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检索条件"任意字段=Conference on Nonlinear Image Processing XI"

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Prediction and Modeling of Reservoir Properties in Ultra-Low Permeability Reservoirs with Edge and Bottom Water Based on Deep Learning

Prediction and Modeling of Reservoir Properties in Ultra-Low...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Jieyou Yang Hong Liu Feilong Liu Huan Yang Qiu Chen Dongmei Han The Fifth Oil Production Plant of Petrochina Changqing Oilfield Company Xi'an Shaanxi China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The reservoir properties of reservoirs in edge and bottom water reservoirs have complex and nonlinear characteristics, and traditional prediction methods have low accuracy and cannot accurately predict them. This study adopted a CNN algorithm based method for predicting the reservoir properties of reservoirs in edge and bottom water reservoirs. Firstly, a large amount of reservoir data was collected and prepared, including physical properties and geological features. Then, CNN models were used for feature learning and predictive modeling. The model performed deep learning and feature extraction on the data through convolutional and pooling layers, and predicted attributes through fully connected layers. The experimental results showed that the prediction accuracy range of CNN was 95%-99%. The prediction model has achieved significant results in predicting reservoir properties in reservoirs with edge and bottom water. It can accurately predict reservoir properties and achieve lower error rates in both positive and negative class predictions.

关键词： Water Deep learning Accuracy Geology Predictive models Reservoirs Prediction algorithms Data models Permeability Convolutional neural networks

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State Identification and Prediction of nonlinear Dynamic Systems Based on Mixed Spiking Neural Networks

State Identification and Prediction of Nonlinear Dynamic Sys...

引用

IEEE Joint International Information Technology and Artificial Intelligence conference (ITAIC)

作者： Bowen Wu xiaoyang Wang xinyu Zhang Yujia Yu School of Mathematics and Statistics Northwestern Poly technical University Xi'an Shaanxi Province China

nonlinear dynamical systems exist widely in nature and engineering fields, and the study of their dynamic behavior characteristics such as oscillation, chaos and bifurcation is still a hot topic in academic circles. The chaotic state of nonlinear dynamic system can be judged by images. Dynamics problems usually involve the interaction of multiple variables and complex nonlinear relationships, which may not be well dealt with by traditional statistical methods. The impulsive neural network and brain-like intelligence have the advantages of self-adaptability, nonlinear processing ability and parallel computing ability, and can deal with these complex dynamic problems better. In this paper, a mixed spiking neural network is adopted to classify dynamic system images, and an image-based mixed spiking neural network classification and prediction model is established. The performance of the model is analyzed by using nonlinear dynamic system cases, and the model generation parameters are modified to realize the classification and prediction of nonlinear dynamic system. This scheme has the characteristics of low complexity and strong anti-noise ability, and can play an important role in production and life.

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Ensemble Kernel-NN: Machine Learning with Hybrid Features

Ensemble Kernel-NN: Machine Learning with Hybrid Features

引用

Advanced Algorithms and Control Engineering (ICAACE), International conference on

作者： Yifei Gong Department of Financial and Actuarial Mathematics Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Classification is a fundamental task in machine learning and data analysis that has practical applications in image and speech recognition, natural language processing, medical detection, and fault detection. However, determining whether data exhibits linear or nonlinear characteristics remains challenging in practice. This difficulty stems from two factors: (1) the inherent complexity of data characteristics and their representations, and (2) the computational overhead required for preprocessing methods such as parameter adjustment, data visualization, dimension reduction, and kernel function replacement. This paper proposes an Ensemble Kernel-NN algorithm that utilizes the idea of ensemble learning. It is based on the Radial Basis Function neural network and support vector machine method for bagging and boosting. This algorithm simplifies the entire machine-learning process by eliminating the need for judgment and preprocessing of dimensions and features. It can directly provide better classification prediction results than any hypothesis in an end-to-end manner, solving the linear and nonlinear feature classification problem simultaneously. We conducted detailed comparison experiments and ablation experiments to prove the effectiveness of our model and also performed a performance analysis of our baseline model.

关键词： Support vector machines Representation learning Analytical models Adaptation models Radial basis function networks Boosting Complexity theory Classification algorithms Ensemble learning Kernel

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A Novel Deep Reinforcement Learning Strategy in Financial Portfolio Management 7

A Novel Deep Reinforcement Learning Strategy in Financial Po...

