we consider a distributed resource allocation problem of minimizing a global convex function formed by a sum of local convex functions with coupling constraints.Based on neighbor communication and stochastic gradient,a distributed stochastic mirror descent algorithm is designed for the distributed resource allocation problem.Sublinear convergence to an optimal solution of the proposed algorithm is given when the second moments of the gradient noises are summable.A numerical example is also given to illustrate the effectiveness of the proposed algorithm.
基金Item Sponsored by National Natural Science Foundation of China(61290325)
文摘Mathematical models for burden descending process have been applied to obtain whole burden structures in blast furnace,whereas the accuracy of those burden descent models has not been sufficiently investigated.Special evaluation method based on timeline burden profiles was established to quantitatively evaluate the error between experimental and modeled burden structures.Four existing burden descent models were utilized to describe the burden structure of a 1/20 scaled warm blast furnace.Input modeling conditions including initial burden profile,descending volumes in each time interval,and normalized descending velocity distribution were determined via special image processing technology.Modeled burden structures were evaluated combined with the published experimental data.It is found that all the models caught the main profile of the burden structure.Furthermore,the improved nonuniform descent model（Model IV）shows the highest level of precision especially when burden descends with unstable velocity distribution tendency.Meanwhile,the traditional nonuniform descent model（Model III）may also be desirable to model the burden descending process when the burden descending velocity presents a linear tendency.Finally,the uniform descent model（Model I）might be the first option for roughly predicting burden structure.
基金Supported by Research Council of Semnan University
文摘A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysis, it is shown that search directions of the proposed method satisfy the sufficient descent condition, independent of the line search and the objective function convexity. Global convergence of the method is established under an Armijo–type line search condition. Numerical experiments show practical efficiency of the proposed method.
基金Project supported by the National Natural Science Foundation of China(Grant No.10271073)
文摘In this paper, a new method named as the gradually descent method was proposed to solve the discrete global optimization problem. With the aid of an auxiliary function, this method enables to convert the problem of finding one discrete minimizer of the objective function f to that of finding another at each cycle. The auxiliary function can ensure that a point, except a prescribed point, is not its integer stationary point if the value of objective function at the point is greater than the scalar which is chosen properly. This property leads to a better minimizer of f found more easily by some classical local search methods. The computational results show that this algorithm is quite efficient and reliable for solving nonlinear integer programming problems.
文摘The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm.The deregularization maximized the energy of the solution,which was opposite to the Tikhonov regularization where the energy was minimized.However,combined with box-constraints,the deregularization forced the solution to be close to the binary set.It further exploited the box-constrained dichotomous coordinate descent algorithm and adapted it to the nonstationary iterative Tikhonov regularization to present an efficient detector.As a result,the worst-case and average complexity are reduced down as K2.8 and K2.5 floating point operation per second,respectively.The development improves the "efficient frontier" in multiuser detection,which is illustrated by simulation results.In addition,most operations in the detector are additions and bit-shifts.This makes the proposed technique attractive for fixed-point hardware implementation.
基金Supported by the 2021 General Special Scientific Research Project of Education Department of Shaanxi Provincial Government(21JK0637)Science and Technology Planning Project of Weinan Science and Technology Bureau(2022ZDYFJH-11)2021 Talent Project of Weinan Normal University(2021RC16)。
文摘In this paper,a-Browder’s theorem and a-Weyl’s theorem for bounded linear operators are studied by means of the property of the topological uniform descent.The sufficient and necessary conditions for a bounded linear operator defined on a Hilbert space holding aBrowder’s theorem and a-Weyl’s theorem are established.As a consequence of the main result,the new judgements of a-Browder’s theorem and a-Weyl’s theorem for operator function are discussed.
文摘This study aimed to compare gait properties during level walking and during stair ascent and descent with varying loads. Fifteen healthy young men (mean age: 22.1 ± 1.6 years) walked while holding four different loads relative to each subject’s body mass (0, 20, 40 and 60% of body mass: BM) on their backs. Stance time, swing time, and double support times were selected as gait parameters. All parameters showed a maximal value during stair ascent and a minimum value during level walking. Stance and double support times increased significan- tly with each load during level walking and during stair ascent and descent. In conclusion, st- air ascent and descent creates more unstable movement than level walking regardless of the weight of the load. The effect of loads on gait increases with the weight of the load and becomes obvious once the load exceeds 60% of BM.
