A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and ...A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and the sort of matrix are the key controlling factors for its ablative performance.Then,a brief fuzzy mathe- matical relationship was established between these factors and ablative performance.Through experiments, the performance of the ANN was evaluated,which was used in the ablative performance prediction of C/C composites.When the training set,the structure and the training parameter of the net change,the best match ratio of these parameters was achieved.Based on the match ratio,this paper forecasts and evalu- ates the carbon-carbon ablation performance.Through experiences,the ablative performance prediction of carbon-carbon using ANN can achieve the line ablation rate,which satisfies the need of precision of practical engineering fields.展开更多
An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and ...An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.展开更多
To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resul...To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.展开更多
In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties ...In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).展开更多
This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather event...This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.展开更多
The effect of neutral pressure on the blue core in Ar helicon plasma under an inhomogeneous magnetic field was investigated in this work.The neutral pressure was set to 0.08 Pa,0.36 Pa,and 0.68 Pa.A Nikon camera,inten...The effect of neutral pressure on the blue core in Ar helicon plasma under an inhomogeneous magnetic field was investigated in this work.The neutral pressure was set to 0.08 Pa,0.36 Pa,and 0.68 Pa.A Nikon camera,intensified charge-coupled device(ICCD),optical emission spectrometer(OES),and Langmuir probe were used to diagnose the blue core in helicon plasma.Helicon plasma discharges experienced density jumps from the E mode,H mode to W mode before power just rose to 200 W.The plasma density increased and maintained a central peak with the increase of neutral pressure.However,the brightness of the blue core gradually decreased.It is demonstrated that the relative intensity of Ar II spectral lines and the ionization rate in the central area were reduced.Radial electron temperature profiles were flattened and became hollow as neutral pressure increased.It is demonstrated that increasing the neutral pressure weakened the central heating efficiency dominated by the helicon wave and strengthened the edge heating efficiency governed by the TG wave and skin effect.Therefore,the present experiment successfully reveals how the neutral pressure affects the heating mechanism of helicon plasma in an inhomogeneous magnetic field.展开更多
A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network(ANN),which replaces the governing equa...A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network(ANN),which replaces the governing equation of the intermittency factor in transition-predictive Spalart-Allmaras(SA)-γmodel.By taking SA-γmodel as the benchmark,the present ANN model is trained at two airfoils with various angles of attack,Mach numbers and Reynolds numbers,and tested with unseen airfoils in different flow states.The a posteriori tests manifest that the mean pressure coefficient,skin friction coefficient,size of laminar separation bubble,mean streamwise velocity,Reynolds shear stress and lift/drag/moment coefficient from the present two-way coupling ANN model almost coincide with those from the benchmark SA-γmodel.Furthermore,the ANN model proves to exhibit a higher calculation efficiency and better convergence quality than traditional SA-γmodel.展开更多
The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the...The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network(BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missiletarget intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuzewarhead coordination effect is less influenced by the location of the aim point.展开更多
Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a d...Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a delay-independent dynamical event-triggering controller is considered, operating the same way as the intermittent coupling and excluding the Zeno behavior naturally.展开更多
We propose to employ a hierarchical coarse-grained structure in artificial neural networks explicitly to improve the interpretability without degrading performance.The idea has been applied in two situations.One is a ...We propose to employ a hierarchical coarse-grained structure in artificial neural networks explicitly to improve the interpretability without degrading performance.The idea has been applied in two situations.One is a neural network called Taylor Net,which aims to approximate the general mapping from input data to output result in terms of Taylor series directly,without resorting to any magic nonlinear activations.The other is a new setup for data distillation,which can perform multi-level abstraction of the input dataset and generate new data that possesses the relevant features of the original dataset and can be used as references for classification.In both the cases,the coarse-grained structure plays an important role in simplifying the network and improving both the interpretability and efficiency.The validity has been demonstrated on MNIST and CIFAR-10 datasets.Further improvement and some open questions related are also discussed.展开更多
Recently,artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties.Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reli...Recently,artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties.Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of nondistributed weight updates owing to stable ion migrations.However,the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems.Meanwhile,achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging.Here,two-terminal Au/LixCoO_(2)/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks.The Au/LixCoO_(2)/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LixCoO_(2)films.The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike,resulting in improved weight control functionality.Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity,symmetricity,and dynamic range.Notably,the LixCoO_(2)-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error.These impressive performances suggest the vertical two-terminal Au/LixCoO_(2)/Pt artificial synapses as promising candidates for hardware neural networks.展开更多
Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and...Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.展开更多
This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different pola...This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.展开更多
Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existe...Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the Coinmarket.com website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.展开更多
