Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development ...Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.展开更多
This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests.Establishing failure c...This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests.Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation.The proposed method can overcome such practical challenges.The methodology is formalized by combining four ideas:1)The deep learning neural network(DLNN)-based material constitutive model,2)Self-learning inverse finite element(SELIFE)simulation,3)Algorithmic identification of failure points from the selflearned stress-strain curves and 4)Derivation of the failure criteria through symbolic regression of the genetic programming.Stress update and the algorithmic tangent operator were formulated in terms of DLNN parameters for nonlinear finite element analysis.Then,the SELIFE simulation algorithm gradually makes the DLNN model learn highly complex multi-axial stress and strain relationships,being guided by the experimental boundary measurements.Following the failure point identification,a self-learning data-driven failure criteria are eventually developed with the help of a reliable symbolic regression algorithm.The methodology and the self-learning data-driven failure criteria were verified by comparing with a reference failure criteria and simulating with different materials orientations,respectively.展开更多
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of...With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.展开更多
Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated w...Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.展开更多
A new algorithm to exploit the learning rates of gradient descent method is presented, based on the second-order Taylor expansion of the error energy function with respect to learning rate, at some values decided by &...A new algorithm to exploit the learning rates of gradient descent method is presented, based on the second-order Taylor expansion of the error energy function with respect to learning rate, at some values decided by "award-punish" strategy. Detailed deduction of the algorithm applied to RBF networks is given. Simulation studies show that this algorithm can increase the rate of convergence and improve the performance of the gradient descent method.展开更多
Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing ...Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement characteristics.First,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight MPs.We used the softmax classifier to obtain the behavior-movement hierarchical probability model.Secondly,we specified the MPs using movement parameters that are static and dynamic.We obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,respectively.These predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden layers.Finally,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,respectively.We implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.展开更多
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomi...Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomic arrangement make this an essential task in the development of new materials.We present a method that efficiently uses active learning of neural network force fields for structure relaxation,minimizing the required number of steps in the process.This is achieved by neural network force fields equipped with uncertainty estimation,which iteratively guide a pool of randomly generated candidates toward their respective local minima.Using this approach,we are able to effectively identify themost promising candidates for further evaluation using density functional theory(DFT).Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systemsSi_(16),Na_(8)Cl_(8),Ga_(8)As_(8)and Al_(4)O_(6)but also excels in finding themost stable minimum for the unseen,more complex systems Si46 and Al16O24.Moreover,we demonstrate at the example of Si_(16)that our method can find multiple relevant local minima while only adding minor computational effort.展开更多
Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of...Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.展开更多
We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines m...We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines multiple modules including application terminal interface,docker container,data visualization,SSH protocol data transmission and other auxiliary modules.Characterized by a series of technologically powerful functions,the software is highly convenient for all users.To obtain the P-and S-wave picks,one only needs to prepare threecomponent seismic data as input and customize some parameters in the interface.In particular,the software can automatically identify complex waveforms(i.e.continuous or truncated waves)and support multiple types of input data such as SAC,MSEED,NumPy array,etc.A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software.The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data,thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.展开更多
The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Base...The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.展开更多
The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analys...The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bambara-French Facebook comments. We develop four Long Short-term Memory（LSTM）-based models and two Convolutional Neural Network（CNN）-based models, and use these six models, Na？ve Bayes, and Support Vector Machines（SVM） to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %.展开更多
Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil...Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.展开更多
