The artificial neural network(ANN)and hybrid of artificial neural network and genetic algorithm(GANN)were appliedto predict the optimized conditions of column leaching of copper oxide ore with relations of input and o...The artificial neural network(ANN)and hybrid of artificial neural network and genetic algorithm(GANN)were appliedto predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data.The leachingexperiments were performed in three columns with the heights of2,4and6m and in particle size of<25.4and<50.8mm.Theeffects of different operating parameters such as column height,particle size,acid flow rate and leaching time were studied tooptimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale.It was found that therecovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height.Theefficiency of GANN and ANN algorithms was compared with each other.The results showed that GANN is more efficient than ANNin predicting copper recovery.The proposed model can be used to predict the Cu recovery with a reasonable error.展开更多
This paper presents a recovery algorithm for self-stabilizing communication protocols. It first describes some concepts and a formal description method for the algorithm. Then it proposes the algorithm procedures, pro...This paper presents a recovery algorithm for self-stabilizing communication protocols. It first describes some concepts and a formal description method for the algorithm. Then it proposes the algorithm procedures, proves its correctness and analyses its complexity. Finally, it also verifies the availability and efficiency of the algorithm by illustrating an example protocol with multi-processes.展开更多
Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design...Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.展开更多
In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms accordin...In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms according to the correlation between received signals and hyper planes, which are composed by column vectors of the mixing matrix, and uses these atoms to recover source signals. Simulation results demonstrate that the PP algorithm has low complexity and higher accuracy as compared with basic pursuit(BP), orthogonal matching pursuit(OMP), and adaptive sparsity matching pursuit(ASMP) algorithms.展开更多
Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral proce...Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.展开更多
In this work,an optimal Q algorithm based on a collision recovery scheme is presented. Tags use BIBD-( 16,4,1) codes instead of RN16 s. Therefore,readers can make a valid recognition even in collision slots. A way of ...In this work,an optimal Q algorithm based on a collision recovery scheme is presented. Tags use BIBD-( 16,4,1) codes instead of RN16 s. Therefore,readers can make a valid recognition even in collision slots. A way of getting the optimal slot-count parameter is studied and an optimal Q algorithm is proposed. The theoretical and simulation results show that the proposed algorithm can improve reading efficiency by 100% more than the conventional Q algorithm. Moreover,the proposed scheme changes little to the existing standard. Thus,it is easy to implement and compatible with ISO 18000-6C.展开更多
Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely the signal sparsity of the impulse respon...Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely the signal sparsity of the impulse response of the UWB channel that is exploited in this work aiming at UWB channel estimation based on Compressed Sensing (CS). However, these multipath arrivals mainly depend on the channel environments that generate different sparse levels (low-sparse or high-sparse) of the UWB channels. According to this basis, we have analyzed the two most basic recovery algorithms, one based on linear programming Basis Pursuit (BP), another using greedy method Orthogonal Matching Pursuit (OMP), and chosen the best recovery algorithm which are suitable to the sparse level for each type of channel environment. Besides, the results of this work is an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.展开更多
Under the background of the rapid development of the air transport industry, the abnormal phenomenon of flights has become increasingly serious due to various factors such as the gradual reduction of resources, advers...Under the background of the rapid development of the air transport industry, the abnormal phenomenon of flights has become increasingly serious due to various factors such as the gradual reduction of resources, adverse climatic conditions, problems in air traffic control and mechanical failures. In order to reduce losses, it has become a major problem for airlines to use optimization algorithm to study the recovery of abnormal flights. By upgrading the passenger recovery engine, the purpose of this paper is to provide the optimal recovery scheme for passengers, so as to reduce the risk of transferring overseas flights, and thus reduce the economic loss of airlines. In this paper, the optimization model and algorithm based on network flow, combined with actual business requirements, comprehensively consider multiple optimization objectives to quickly generate passenger recovery solutions, and at the same time achieve the optimal income of airlines and the acceptance rate of passenger recovery, so as to balance the two. The practicability and effectiveness of the proposed model and algorithm are proved by some concrete examples.展开更多
Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for a...Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for accurate support recovery of the block K-joint sparse matrix via the BMMV algorithm in the noisy case. Furthermore, we show the optimality of the condition we proposed in the absence of noise when the problem reduces to single measurement vector case.展开更多
With the rise of artificial intelligence(AI)in mineral processing,predicting the flotation indexes has attracted significant research attention.Nevertheless,current prediction models suffer from low accuracy and high ...With the rise of artificial intelligence(AI)in mineral processing,predicting the flotation indexes has attracted significant research attention.Nevertheless,current prediction models suffer from low accuracy and high prediction errors.Therefore,this paper utilizes a two-step procedure.First,the outliers are pro-cessed using the box chart method and filtering algorithm.Then,the decision tree(DT),support vector regression(SVR),random forest(RF),and the bagging,boosting,and stacking integration algorithms are employed to construct a flotation recovery prediction model.Extensive experiments compared the prediction accuracy of six modeling methods on flotation recovery and delved into the impact of diverse base model combinations on the stacking model’s prediction accuracy.In addition,field data have veri-fied the model’s effectiveness.This study demonstrates that the stacking ensemble approaches,which uses ten variables to predict flotation recovery,yields a more favorable prediction effect than the bagging ensemble approach and single models,achieving MAE,RMSE,R2,and MRE scores of 0.929,1.370,0.843,and 1.229%,respectively.The hit rates,within an error range of±2%and±4%,are 82.4%and 94.6%.Consequently,the prediction effect is relatively precise and offers significant value in the context of actual production.展开更多
文摘The artificial neural network(ANN)and hybrid of artificial neural network and genetic algorithm(GANN)were appliedto predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data.The leachingexperiments were performed in three columns with the heights of2,4and6m and in particle size of<25.4and<50.8mm.Theeffects of different operating parameters such as column height,particle size,acid flow rate and leaching time were studied tooptimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale.It was found that therecovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height.Theefficiency of GANN and ANN algorithms was compared with each other.The results showed that GANN is more efficient than ANNin predicting copper recovery.The proposed model can be used to predict the Cu recovery with a reasonable error.
