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deep neural network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

Intelligent Automation & Soft Computing 2023年第11期38卷 185-202页

作者： Hongshang Xu Bei Dong Xiaochang Liu Xiaojun Wu School of Computer Science Shaanxi Normal UniversityXi’an710119China Key Laboratory of Modern Teaching Technology Ministry of EducationXi’an710062China

deep neural networks often outperform classical machine learning algorithms in solving real-world ***,designing better networks usually requires domain expertise and consumes significant time and com-puting ***,when the task changes,the original network architecture becomes outdated and requires ***,neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network *** NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively ***,complex network structures will bring enormous computing *** cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters *** then a decomposition-based multi-objective stochastic fractal search method is proposed to solve *** view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more ***,two distinct update methods are employed in step size update stage to enhance the global and local search abilities ***,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the *** studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

关键词： deep neural network neural architecture search multi-objective optimization stochastic fractal search decomposition

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Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

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Chinese Physics B 2022年第6期31卷 470-475页

作者：孟海洋徐自翔杨京梁彬程建春 Key Laboratory of Modern Acoustics MOEInstitute of AcousticsDepartment of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China

Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and *** conventional prediction methods based on numerical simulation often demand huge computational resources,which are difficult to balance between accuracy and ***,we present a data-driven deep neural network(DNN)method to realize fast aerodynamic noise prediction while maintaining *** proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working *** on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory,a dataset composed of 1216samples is *** reference to the deep learning method,a DNN framework is proposed to map the relationship between spatial coordinates,inlet velocity and overall sound pressure *** root-mean-square-errors of prediction are below 0.82 dB in the test dataset,and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical *** work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.

关键词： aerodynamic noise prediction deep neural network aeroacoustics

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A point cloud deep neural network metamodel method for aerodynamic prediction

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Chinese Journal of Aeronautics 2023年第4期36卷 92-103页

作者： Fenfen XIONG Li ZHANG Xiao HU Chengkun REN School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Department of Mechanical Engineering Imperial College LondonLondon SW72AZUK

Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this *** 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet *** network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical *** the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between ***,the point clouds are easily accessible from 3D *** point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D *** effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 *** results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.

关键词： Aerodynamic prediction deep neural network Metamodel Point clouds Robust shape optimization

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Improve Robustness and Accuracy of deep neural network with L_(2,∞) Normalization

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Journal of Systems Science & Complexity 2023年第1期36卷 3-28页

作者： YU Lijia GAO Xiao-Shan Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

In this paper,the L_(2,∞)normalization of the weight matrices is used to enhance the robustness and accuracy of the deep neural network(DNN)with Relu as activation *** is shown that the L_(2,∞)normalization leads to large dihedral angles between two adjacent faces of the DNN function graph and hence smoother DNN functions,which reduces over-fitting of the DNN.A global measure is proposed for the robustness of a classification DNN,which is the average radius of the maximal robust spheres with the training samples as centers.A lower bound for the robustness measure in terms of the L_(2,∞)norm is ***,an upper bound for the Rademacher complexity of DNNs with L_(2,∞)normalization is *** algorithm is given to train DNNs with the L_(2,∞)normalization and numerical experimental results are used to show that the L_(2,∞)normalization is effective in terms of improving the robustness and accuracy.

关键词： deep neural network global robustness measure L_(2,∞)normalization over-fitting Rademacher complexity smooth DNN

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Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

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International Journal of Minerals,Metallurgy and Materials 2023年第2期30卷 335-344页

作者： Zicheng Xin Jiangshan Zhang Yu Jin Jin Zheng Qing Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Tangshan Branch Hebei Iron and Steel Co.Ltd.Tangshan 063000China

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining *** this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN *** prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN *** results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN ***,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten *** construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

关键词： ladle furnace element yield principal component analysis deep neural network statistical evaluation

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FlowDNN:a physics-informed deep neural network for fast and accurate flow prediction

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Frontiers of Information Technology & Electronic Engineering 2022年第2期23卷 207-219页

作者： Donglin CHEN Xiang GAO Chuanfu XU Siqi WANG Shizhao CHEN Jianbin FANG Zheng WANG College of Computer National University of Defense TechnologyChangsha 410073China State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China School of Computing University of LeedsLeeds LS29JTUK

For flow-related design optimization problems,e.g.,aircraft and automobile aerodynamic design,computational fluid dynamics(CFD)simulations are commonly used to predict flow fields and analyze *** important,CFD simulations are a resource-demanding and time-consuming iterative *** expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive *** this paper,we propose Flow DNN,a novel deep neural network(DNN)to efficiently learn flow representations from CFD *** DNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry *** DNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow *** approach not only improves the prediction accuracy,but also preserves the physical consistency of the predicted flow fields,which is essential for *** metrics are derived to evaluate Flow DNN with respect to the whole flow fields or regions of interest(RoIs)(e.g.,boundary layers where flow quantities change rapidly).Experiments show that Flow DNN significantly outperforms alternative methods with faster inference and more accurate *** speeds up a graphics processing unit(GPU)accelerated CFD solver by more than 14000×,while keeping the prediction error under 5%.

