Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this wor...Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.展开更多
We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can condu...We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.展开更多
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ...The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.展开更多
Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input ...Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
Artificial intelligence(AI),encompassing machine learning and deep learning,is being extensively used in medical sciences.It is slated to positively impact the diagnosis and prognostication of various diseases.Deep le...Artificial intelligence(AI),encompassing machine learning and deep learning,is being extensively used in medical sciences.It is slated to positively impact the diagnosis and prognostication of various diseases.Deep learning,a subset of AI,has been instrumental in diagnosing diabetic retinopathy(DR),diabetic macular edema,glaucoma,age-related macular degeneration,and numerous other ocular diseases.AI performs equally well in the early prediction of glaucoma and agerelated macular degeneration.Integrating AI with telemedicine promises to improve healthcare delivery,although challenges persist in implementing AI algorithms,especially in deve-loping countries.This review provides a compre hensive summary of AI,its applications in ophthalmology,particularly DR,the diverse algorithms utilized for different ocular conditions,and prospects for the future integration of AI in eye care.展开更多
Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be useful.Published examples of n...Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be useful.Published examples of neural networks have mostly been limited to one individual mineral deposit for training.Variation of geologic features among deposits within a type are so large that a single deposit cannot provide proper information to train a neural net to generalize and guide exploration for other deposits.Models trained with only one deposit tend to be academic successes but are not of practical value in exploration for other deposits.This is why it takes much experience examining many deposits to properly train an economic geologist—a neural network is not any different.Two examples of shallow neural networks are used to demonstrate the power of neural networks to possibly locate undiscovered deposits and to provide some suggestions of how to deal with missing data.The training data needs to include information spatially related to known deposits and hopefully information from many different deposits of the type.Lessons learned from these and other examples point to a proposed sampling plan for data that could lead to a generalized neural network for exploration.In this plan,10 or more well-explored gold-rich porphyry copper deposits from around the world with 100 or more sample sites near and some distance from each deposit would probably capture important variability among such deposits and provide proper data to train and test a shallow neural network to predict locations of undiscovered deposits.展开更多
An effective design and optimum production strategies of a well depend on the accurate prediction of its bottom hole pressure(BHP)which may be calculated or determined by several methods.However,it is not practical te...An effective design and optimum production strategies of a well depend on the accurate prediction of its bottom hole pressure(BHP)which may be calculated or determined by several methods.However,it is not practical technically or economically to apply for a well test or to deploy a permanent pressure gauge in the bottom hole to predict the BHP.Consequently,several correlations and mechanistic models based on the knownsurfacemeasurementshave beendeveloped.Unfortunately,all these tools(correlations&mechanistic models)are limited to some conditions and intervals of application.Therefore,establish a global model that ensures a large coverage of conditions with a reduced cost and high accuracy becomes a necessity.In this study,we propose new models for estimating bottom hole pressure of vertical wells with multiphase flow.First,Artificial Neural Network(ANN)based on back propagation training(BP-ANN)with 12 neurons in its hidden layer is established using trial and error.The next methods correspond to optimized or evolved neural networks(optimize the weights and thresholds of the neural networks)with Grey Wolves Optimization(GWO),and then its accuracy to reach the global optima is compared with 2 other naturally inspired algorithms which are the most used in the optimization field:Genetic Algorithm(GA)and Particle Swarms Optimization(PSO).The models were developed and tested using 100 field data collected from Algerian fields and covering a wide range of variables.The obtained results demonstrate the superiority of the hybridization ANN-GWO compared with the 2 other hybridizations or with the BP learning alone.Furthermore,the evolved neural networks with these global optimization algorithms are strongly shown to be highly effective to improve the performance of the neural networks to estimate flowing BHP over existing approaches and correlations.展开更多
Purpose-Diabetic retinopathy(DR)is a central root of blindness all over the world.Though DR is tough to diagnose in starting stages,and the detection procedure might be time-consuming even for qualified experts.Nowada...Purpose-Diabetic retinopathy(DR)is a central root of blindness all over the world.Though DR is tough to diagnose in starting stages,and the detection procedure might be time-consuming even for qualified experts.Nowadays,intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various diseases.Therefore,a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark dataset.Design/methodology/approach-The proposed DR diagnostic procedure involves four main steps:(1)image pre-processing,(2)blood vessel segmentation,(3)feature extraction,and(4)classification.Initially,the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization(CLAHE)and average filter.In the next step,the blood vessel segmentation is carried out using a segmentation process with optimized gray-level thresholding.Once the blood vessels are extracted,feature extraction is done,using Local Binary Pattern(LBP),Texture Energy Measurement(TEM based on Laws of Texture Energy),and two entropy computations-Shanon’s entropy,and Kapur’s entropy.These collected features are subjected to a classifier called Neural Network(NN)with an optimized training algorithm.Both the gray-level thresholding and NN is enhanced by the Modified Levy Updated-Dragonfly Algorithm(MLU-DA),which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the NN.Finally,this classification error can correctly prove the efficiency of the proposed DR detection model.Findings-The overall accuracy of the proposed MLU-DA was 16.6%superior to conventional classifiers,and the precision of the developed MLU-DA was 22%better than LM-NN,16.6%better than PSO-NN,GWO-NN,and DA-NN.Finally,it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting DR.Originality/value-This paper adopts the latest optimization algorithm called MLU-DA-Neural Network with optimal gray-level thresholding for detecting diabetic retinopathy disease.This is the first work utilizes MLU-DA-based Neural Network for computer-aided Diabetic Retinopathy diagnosis.展开更多
基金supported by the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302100)the National Natural Science Foundation of China(Grant Nos.12361131576,92265205,and 92476205).