基金National Natural Science Foundation of China and NSF of Hubei Province.
文摘This paper presents a recovery algorithm for self-stabilizing communication protocols. It first describes some concepts and a formal description method for the algorithm. Then it proposes the algorithm procedures, proves its correctness and analyses its complexity. Finally, it also verifies the availability and efficiency of the algorithm by illustrating an example protocol with multi-processes.
基金Project supported by the National Natural Science Foundation of China(No.61603322)the Research Foundation of Education Bureau of Hunan Province of China(No.16C1542)
文摘Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.
基金supported by the National Natural Science Foundation of China(61201134)the 111 Project(B08038)
文摘In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms according to the correlation between received signals and hyper planes, which are composed by column vectors of the mixing matrix, and uses these atoms to recover source signals. Simulation results demonstrate that the PP algorithm has low complexity and higher accuracy as compared with basic pursuit(BP), orthogonal matching pursuit(OMP), and adaptive sparsity matching pursuit(ASMP) algorithms.
基金the support of the Department of Research and Development of Sarcheshmeh copper plants for this research
文摘Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.
基金Supported by the National Natural Science Foundation of China(No.61340005)Beijing Natural Science Foundation(No.4132012)+2 种基金Beijing Education Committee Science and Technology Development Plan(No.KM201411232011)Beijing Outstanding Personnel Training Project(No.2013D005007000006)Scientific Research Improving Project-Intelligent Sense and Information Processing(No.5211524100)
文摘In this work,an optimal Q algorithm based on a collision recovery scheme is presented. Tags use BIBD-( 16,4,1) codes instead of RN16 s. Therefore,readers can make a valid recognition even in collision slots. A way of getting the optimal slot-count parameter is studied and an optimal Q algorithm is proposed. The theoretical and simulation results show that the proposed algorithm can improve reading efficiency by 100% more than the conventional Q algorithm. Moreover,the proposed scheme changes little to the existing standard. Thus,it is easy to implement and compatible with ISO 18000-6C.
文摘Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely the signal sparsity of the impulse response of the UWB channel that is exploited in this work aiming at UWB channel estimation based on Compressed Sensing (CS). However, these multipath arrivals mainly depend on the channel environments that generate different sparse levels (low-sparse or high-sparse) of the UWB channels. According to this basis, we have analyzed the two most basic recovery algorithms, one based on linear programming Basis Pursuit (BP), another using greedy method Orthogonal Matching Pursuit (OMP), and chosen the best recovery algorithm which are suitable to the sparse level for each type of channel environment. Besides, the results of this work is an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.
文摘Under the background of the rapid development of the air transport industry, the abnormal phenomenon of flights has become increasingly serious due to various factors such as the gradual reduction of resources, adverse climatic conditions, problems in air traffic control and mechanical failures. In order to reduce losses, it has become a major problem for airlines to use optimization algorithm to study the recovery of abnormal flights. By upgrading the passenger recovery engine, the purpose of this paper is to provide the optimal recovery scheme for passengers, so as to reduce the risk of transferring overseas flights, and thus reduce the economic loss of airlines. In this paper, the optimization model and algorithm based on network flow, combined with actual business requirements, comprehensively consider multiple optimization objectives to quickly generate passenger recovery solutions, and at the same time achieve the optimal income of airlines and the acceptance rate of passenger recovery, so as to balance the two. The practicability and effectiveness of the proposed model and algorithm are proved by some concrete examples.
文摘Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for accurate support recovery of the block K-joint sparse matrix via the BMMV algorithm in the noisy case. Furthermore, we show the optimality of the condition we proposed in the absence of noise when the problem reduces to single measurement vector case.
基金supported by the National Key R&D Program of China(No.2023YFC2908200)National Natural Science Foundation of China(No.52174249)Key Research and Development Program of Jiangxi Province(No.20203BBGL73231).
文摘With the rise of artificial intelligence(AI)in mineral processing,predicting the flotation indexes has attracted significant research attention.Nevertheless,current prediction models suffer from low accuracy and high prediction errors.Therefore,this paper utilizes a two-step procedure.First,the outliers are pro-cessed using the box chart method and filtering algorithm.Then,the decision tree(DT),support vector regression(SVR),random forest(RF),and the bagging,boosting,and stacking integration algorithms are employed to construct a flotation recovery prediction model.Extensive experiments compared the prediction accuracy of six modeling methods on flotation recovery and delved into the impact of diverse base model combinations on the stacking model’s prediction accuracy.In addition,field data have veri-fied the model’s effectiveness.This study demonstrates that the stacking ensemble approaches,which uses ten variables to predict flotation recovery,yields a more favorable prediction effect than the bagging ensemble approach and single models,achieving MAE,RMSE,R2,and MRE scores of 0.929,1.370,0.843,and 1.229%,respectively.The hit rates,within an error range of±2%and±4%,are 82.4%and 94.6%.Consequently,the prediction effect is relatively precise and offers significant value in the context of actual production.