引用

7th International conference on Big Data Analytics, ICBDA 2022

作者： Cui, Bowen Sun, Ruoyu Su, Jionglong Xi'An Jiaotong-Liverpool University Department of Mathematics Sciences Suzhou China Suzhou China

ISBN: (纸本)9781665479387

Reinforcement learning algorithms are used in various fields widely, such as cryptocurrency market forecasting, image recognition, and natural language processing. In this research, we use the Reinforcement learning algorithm to solve the portfolio management problem. In the Reinforcement learning algorithm, we adopt the squeeze-and-excitation in the neural network neural network to realize the Ensemble of Identical Independent Evaluators proposed by Jiang et al. The Squeeze-and-Excitation block works by adaptively recalibrating channel-wise feature responses, which improves the ability of the network in extracting information from the financial environment. To further improve the performance of the network, we adopt the soft thresholding function as nonlinear transformation layers to effectively eliminate the noise-related features. The cryptocurrency market is used to test the efficacy of our strategy along with eight traditional portfolio management strategies as well as Jiang et al.'s strategies. In our experiments, we use the Accumulated Portfolio Value, Sharpe Ratio, and Maximum Downward to assess the efficacies of the strategies. In conclusion, our strategy outperforms Jiang et al.'s strategies and other traditional strategies. Although our strategy has a nearly 30% Maximum Downward as metrics in back-tests, Accumulated Portfolio Value can reach nearly 170% in two different two-month back-tests, which is about 160% greater than the traditional strategy and Jiang et al.'s strategy. Moreover, our result Sharp ratios are 393.88% and 96.18% higher than the traditional strategy and Jiang et al.'s strategy, respectively. © 2022 IEEE.

关键词： Deep learning

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Management forecast based on big data fusion DEA and RBF algorithm

Management forecast based on big data fusion DEA and RBF alg...

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2021 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Zhu, Yan Xi'an Jiao Tong University City College Xi'an710018 China

with the rapid development of information technology, how to use big data for effective analysis has become the focus of various industries. Big data will inevitably bring data noise and data redundancy. The traditional big data preprocessing methods do not take into account the functional relationship between variables, and the commonly used big data modeling tools can not effectively model big data with complex nonlinear relationships. This paper puts forward a prediction method combining DEA and RBF, uses DEA to preprocess big data to select the effective date, then uses RBF to model the data, and compares the prediction accuracy with the original environmental management fund data without pre-processing. The results show that the DEA-RBF method has higher prediction efficiency and better prediction accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

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Multi-view Attention Networks for Visual Question Answering

Multi-view Attention Networks for Visual Question Answering

引用

Natural Language processing (ICNLP), International conference on

作者： Min Li Zongwen Bai Jie Deng School of Physics and Electronic Information Yan’an Univeristy Yan’an China Department 705 Sichuan Jiuzhou Electric Group Co.Ltd. Mianyang China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Visual question answering (VQA) is a typical multimodal task that necessitates a combination of computer vision and natural language processing expertise. The fundamental essence of VQA lies in the simultaneous comprehension of fine-grained language and visual information. In recent years, transformer-based methods have exhibited remarkable success in advancing the state-of-the-art in VQA. In this paper, we present an enhanced model, namely the Multi-view Attention Network (MVAN), a variant of the Transformer architecture. MVAN improves the effect of the model in filtering irrelevant information and focusing on local features. Specifically, we augment our network with a Gated Linear Unit (GLU) to discern and filter irrelevant or inconsequential information. Additionally, a Gated Convolution Block (GCB) is introduced to the self-attention layer of the Transformer variant. This integration facilitates the extraction of contextual semantic image information by considering both channel and spatial perspectives. As a result, the model effectively combines both local and global information, hence improving its predicted accuracy in VQA tasks. Ultimately, the model is subjected to verification and testing using the VQAv2 dataset. The outcomes of these evaluations demonstrate a notable enhancement in the performance of our model when compared to the existing methods. Furthermore, we also conduct extensive ablation experiments to explore the reasons for the effectiveness of the MVAN.

关键词： Visualization Semantics nonlinear filters Logic gates Predictive models Transformers Information filters Question answering (information retrieval) Standards Testing

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Concrete Structural Crack Damage Classification Using nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Bingshu Wang Jia Lin xiaodong Zhuang Guanghui Zhang C.L. Philip Chen School of Software Northwestern Polytechnical University Xi'an China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy.