文摘Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.
基金supported by the National Key R&D Program of China (No.2019YFB2203700)the National Natural Science Foundation of China (No.61822508)。
文摘We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.
基金supported by the National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154)the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)+1 种基金the Young and Middle-Aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province,China(Grant No.T2023007)the National Natural Science Foundation of China(Grant No.U23A20318).
文摘The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.
基金Supported by Beijing Municipal Education Commission (No.xk100100435) and the Key Research Project of Science andTechnology from Sinopec (No.E03007).
文摘Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
文摘Artificial intelligence(AI),encompassing machine learning and deep learning,is being extensively used in medical sciences.It is slated to positively impact the diagnosis and prognostication of various diseases.Deep learning,a subset of AI,has been instrumental in diagnosing diabetic retinopathy(DR),diabetic macular edema,glaucoma,age-related macular degeneration,and numerous other ocular diseases.AI performs equally well in the early prediction of glaucoma and agerelated macular degeneration.Integrating AI with telemedicine promises to improve healthcare delivery,although challenges persist in implementing AI algorithms,especially in deve-loping countries.This review provides a compre hensive summary of AI,its applications in ophthalmology,particularly DR,the diverse algorithms utilized for different ocular conditions,and prospects for the future integration of AI in eye care.
文摘Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be useful.Published examples of neural networks have mostly been limited to one individual mineral deposit for training.Variation of geologic features among deposits within a type are so large that a single deposit cannot provide proper information to train a neural net to generalize and guide exploration for other deposits.Models trained with only one deposit tend to be academic successes but are not of practical value in exploration for other deposits.This is why it takes much experience examining many deposits to properly train an economic geologist—a neural network is not any different.Two examples of shallow neural networks are used to demonstrate the power of neural networks to possibly locate undiscovered deposits and to provide some suggestions of how to deal with missing data.The training data needs to include information spatially related to known deposits and hopefully information from many different deposits of the type.Lessons learned from these and other examples point to a proposed sampling plan for data that could lead to a generalized neural network for exploration.In this plan,10 or more well-explored gold-rich porphyry copper deposits from around the world with 100 or more sample sites near and some distance from each deposit would probably capture important variability among such deposits and provide proper data to train and test a shallow neural network to predict locations of undiscovered deposits.
文摘An effective design and optimum production strategies of a well depend on the accurate prediction of its bottom hole pressure(BHP)which may be calculated or determined by several methods.However,it is not practical technically or economically to apply for a well test or to deploy a permanent pressure gauge in the bottom hole to predict the BHP.Consequently,several correlations and mechanistic models based on the knownsurfacemeasurementshave beendeveloped.Unfortunately,all these tools(correlations&mechanistic models)are limited to some conditions and intervals of application.Therefore,establish a global model that ensures a large coverage of conditions with a reduced cost and high accuracy becomes a necessity.In this study,we propose new models for estimating bottom hole pressure of vertical wells with multiphase flow.First,Artificial Neural Network(ANN)based on back propagation training(BP-ANN)with 12 neurons in its hidden layer is established using trial and error.The next methods correspond to optimized or evolved neural networks(optimize the weights and thresholds of the neural networks)with Grey Wolves Optimization(GWO),and then its accuracy to reach the global optima is compared with 2 other naturally inspired algorithms which are the most used in the optimization field:Genetic Algorithm(GA)and Particle Swarms Optimization(PSO).The models were developed and tested using 100 field data collected from Algerian fields and covering a wide range of variables.The obtained results demonstrate the superiority of the hybridization ANN-GWO compared with the 2 other hybridizations or with the BP learning alone.Furthermore,the evolved neural networks with these global optimization algorithms are strongly shown to be highly effective to improve the performance of the neural networks to estimate flowing BHP over existing approaches and correlations.
文摘Purpose-Diabetic retinopathy(DR)is a central root of blindness all over the world.Though DR is tough to diagnose in starting stages,and the detection procedure might be time-consuming even for qualified experts.Nowadays,intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various diseases.Therefore,a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark dataset.Design/methodology/approach-The proposed DR diagnostic procedure involves four main steps:(1)image pre-processing,(2)blood vessel segmentation,(3)feature extraction,and(4)classification.Initially,the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization(CLAHE)and average filter.In the next step,the blood vessel segmentation is carried out using a segmentation process with optimized gray-level thresholding.Once the blood vessels are extracted,feature extraction is done,using Local Binary Pattern(LBP),Texture Energy Measurement(TEM based on Laws of Texture Energy),and two entropy computations-Shanon’s entropy,and Kapur’s entropy.These collected features are subjected to a classifier called Neural Network(NN)with an optimized training algorithm.Both the gray-level thresholding and NN is enhanced by the Modified Levy Updated-Dragonfly Algorithm(MLU-DA),which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the NN.Finally,this classification error can correctly prove the efficiency of the proposed DR detection model.Findings-The overall accuracy of the proposed MLU-DA was 16.6%superior to conventional classifiers,and the precision of the developed MLU-DA was 22%better than LM-NN,16.6%better than PSO-NN,GWO-NN,and DA-NN.Finally,it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting DR.Originality/value-This paper adopts the latest optimization algorithm called MLU-DA-Neural Network with optimal gray-level thresholding for detecting diabetic retinopathy disease.This is the first work utilizes MLU-DA-based Neural Network for computer-aided Diabetic Retinopathy diagnosis.
