检索结果-内蒙古大学图书馆

Multi-source data fusion for economic data analysis

NEURAL COMPUTING & APPLICATIONS 2021年第10期33卷 4729-4739页

作者： Li, Menggang Wang, Fang Jia, Xiaojun Li, Wenrui Li, Ting Rui, Guangwei Beijing Jiaotong Univ Natl Acad Econ Secur Beijing Peoples R China Beijing Jiaotong Univ Beijing Lab Natl Econ Secur Early Warning Engn Beijing Peoples R China Beijing Jiaotong Univ Beijing Ctr Ind Secur & Dev Res Beijing Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing Peoples R China

Economic data include data of various types and characteristics such as macro-data, meso-data, and micro-data. The source of economic data can be the data related to economy held by the National Bureau of statistics and a various software. These multi-source and heterogeneous data have important value for economic analysis and forecasting. Taking into account the limitations of existing methods such as low accuracy and complex calculations, this paper proposes an economic data analysis and prediction method based on machine learning. We use machine learning to solve the data fusion problem in the process of multi-source data analysis and prediction in the economic field. Specifically, we proposes an economic data analysis and forecasting method combining convolutional auto-encoder and extreme gradient boosting algorithms. This method uses a convolutional auto-encoder to extract the data characteristics of the normalized parameter sequence and uses it to train an extreme gradient boosting model to predict the level of economic development and evaluate the importance of each influencing factor. Finally, through a case study, this paper integrates the data of labor force, education and population to forecast GDP. Through the verification of this case, the prediction accuracy of the proposed method is higher than the AE-XGBoost method and CAE-1D-XGBoost method used in this experiment, and the error is kept below 11.7%.

关键词： Economic forecasts Multi-source data fusion convolutional auto-encoder Feature extraction Extreme gradient boosting

来源：评论

学校读者我要写书评

暂无评论

Control chart recognition based on the parallel model of CNN and LSTM with GA optimization

引用

EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115689-115689页

作者： Yu, Yaoxiang Zhang, Min Southwest Jiaotong Univ Sch Mech Engn Chengdu 61003 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu 610031 Peoples R China

Quality control process has become one of the most critical issues in intelligent manufacturing. As the most practical and prevalent tools for continuously monitoring, control chart patterns (CCPs) can be automatically recognized to judge the fault of production process. However, there are three shortcomings in the previous researches. 1) Insufficient or superfluous features are considered;2) Few studies simultaneously take the local features and time sequence information into account;3) With the exception of empiricism, little work has been done on searching for the optimal hyper-parameters for the neural networks. Accordingly, FFR-GACLN, a method for ten CCPs recognition is proposed in this paper. Such a method is comprised of two sections including the fusion feature reduction (FFR) and GACLN model construction. Firstly, the feature extraction comprising of features fusion and feature dimensionality reduction by convolutional auto-encoder is applied to enhance the reliability and performance of the extracted features. Then, such features are input into GACLN, a network combining the convolutional neural network (CNN) and the long-short term memory (LSTM) for achieving the final recognition task. Moreover, genetic algorithm (GA) is applied to search for the optimal hyper-parameters for FFR-GACLN. The quantitative and simulation results demonstrate that the performance of the proposed method is superior over the previous techniques.

关键词： Control chart patterns recognition Fusion feature reduction convolutional auto-encoder Long-short term memory convolutional neural network Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Place perception from the fusion of different image representation

引用

PATTERN RECOGNITION 2021年 110卷 107680-107680页

作者： Li, Pei Li, Xinde Li, Xianghui Pan, Hong Khyam, M. O. Noor-A-Rahim, Md. Ge, Shuzhi Sam Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Cent Queensland Univ Sch Engn & Technol Melbourne Vic Australia Univ Coll Cork Sch Comp Sci & IT Cork Ireland Natl Univ Singapore Interact Digital Media Inst Dept Elect & Comp Engn Singapore Singapore