In this paper,a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios.It is designed further to make their transmission decision th...In this paper,a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios.It is designed further to make their transmission decision that automati-cally adapts to the transmission dynamics to mitigate the launched jamming attacks.The generative adversarial learning neural network(GALNN)or genera-tive dynamic neural network(GDNN)automatically learns with the synthesized training data(training)with a generator and discriminator type neural networks that encompass minimax game theory.The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network(GAN).The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm,which improves the communica-tion reliability in the network.The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates.The results show that the GDNN obtains an increased rate of successful transmis-sion by taking optimal actions to act as a defense mechanism to mislead the jam-mer,where the jammer makes high misclassification errors on transmission dynamics.展开更多
Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids ...Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics.There are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation etc.There are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction etc.To overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved BAT optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral features.In HDIB,we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)image.Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology.Then,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of classification.Finally,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.展开更多
Distributed denial-of-service(DDoS)attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks.Furthermore,the enormous number of connected devices makes it d...Distributed denial-of-service(DDoS)attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks.Furthermore,the enormous number of connected devices makes it difficult to operate such a network effectively.Software defined networks(SDN)are networks that are managed through a centralized control system,according to researchers.This controller is the brain of any SDN,composing the forwarding table of all data plane network switches.Despite the advantages of SDN controllers,DDoS attacks are easier to perpetrate than on traditional networks.Because the controller is a single point of failure,if it fails,the entire network will fail.This paper offers a Hybrid Deep Learning Intrusion Detection and Prevention(HDLIDP)framework,which blends signature-based and deep learning neural networks to detect and prevent intrusions.This framework improves detection accuracy while addressing all of the aforementioned problems.To validate the framework,experiments are done on both traditional and SDN datasets;the findings demonstrate a significant improvement in classification accuracy.展开更多
In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independ...In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their interrelation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of adjustable weights) is appropriately limited in order to speed up the learning and ensure small errors. Numerical computer simulation results are provided to support the present approaches.展开更多
The increasing intensity of strong earthquakes has a large impact on the seismic safety of bridges worldwide.As the key component in the transportation network,the cable-stayed bridge should cope with the increasing f...The increasing intensity of strong earthquakes has a large impact on the seismic safety of bridges worldwide.As the key component in the transportation network,the cable-stayed bridge should cope with the increasing future hazards to improve seismic safety.Seismic fragility analysis can assist the resilience assessment under different levels of seismic intensity.However,such an analysis is computationally intensive,especially when considering various random factors.The present paper implemented the deep learning neural networks that are integrated into the performance-based earthquake engineering framework to predict fragility functions and associated resilience index of cable-stayed bridges under seismic hazards to improve the computational efficiency while having sufficient accuracy.In the proposed framework,the Latin hypercube sampling was improved with additional uniformity to enhance the training process of the neural network.The well-trained neural network was then applied in a probabilistic simulation process to derive different component fragilities of the cable-stayed bridge.The estimated fragility functions were combined with the Monte Carlo simulations to predict system resilience.The proposed integrated framework in this study was demonstrated on an existing single-pylon cable-stayed bridge in China.Results reveal that this integrated framework yields accurate predictions of fragility functions for the cable-stayed bridge and has reasonable accuracy compared with the conventional methods.展开更多