关键词： deep neural network Flow prediction Attention mechanism Physics-informed loss

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Evaluation of hydraulic fracturing of horizontal wells in tight reservoirs based on the deep neural network with physical constraints

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Petroleum Science 2023年第2期20卷 1129-1141页

作者： Hong-Yan Qu Jian-Long Zhang Fu-Jian Zhou Yan Peng Zhe-Jun Pan Xin-Yao Wu State Key Laboratory of Petroleum Resources and Prospecting China University of PetroleumBeijing102249China School of Artificial Intelligence China University of PetroleumBeijing102249China Unconventional Oil and Gas Institute China University of PetroleumBeijing102249China School of Petroleum Engineering China University of PetroleumBeijing102249China Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development Ministry of EducationNortheast Petroleum UniversityDaqingHeilongjiang163318China Engineering Technology Research Institute PetroChina Xinjiang Oilfield CompanyKaramayXinjiang834000China

Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.

关键词： Evaluation of fracturing effects Tight reservoirs Physical constraints deep neural network Horizontal wells Combined neural network

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DNNBoT: deep neural network-Based Botnet Detection and Classification

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Computers, Materials & Continua 2022年第4期71卷 1729-1750页

作者： Mohd Anul Haq Mohd Abdul Rahim Khan Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah 11952Saudi Arabia

The evolution and expansion of IoT devices reduced human efforts,increased resource utilization, and saved time;however, IoT devices createsignificant challenges such as lack of security and privacy, making them morevulnerable to IoT-based botnet attacks. There is a need to develop efficientand faster models which can work in real-time with efficiency and stability. The present investigation developed two novels, deep neural network(DNN) models, DNNBoT1 and DNNBoT2, to detect and classify well-knownIoT botnet attacks such as Mirai and BASHLITE from nine compromisedindustrial-grade IoT devices. The utilization of PCA was made to featureextraction and improve effectual and accurate Botnet classification in IoTenvironments. The models were designed based on rigorous hyperparameterstuning with GridsearchCV. Early stopping was utilized to avoid the effects ofoverfitting and underfitting for both DNN models. The in-depth assessmentand evaluation of the developed models demonstrated that accuracy andefficiency are some of the best-performed models. The novelty of the presentinvestigation, with developed models, bridge the gaps by using a real datasetwith high accuracy and a significantly lower false alarm rate. The results wereevaluated based on earlier studies and deemed efficient at detecting botnetattacks using the real dataset.

关键词： Botnet network monitoring machine learning deep neural network IoT threat

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A Convolutional and Transformer Based deep neural network for Automatic Modulation Classification

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China Communications 2023年第5期20卷 135-147页

作者： Shanchuan Ying Sai Huang Shuo Chang Zheng Yang Zhiyong Feng Ningyan Guo Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China

Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil *** this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification *** can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final *** the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential *** the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN *** the transformer module,three self-attention layers are designed for extracting global features and generating the classification *** the classifier,the final decision is made based on the maximum a posterior *** simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.

关键词： automatic modulation classification deep neural network convolutional neural network transformer

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State-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using deep neural network

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Computers, Materials & Continua 2022年第12期73卷 6289-6306页

作者： M.Premkumar R.Sowmya S.Sridhar C.Kumar Mohamed Abbas Malak S.Alqahtani Kottakkaran Sooppy Nisar Department of Electrical and Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Department of Electrical and Electronics Engineering National Institute of TechnologyTiruchirapalli620015Tamil NaduIndia Department of Electrical and Electronics Engineering M S Ramaiah Institute of TechnologyBengaluru560054KarnatakaIndia Department of Electrical and Electronics Engineering M.Kumarasamy College of EngineeringKarur639113Tamil NaduIndia Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Computers and Communications Department College of EngineeringDelta University for Science and TechnologyGamasa35712Egypt Faculty of Technology The GatewayDe Montfort UniversityLeicesterLE19BHUnited Kingdom Department of Mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric *** strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been *** a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall *** a result of aging,the variation in the parameters of each cell is higher as more time is spent in *** is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced deep neural network(DNN)*** is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC *** evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is *** current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing *** the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their *** simulation has been conducted for two different Li-ion battery *** to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.

关键词： Artificial intelligence deep neural network Li-ion battery parameter variation SoC estimation

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