Central nervous system abnormalities in fetuses are fairly common,happening in 0.1%to 0.2%of live births and in 3%to 6%of stillbirths.So initial detection and categorization of fetal Brain abnormalities are critical.M...Central nervous system abnormalities in fetuses are fairly common,happening in 0.1%to 0.2%of live births and in 3%to 6%of stillbirths.So initial detection and categorization of fetal Brain abnormalities are critical.Manually detecting and segmenting fetal brain magnetic resonance imaging(MRI)could be timeconsuming,and susceptible to interpreter experience.Artificial intelligence(AI)algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems,improving the diagnosis process and follow-up procedures.The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper.Using AI,anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically.All gestation age weeks(17-38 wk)and different AI models(mainly Convolutional Neural Network and U-Net)have been used.Some models'accuracy achieved 95%and more.AI could help preprocess and postprocess fetal images and reconstruct images.Also,AI can be used for gestational age prediction(with one-week accuracy),fetal brain extraction,fetal brain segmentation,and placenta detection.Some fetal brain linear measurements,such as Cerebral and Bone Biparietal Diameter,have been suggested.Classification of brain pathology was studied using diagonal quadratic discriminates analysis,Knearest neighbor,random forest,naive Bayes,and radial basis function neural network classifiers.Deep learning methods will become more powerful as more large-scale,labeled datasets become available.Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available.Also,physicians should be aware of AI's function in fetal brain MRI,particularly neuroradiologists,general radiologists,and perinatologists.展开更多
To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Eart...To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.展开更多
A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artif...A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artificial neural networks.With this newly developed wave function,variational Monte Carlo calculations were carried out for3H and3He nuclei starting from a nuclear Hamiltonian based on the leadingorder pionless effective field theory.The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hypersphericalharmonics method.The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and,thus,providing accurate ground-state energy.展开更多
Artificial intelligence in general and software agents in particular are recognized as computer science disciplines that aim to model or simulate so-called intelligent human behaviors such as perception, decision-maki...Artificial intelligence in general and software agents in particular are recognized as computer science disciplines that aim to model or simulate so-called intelligent human behaviors such as perception, decision-making, understanding, learning, etc. This work presents an approach to designing a generic Intelligent Agent that can be used in a multi-agent system to solve a complex problem. The generic agent that is proposed can be instantiated as a concrete agent, which is enabled with learning and autonomy capabilities by using Artificial Neural Networks. To highlight the generic aspect, the proposition is instantiated to be used in agriculture, health and education. The instantiated software agent applied in agriculture can process images in real time and detect defect on plants’ leaf. In the health field, the agent process image to diagnose breast cancer. When applied in Education, the agent can load an image of a student’s script and grade it. The performance of the designed agent system has the same accuracy as that of the respective neural networks used to instantiate them. In the educational field, the software agent has an accuracy of 98.9% and in the health field, it has an accuracy of 99.56% while in the agricultural field, it has an accuracy of 97.2%.展开更多
The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data...The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data traffic for Mobile Network Operators (MNOs) to handle. At the same time, MNOs are preparing for a paradigm shift to decouple the control and forwarding plane in a Software-Defined Networking (SDN) architecture. Artificial Intelligence powered Self-Organising Networks (AI-SON) can fit into the SDN architecture by providing prediction and recommender systems to minimise costs in supporting the MNO’s infrastructure. This paper presents a review report on AI-SON frameworks in 5G and SDN. The review considers the dynamic deployment and functions of the AI-SON frameworks, especially for SDN support and applications. Each module in the frameworks was discussed to ascertain its relevance based on the context of AI-SON and SDN integration. After examining each framework, the identified gaps are summarised as open issues for future works.展开更多
In recent decades,the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services.The doctors have switched to cloud co...In recent decades,the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services.The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations.This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases,electrocardiogram(ECG)reports,and payment details.The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)resources over the private and public cloud.The stored data are categorized as significant and non-significant by Artificial Neural Networks(ANN).The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer.Upon receiving the request from a secondary user,the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user.Once the key matches the key in the hidden layer,the preserved information will be shared between the users.Due to the enhanced privacy preserving key generation,the proposed work prevents the tracking of keys by malicious users.The outcomes reveal that the introduced work provides improved success rate with reduced computational time.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.10572044.
文摘A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and the sort of matrix are the key controlling factors for its ablative performance.Then,a brief fuzzy mathe- matical relationship was established between these factors and ablative performance.Through experiments, the performance of the ANN was evaluated,which was used in the ablative performance prediction of C/C composites.When the training set,the structure and the training parameter of the net change,the best match ratio of these parameters was achieved.Based on the match ratio,this paper forecasts and evalu- ates the carbon-carbon ablation performance.Through experiences,the ablative performance prediction of carbon-carbon using ANN can achieve the line ablation rate,which satisfies the need of precision of practical engineering fields.