Inspired by the human way of place understanding, we present a novel indoor place perception network to overcome: 1). the simplicity of existing methods that only use the image features of object regions to recognize the indoor place, 2). insufficient consideration of the semantic information about object at-tributes and states. By utilizing multi-modal information containing the image and natural language, the proposed method can comprehensively express the attributes, state, and relationships of objects which are beneficial for indoor place understanding and recognition. Specifically, we first present a natural language generation framework based on a Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) to imitate the process of place understanding. Next, a convolutional auto-encoder (CAE) and a mixed CNN-LSTM are proposed to extract image features and semantic features, respectively. Then, two different fusion strategies, namely feature-level fusion and object-level fusion, are designed to integrate different types of features and features from different objects. The category of the indoor place is finally recognized based on fused information. Comprehensive experiments are conducted on public datasets, and the results verify the effectiveness of the proposed place perception method based on linguistic cues. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Indoor place perception CNN LSTM convolutional auto-encoder Natural language

来源：评论

学校读者我要写书评

暂无评论

Double convolutional neural network for fault identification of power distribution network

引用

ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期210卷

作者： Zou, Mi Zhao, Yan Yan, Dong Tang, Xianlun Duan, Pan Liu, Sanwei Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China State Grid Hunan Elect Power Co Ltd Elect Power Res Inst Changsha 410000 Peoples R China

The rapid and accurate identification of different types of faults in the power grid is of great significance to the stable operation of the power grid. An identification model of transient fault recording data for the distribution network based on a double convolutional neural network is proposed in this study. The 1-dimension convolutional auto-encoder (1-D CAE) is used to learn features from the power grid transient data. The obtained lowdimensional fault features are imported into the 1-dimension convolutional neural network (1-D CNN) identification model. The identification accuracy of the proposed model is higher than that of the traditional methods by the verification of the measured transient fault data of the power distribution network. The robustness study implies that the DCNN model can be applied in practical situations prone to using contaminated samples.

关键词： Feature extraction Fault identification CNN convolutional auto-encoder

来源：评论

学校读者我要写书评

暂无评论

Deep robust multi-channel learning subspace clustering networks

引用

IMAGE AND VISION COMPUTING 2023年第1期137卷

作者： Fang, Mengzhu Gao, Wei Feng, Zirui Northeast Normal Univ Sch Math & Stat Changchun 130024 Jilin Peoples R China Loughborough Univ Sch Business & Econ Loughborough LE11 3TU England

Subspace clustering methods are now widely used for unsupervised high-dimensional data processing in computer vision and other domains. Deep subspace clustering methods based on auto-encoder networks have made a significant improvement in nonlinear subspace clustering problems in comparison to previous works. However, these methods ignore the valid information lost during feature extraction, resulting in incomplete information and imprecise feature representations for subspace clustering. In addition, the clustering performance of the existing clustering methods is excessively dependent on hyper-parameters, making training difficult and unstable. In this paper, we propose Deep Robust Multi-Channel Learning Subspace Clustering Networks (DRMCLSC), a novel deep subspace clustering network for learning more comprehensive feature representations with good robustness for subspace clustering. The multi-channel learning strategy allows the model to extract, retain and fuse features simultaneously, enabling all valid information from the sample data to be obtained. Moreover, the multi-channel learning structure of the proposed method produces a more stable integration network that is less dependent on hyper-parameters and more resistant to training errors than previous works. Extensive experimental results on four benchmark datasets demonstrate the proposed method is superior and more effective than the state-of-the-art subspace clustering methods.