关键词： Training Learning systems Dimensionality reduction Accuracy Artificial neural networks Feature extraction Road safety Cybernetics Principal component analysis image classification

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Inverse Scattering via Cascaded Neural Network

Inverse Scattering via Cascaded Neural Network

引用

2021 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2021

作者： Yao, Lei Li, Shiyong Sun, Houjun An, Qiang Beijing Key Laboratory of Millimeter Wave and Terahertz Technology Beijing Institute of Technology Beijing China Fourth Military Medical University Department of Biomedical Engineering Xi'an China

ISBN: (纸本)9781665429184

Traditional nonlinear solutions for inverse scattering problem have the drawback of high computational complexity. Its approximated alternative, back-projection (BP) algorithm, can achieve a good trade-off between imaging quality and complexity. However, in the case of limited frequency samples, BP suffers from the image quality degeneration. In order to achieve high-resolution imaging, this work turns to deep learning (DL) based approach and proposes an end-To-end cascaded neural network structure, namely a convolutional neural network (CNN) followed by a UNet network. Firstly, the equivalence between the fully connected network and the BP algorithm is derived. Secondly, to increase the learning ability of the network and avoid overfitting, a CNN is used to replace the fully connected network. By directly focusing the raw scattered radar echoes using the network, a coarse radar imagery of the region under investigation can be obtained. Then, a UN et network is further cascaded to suppress the clutter and improve the image quality of the coarse focused radar imagery. Finally, EM simulations using the MINST dataset are conducted to train the proposed network. The results show that the reconstruction using the trained cascaded network outperforms the BP algorithm under the condition that the computational complexity of the proposed algorithm and the BP algorithm is close. A better focusing performance is achieved as expected. © 2021 IEEE.

关键词： Computational complexity

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Fourier residuals image deblurring network based on deep learning

Fourier residuals image deblurring network based on deep lea...

引用

第43届中国控制会议

作者： Ziyang Wu Xuanhong Wang Dou Zhang Gang Li Yun xiao School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications School of Information Science and Technology Northwest University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Blurry images not only affect people's visual perception but also adversely affect autonomous driving systems,medical imaging, object recognition, and other fields. With the rapid development of the CNN has become one of the research hotspots due to its good performance in the field of image deblurring. The existing deblurring model based on deep learning has low parameters, fast running time, and good detail processing of blurry images, but the accuracy of the deblurring image from the global perspective needs to be improved. To solve this problem, Fourier Residual GELU Network(FRGNet) is proposed in this paper. FRGNet introduces a new feature extraction module to improve the feature extraction capability of blurry images. This module uses atrous convolution to increase the receptive field. The standard ResBlock is modified and the Layer Normalization is added so that the network can be smoothly trained when the learning rate is large. The nonlinear activation function GELU is applied in the frequency domain of blurry images, which can bring the global context learning ability to the network. Also added is a coordinated attention module to build information and build an improved Res *** the information of the image blurry kernel, the pixel-level difference between a blurry image and a sharp image is learned from the frequency domain. Comprehensive experiments show that this method improves the deblurring *** PSNR value in GOPRO is 33.22dB, which is 0.4dB higher than DeepRFT. The PSNR value in the RealBlur-R is 40.16dB,which is 0.36dB higher than DeepRFT. Further ablation studies showed the effectiveness of the various components in our proposed model.

关键词： Deep Learning Single image Deblurring Fast Fourier Transform image Restoration

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Seismic Signal Filtering based on Pseudo Wigner-Ville Distribution and Catte Model

Seismic Signal Filtering based on Pseudo Wigner-Ville Distri...

引用

2020 International conference on Intelligent Control, Measurement and Signal processing and Intelligent Oil Field, ICMSP 2020

作者： Yanping, Liu Zheng, Ma Huiqin, Jia Jianshen, Gao College of xi'An Shiyou University Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells Xi'an China

In practice, there are various noises included in the collected seismic data. As a common background noise, random noise may interfere even annihilate valid signals. In view of this, it needs to adopt effective methods to eliminate random noise as much as possible. This paper proposes a filtering method that is based on pseudo Wigner-Ville distribution (PWVD) and Catte model. PWVD is a windowed form of Wigner-Ville distribution (WVD) which can make the nonlinear signal to be locally linearized in addition to weakening the influence of cross item. And the Catte model is a kind of anisotropic diffusion algorithm which can eliminate the noise while protecting the edge of image. The implementation of the proposed method is to do PWVD for the frequency modulation form of noise-containing signal and then to filter the PWVD via Catte model, at last to adopt peak search to obtain the estimation of the valid signal. © Published under licence by IOP Publishing Ltd.

关键词： Wigner-Ville distribution

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