基金the support of the National Natural Science Foundation of China(22278234,21776151)。
文摘An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.
基金This work was supported by the National Key R&D Program of China(Nos.2022YFF0709503,2022YFB1902700,2017YFC0602101)the Key Research and Development Program of Sichuan province(No.2023YFG0347)the Key Research and Development Program of Sichuan province(No.2020ZDZX0007).
文摘To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.
文摘In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).
文摘This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.
基金supported by National Natural Science Foundation of China(Nos.11505013 and 11875090)Beijing Municipal Natural Science Foundation(No.1192008)Beijing Municipal Commission of Education(Nos.KM202010015003,22150122029,and 202210015017)。
文摘The effect of neutral pressure on the blue core in Ar helicon plasma under an inhomogeneous magnetic field was investigated in this work.The neutral pressure was set to 0.08 Pa,0.36 Pa,and 0.68 Pa.A Nikon camera,intensified charge-coupled device(ICCD),optical emission spectrometer(OES),and Langmuir probe were used to diagnose the blue core in helicon plasma.Helicon plasma discharges experienced density jumps from the E mode,H mode to W mode before power just rose to 200 W.The plasma density increased and maintained a central peak with the increase of neutral pressure.However,the brightness of the blue core gradually decreased.It is demonstrated that the relative intensity of Ar II spectral lines and the ionization rate in the central area were reduced.Radial electron temperature profiles were flattened and became hollow as neutral pressure increased.It is demonstrated that increasing the neutral pressure weakened the central heating efficiency dominated by the helicon wave and strengthened the edge heating efficiency governed by the TG wave and skin effect.Therefore,the present experiment successfully reveals how the neutral pressure affects the heating mechanism of helicon plasma in an inhomogeneous magnetic field.
基金the financial supports provided by the National Natural Science Foundation of China(Nos.91852112 and 11988102)。
文摘A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network(ANN),which replaces the governing equation of the intermittency factor in transition-predictive Spalart-Allmaras(SA)-γmodel.By taking SA-γmodel as the benchmark,the present ANN model is trained at two airfoils with various angles of attack,Mach numbers and Reynolds numbers,and tested with unseen airfoils in different flow states.The a posteriori tests manifest that the mean pressure coefficient,skin friction coefficient,size of laminar separation bubble,mean streamwise velocity,Reynolds shear stress and lift/drag/moment coefficient from the present two-way coupling ANN model almost coincide with those from the benchmark SA-γmodel.Furthermore,the ANN model proves to exhibit a higher calculation efficiency and better convergence quality than traditional SA-γmodel.
文摘The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network(BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missiletarget intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuzewarhead coordination effect is less influenced by the location of the aim point.
基金supported by the National Natural Science Foundation of China (62173231, 61903252)Natural Science Foundation of Shanghai (21ZR444900)。
文摘Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a delay-independent dynamical event-triggering controller is considered, operating the same way as the intermittent coupling and excluding the Zeno behavior naturally.
基金supported by the National R&D Program of China(Grant Nos.2017YFA0302900 and 2016YFA0300503)the National Natural Science Foundation of China(Grant Nos.52176064,12274458,and 11774420)the Research Funds of Renmin University of China(Grant No.20XNLG19)。
文摘We propose to employ a hierarchical coarse-grained structure in artificial neural networks explicitly to improve the interpretability without degrading performance.The idea has been applied in two situations.One is a neural network called Taylor Net,which aims to approximate the general mapping from input data to output result in terms of Taylor series directly,without resorting to any magic nonlinear activations.The other is a new setup for data distillation,which can perform multi-level abstraction of the input dataset and generate new data that possesses the relevant features of the original dataset and can be used as references for classification.In both the cases,the coarse-grained structure plays an important role in simplifying the network and improving both the interpretability and efficiency.The validity has been demonstrated on MNIST and CIFAR-10 datasets.Further improvement and some open questions related are also discussed.
基金financially supported by National R&D Program(2018M3D1A1058793,2021M3H4A3A02086430)through NRF(National Research Foundation of Korea)funded by the Ministry of Science and ICTsupported by SAIT,Samsung Electronics Co.,Ltd。
文摘Recently,artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties.Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of nondistributed weight updates owing to stable ion migrations.However,the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems.Meanwhile,achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging.Here,two-terminal Au/LixCoO_(2)/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks.The Au/LixCoO_(2)/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LixCoO_(2)films.The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike,resulting in improved weight control functionality.Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity,symmetricity,and dynamic range.Notably,the LixCoO_(2)-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error.These impressive performances suggest the vertical two-terminal Au/LixCoO_(2)/Pt artificial synapses as promising candidates for hardware neural networks.