Relic gravitational waves(RGWs)from the early Universe carry crucial and fundamental cosmological information.Therefore,it is of extraordinary importance to investigate potential RGW signals in the data from observato...Relic gravitational waves(RGWs)from the early Universe carry crucial and fundamental cosmological information.Therefore,it is of extraordinary importance to investigate potential RGW signals in the data from observatories such as the LIGO-Virgo-KAGRA network.Here,focusing on typical RGWs from the inflation and the first-order phase transition(by sound waves and bubble collisions),effective and targeted deep learning neural networks are established to search for these RGW signals within the real LIGO data(O2,O3a and O3b).Through adjustment and adaptation processes,we develop suitable Convolutional Neural Networks(CNNs)to estimate the likelihood(characterized by quantitative values and distributions)that the focused RGW signals are present in the LIGO data.We find that if the constructed CNN properly estimates the parameters of the RGWs,it can determine with high accuracy(approximately 94%to 99%)whether the samples contain such RGW signals;otherwise,the likelihood provided by the CNN cannot be considered reliable.After testing a large amount of LIGO data,the findings show no evidence of RGWs from:1)inflation,2)sound waves,or 3)bubble collisions,as predicted by the focused theories.The results also provide upper limits of their GW spectral energy densities of h^(2)Ω_(gw)~10^(-5),respectively for parameter boundaries within 1)[β∈(-1.87,-1.85)×α∈(0.005,0.007)],2)[β/H_(pt)∈(0.02,0.16)×α∈(1,10)×T_(pt)∈(5*10^(9),10^(10))Gev],and 3)[β/H_(pt)∈(0.08,0.2)×α∈(1,10)×T_(pt)∈(5*10^(9),8*10^(10))Gev].In short,null results and upper limits are obtained,and the analysis suggests that our developed methods and neural networks to search for typical RGWs in the LIGO data are effective and reliable,providing a viable scheme for exploring possible RGWs from the early Universe and placing constraints on relevant cosmological theories.展开更多
With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently...With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.展开更多
基金Project (No. 60721062) supported by the National Creative Research Groups Science Foundation of China
文摘Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
基金the National Research Foundation of Korea(NRF)grant of the Korea government(MSIP)(2020R1A2B5B01001899)(Grantee:GJY,http://www.nrf.re.kr)and Institute of Engineering Research at Seoul National University(Grantee:GJY,http://www.snu.ac.kr).The authors are grateful for their supports.
文摘This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests.Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation.The proposed method can overcome such practical challenges.The methodology is formalized by combining four ideas:1)The deep learning neural network(DLNN)-based material constitutive model,2)Self-learning inverse finite element(SELIFE)simulation,3)Algorithmic identification of failure points from the selflearned stress-strain curves and 4)Derivation of the failure criteria through symbolic regression of the genetic programming.Stress update and the algorithmic tangent operator were formulated in terms of DLNN parameters for nonlinear finite element analysis.Then,the SELIFE simulation algorithm gradually makes the DLNN model learn highly complex multi-axial stress and strain relationships,being guided by the experimental boundary measurements.Following the failure point identification,a self-learning data-driven failure criteria are eventually developed with the help of a reliable symbolic regression algorithm.The methodology and the self-learning data-driven failure criteria were verified by comparing with a reference failure criteria and simulating with different materials orientations,respectively.
文摘Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
基金This work was partially funded by the Industry Grant Scheme from Jaycorp Berhad in cooperation with UNITAR International University.The authors would like to thank INSFORNET,the Center for Advanced Computing Technology(C-ACT)at Universiti Teknikal Malaysia Melaka(UTeM),and the Center of Intelligent and Autonomous Systems(CIAS)at Universiti Tun Hussein Onn Malaysia(UTHM)for supporting this work.
文摘With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.
基金funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED)under Grant No.105.08-2019.03.
文摘Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.
基金Open Foundation of State Key Lab of Transmission of Wide-Band FiberTechnologies of Communication Systems
文摘A new algorithm to exploit the learning rates of gradient descent method is presented, based on the second-order Taylor expansion of the error energy function with respect to learning rate, at some values decided by "award-punish" strategy. Detailed deduction of the algorithm applied to RBF networks is given. Simulation studies show that this algorithm can increase the rate of convergence and improve the performance of the gradient descent method.
基金supported in part by the National Natural Science Foundation of China(62022014)in part by the National Key Research and Development Program of China(2017YFE0117000)。
文摘Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these MPs.To mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement characteristics.First,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight MPs.We used the softmax classifier to obtain the behavior-movement hierarchical probability model.Secondly,we specified the MPs using movement parameters that are static and dynamic.We obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,respectively.These predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden layers.Finally,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,respectively.We implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.
基金N.W.A.G.and M.G.contributed to this research while working at the BASLEARN-TU Berlin/BASF Joint Lab for Machine Learning,co-financed by TU Berlin and BASF SE.K.T.S.contributed to this research while working at TU Berlin and BIFOLD with grant number 01IS18037Asupported by JSPS KAKENHI Grant Number JP23H05457 and by JST-CREST Grant Number JPMJCR22O2.We thank Jonas Lederer and Klaus-Robert Müller for insightful discussions and feedback.