文摘Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.
基金Funding was provided by the National Key Research and Development Program of China(Grant No.2021ZD0200300)National Natural Science Foundation of China(NSFC)(U2031138).
文摘This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.
基金Financial support.There is no sponsorship.The publication of study results is not contingent on the sponsor’s approval or censorship of the manuscript.
文摘Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the Coinmarket.com website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.
基金Supported by Colonel Robert R McCormick Professorship of Diagnostic Imaging Fund at Rush University Medical Center(The Activity Number is 1233-161-84),No.8410152-03.
文摘Central nervous system abnormalities in fetuses are fairly common,happening in 0.1%to 0.2%of live births and in 3%to 6%of stillbirths.So initial detection and categorization of fetal Brain abnormalities are critical.Manually detecting and segmenting fetal brain magnetic resonance imaging(MRI)could be timeconsuming,and susceptible to interpreter experience.Artificial intelligence(AI)algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems,improving the diagnosis process and follow-up procedures.The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper.Using AI,anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically.All gestation age weeks(17-38 wk)and different AI models(mainly Convolutional Neural Network and U-Net)have been used.Some models'accuracy achieved 95%and more.AI could help preprocess and postprocess fetal images and reconstruct images.Also,AI can be used for gestational age prediction(with one-week accuracy),fetal brain extraction,fetal brain segmentation,and placenta detection.Some fetal brain linear measurements,such as Cerebral and Bone Biparietal Diameter,have been suggested.Classification of brain pathology was studied using diagonal quadratic discriminates analysis,Knearest neighbor,random forest,naive Bayes,and radial basis function neural network classifiers.Deep learning methods will become more powerful as more large-scale,labeled datasets become available.Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available.Also,physicians should be aware of AI's function in fetal brain MRI,particularly neuroradiologists,general radiologists,and perinatologists.
基金China Energy Engineering Group Planning&Engineering Co.,Ltd.Concentrated Development Scientific Research Project Under Grant No.GSKJ2-T11-2019。
文摘To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.
基金Supported by National Key R&D Program of China (018YFA0404400)National Natural Science Foundation of China (12070131001,11875075,11935003,11975031,12141501)。
文摘A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artificial neural networks.With this newly developed wave function,variational Monte Carlo calculations were carried out for3H and3He nuclei starting from a nuclear Hamiltonian based on the leadingorder pionless effective field theory.The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hypersphericalharmonics method.The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and,thus,providing accurate ground-state energy.
文摘Artificial intelligence in general and software agents in particular are recognized as computer science disciplines that aim to model or simulate so-called intelligent human behaviors such as perception, decision-making, understanding, learning, etc. This work presents an approach to designing a generic Intelligent Agent that can be used in a multi-agent system to solve a complex problem. The generic agent that is proposed can be instantiated as a concrete agent, which is enabled with learning and autonomy capabilities by using Artificial Neural Networks. To highlight the generic aspect, the proposition is instantiated to be used in agriculture, health and education. The instantiated software agent applied in agriculture can process images in real time and detect defect on plants’ leaf. In the health field, the agent process image to diagnose breast cancer. When applied in Education, the agent can load an image of a student’s script and grade it. The performance of the designed agent system has the same accuracy as that of the respective neural networks used to instantiate them. In the educational field, the software agent has an accuracy of 98.9% and in the health field, it has an accuracy of 99.56% while in the agricultural field, it has an accuracy of 97.2%.
文摘The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data traffic for Mobile Network Operators (MNOs) to handle. At the same time, MNOs are preparing for a paradigm shift to decouple the control and forwarding plane in a Software-Defined Networking (SDN) architecture. Artificial Intelligence powered Self-Organising Networks (AI-SON) can fit into the SDN architecture by providing prediction and recommender systems to minimise costs in supporting the MNO’s infrastructure. This paper presents a review report on AI-SON frameworks in 5G and SDN. The review considers the dynamic deployment and functions of the AI-SON frameworks, especially for SDN support and applications. Each module in the frameworks was discussed to ascertain its relevance based on the context of AI-SON and SDN integration. After examining each framework, the identified gaps are summarised as open issues for future works.
文摘In recent decades,the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services.The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations.This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases,electrocardiogram(ECG)reports,and payment details.The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)resources over the private and public cloud.The stored data are categorized as significant and non-significant by Artificial Neural Networks(ANN).The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer.Upon receiving the request from a secondary user,the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user.Once the key matches the key in the hidden layer,the preserved information will be shared between the users.Due to the enhanced privacy preserving key generation,the proposed work prevents the tracking of keys by malicious users.The outcomes reveal that the introduced work provides improved success rate with reduced computational time.