文摘Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomic arrangement make this an essential task in the development of new materials.We present a method that efficiently uses active learning of neural network force fields for structure relaxation,minimizing the required number of steps in the process.This is achieved by neural network force fields equipped with uncertainty estimation,which iteratively guide a pool of randomly generated candidates toward their respective local minima.Using this approach,we are able to effectively identify themost promising candidates for further evaluation using density functional theory(DFT).Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systemsSi_(16),Na_(8)Cl_(8),Ga_(8)As_(8)and Al_(4)O_(6)but also excels in finding themost stable minimum for the unseen,more complex systems Si46 and Al16O24.Moreover,we demonstrate at the example of Si_(16)that our method can find multiple relevant local minima while only adding minor computational effort.
基金supported by two Ministry of Education(MoE)Singapore Tier 1 research grants under grant numbers R-296-000-208-133 and R-296-000-241-114.
文摘Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.
基金This study is jointly sponsored by the Basic Scientific Research Fee of Institute of Geophysics,China Earthquake Administration(DQJB19A0114)the National Natural Science Foundation of China(41804047).
文摘We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines multiple modules including application terminal interface,docker container,data visualization,SSH protocol data transmission and other auxiliary modules.Characterized by a series of technologically powerful functions,the software is highly convenient for all users.To obtain the P-and S-wave picks,one only needs to prepare threecomponent seismic data as input and customize some parameters in the interface.In particular,the software can automatically identify complex waveforms(i.e.continuous or truncated waves)and support multiple types of input data such as SAC,MSEED,NumPy array,etc.A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software.The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data,thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.
文摘The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.
基金Supported by the National Natural Science Foundation of China(61272451,61572380,61772383 and 61702379)the Major State Basic Research Development Program of China(2014CB340600)
文摘The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bambara-French Facebook comments. We develop four Long Short-term Memory（LSTM）-based models and two Convolutional Neural Network（CNN）-based models, and use these six models, Na？ve Bayes, and Support Vector Machines（SVM） to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %.
基金supported by CITRIS and the Banatao Institute,Air Force Office of Scientific Research(Grant No.FA9550-22-1-0420)National Science Foundation(Grant No.ACI-1548562).
文摘Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.
文摘In this paper,a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios.It is designed further to make their transmission decision that automati-cally adapts to the transmission dynamics to mitigate the launched jamming attacks.The generative adversarial learning neural network(GALNN)or genera-tive dynamic neural network(GDNN)automatically learns with the synthesized training data(training)with a generator and discriminator type neural networks that encompass minimax game theory.The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network(GAN).The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm,which improves the communica-tion reliability in the network.The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates.The results show that the GDNN obtains an increased rate of successful transmis-sion by taking optimal actions to act as a defense mechanism to mislead the jam-mer,where the jammer makes high misclassification errors on transmission dynamics.
文摘Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics.There are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation etc.There are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction etc.To overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved BAT optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral features.In HDIB,we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)image.Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology.Then,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of classification.Finally,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.
文摘Distributed denial-of-service(DDoS)attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks.Furthermore,the enormous number of connected devices makes it difficult to operate such a network effectively.Software defined networks(SDN)are networks that are managed through a centralized control system,according to researchers.This controller is the brain of any SDN,composing the forwarding table of all data plane network switches.Despite the advantages of SDN controllers,DDoS attacks are easier to perpetrate than on traditional networks.Because the controller is a single point of failure,if it fails,the entire network will fail.This paper offers a Hybrid Deep Learning Intrusion Detection and Prevention(HDLIDP)framework,which blends signature-based and deep learning neural networks to detect and prevent intrusions.This framework improves detection accuracy while addressing all of the aforementioned problems.To validate the framework,experiments are done on both traditional and SDN datasets;the findings demonstrate a significant improvement in classification accuracy.
文摘In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their interrelation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of adjustable weights) is appropriately limited in order to speed up the learning and ensure small errors. Numerical computer simulation results are provided to support the present approaches.
基金supported by the National Natural Science Foundation of China (Grant No.51708527)the R&D Project of China Railway Siyuan Survey and Design Institute Group Co.,Ltd. (Grant No.2020k172)。
文摘The increasing intensity of strong earthquakes has a large impact on the seismic safety of bridges worldwide.As the key component in the transportation network,the cable-stayed bridge should cope with the increasing future hazards to improve seismic safety.Seismic fragility analysis can assist the resilience assessment under different levels of seismic intensity.However,such an analysis is computationally intensive,especially when considering various random factors.The present paper implemented the deep learning neural networks that are integrated into the performance-based earthquake engineering framework to predict fragility functions and associated resilience index of cable-stayed bridges under seismic hazards to improve the computational efficiency while having sufficient accuracy.In the proposed framework,the Latin hypercube sampling was improved with additional uniformity to enhance the training process of the neural network.The well-trained neural network was then applied in a probabilistic simulation process to derive different component fragilities of the cable-stayed bridge.The estimated fragility functions were combined with the Monte Carlo simulations to predict system resilience.The proposed integrated framework in this study was demonstrated on an existing single-pylon cable-stayed bridge in China.Results reveal that this integrated framework yields accurate predictions of fragility functions for the cable-stayed bridge and has reasonable accuracy compared with the conventional methods.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.11605015,12347101 and 12147102the Natural Scienceof Chongqing under Grant No.cstc2020jcyjmsxm X0944the Research Funds for the Central Universities under Grant No.2022CDJXY-002。
文摘Relic gravitational waves(RGWs)from the early Universe carry crucial and fundamental cosmological information.Therefore,it is of extraordinary importance to investigate potential RGW signals in the data from observatories such as the LIGO-Virgo-KAGRA network.Here,focusing on typical RGWs from the inflation and the first-order phase transition(by sound waves and bubble collisions),effective and targeted deep learning neural networks are established to search for these RGW signals within the real LIGO data(O2,O3a and O3b).Through adjustment and adaptation processes,we develop suitable Convolutional Neural Networks(CNNs)to estimate the likelihood(characterized by quantitative values and distributions)that the focused RGW signals are present in the LIGO data.We find that if the constructed CNN properly estimates the parameters of the RGWs,it can determine with high accuracy(approximately 94%to 99%)whether the samples contain such RGW signals;otherwise,the likelihood provided by the CNN cannot be considered reliable.After testing a large amount of LIGO data,the findings show no evidence of RGWs from:1)inflation,2)sound waves,or 3)bubble collisions,as predicted by the focused theories.The results also provide upper limits of their GW spectral energy densities of h^(2)Ω_(gw)~10^(-5),respectively for parameter boundaries within 1)[β∈(-1.87,-1.85)×α∈(0.005,0.007)],2)[β/H_(pt)∈(0.02,0.16)×α∈(1,10)×T_(pt)∈(5*10^(9),10^(10))Gev],and 3)[β/H_(pt)∈(0.08,0.2)×α∈(1,10)×T_(pt)∈(5*10^(9),8*10^(10))Gev].In short,null results and upper limits are obtained,and the analysis suggests that our developed methods and neural networks to search for typical RGWs in the LIGO data are effective and reliable,providing a viable scheme for exploring possible RGWs from the early Universe and placing constraints on relevant cosmological theories.
基金This work was partially supported by the Na- tional High Technology Research and Development Program of China （2011AA010502）, the National Natural Science Foundation of China （Grant No. 61103095）, and the Fundamental Research Funds for the Central Uni- versifies. We are grateful to Shenzhen Key Laboratory of Data Vitalization （Smart City） for supporting this research.
文摘With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.