关键词： Subspace clustering Deep networks Unsupervised learning Multi-channel learning convolutional auto-encoder Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Classification of COVID-19 Patient Based on Multilayer Perceptron Neural Networks Optimized with Garra Rufa Fish Optimization Using CT Scan Images

引用

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第6期37卷 2357003-2357003页

作者： Selvaraj, Vairaprakash Alagarsamy, Manjunathan Thangaraju, Dineshkumar Mani, Dinesh Paramathi Ramco Inst Technol Dept Elect & Commun Engn Rajapalayam 626117 Tamil Nadu India K Ramakrishnan Coll Technol Dept Elect & Commun Engn Trichy 621112 Tamil Nadu India Kongunadu Coll Engn & Technol Dept Elect & Commun Engn Trichy 621215 Tamil Nadu India Sona Coll Technol Dept Elect & Commun Engn Salem 636005 Tamil Nadu India

COVID-19 is known in recent times as a severe syndrome of respiratory organ (Lungs) and has gradually produced pneumonia, a lung disorder all around the world. As coronavirus is continually spreading rapidly globally, the computed tomography (CT) technique has been made important and essential for quick diagnosis of this dangerous syndrome. Hence, it is necessitated to develop a precise computer-based technique for assisting medical clinicians in identifying the COVID-19 influenced patients with the help of CT scan images. Therefore, the multilayer perceptron neural networks optimized with Garra Rufa Fish optimization using images of CT scan is proposed in this paper for the classification of COVID-19 patients (COV-19-MPNN-GRF-CTI). The input images are taken from SARS-COV-2 CT-scan dataset. Initially, the input images are pre-processed utilizing convolutional auto-encoder (CAE) to enhance the quality of the input images by eliminating noises. The pre-processed images are fed to Residual Network (ResNet-50) for extracting the global and statistical features. The extraction over the features of CT scan images is made through ResNet-50 and subsequently input to multilayer perceptron neural networks (MPNN) for CT images classification as COVID-19 and Non-COVID-19 patients. Here, the layer of Batch Normalization of the MPNN is separated and added with ResNet-50 layer. Generally, MPNN classifier does not divulge any adoption of optimization approach for calculating the optimal parameters and accurately classifying the extracted features of CT images. The Garra Rufa Fish (GRF) optimization algorithm performs to optimize the weight parameters of MPNN classifiers. The proposed approach is executed in MATLAB. The performance metrics, such as sensitivity, precision, specificity, F-measure, accuracy and error rate, are examined. Then the performance of the proposed COV-19-MPNN-GRF-CTI method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precisi

关键词： Coronavirus SARS-COV-2 CT-scan dataset convolutional auto-encoder Residual Network (ResNet-50) multilayer perceptron neural networks Garra Rufa Fish (GRF) optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Attention-Aware Spectral Difference Representation for Hyperspectral Anomaly Detection

引用

REMOTE SENSING 2023年第10期15卷 2652-2652页

作者： Zhang, Wuxia Guo, Huibo Liu, Shuo Wu, Siyuan Xian Univ Posts & Telecommun Sch Comp Sci & Technol Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Peoples R China Chengdu Univ Informat Technol Dept Elect Engn Chengdu 610103 Peoples R China Xian Univ Technol Coll Comp Sci & Engn Xian 710048 Peoples R China

Hyperspectral Anomaly Detection (HAD) aims to detect the pixel or target whose spectral characteristics are significantly different from the surrounding pixels or targets. The effectiveness of reconstructing the background model is an essential element affecting the improvement of the HAD performance. This paper proposes a Hyperspectral Anomaly Detection method based on Attention-aware Spectral Difference Representation (HAD-ASDR) to reconstruct more accurate background models by using the generated noise distribution matchable to the background as input. The proposed HAD-ASDR mainly includes three modules: Attention-aware Spectral Difference Representation Module (ASDRM), convolutional auto-encoder based Background Reconstruction Module (CAE-BRM) and Joint Spectrum Intensity and Angle based Anomaly Detection Module (JSIA-ADM). First, inspired by Generative Adversarial Network (GAN), ASDRM is proposed to generate a noise distribution that better matches the background by the attention mechanism and the different operation. Then, CAE-BRM is employed to reconstruct the accurate background using the generated noise distribution as input and the convolutional auto-encoder with skip connections. Finally, JSIA-ADM is presented to detect anomalies more accurately by calculating the reconstructed errors from both spectral intensity and angle perspectives. The proposed HAD-ASDR has been verified on five data sets and achieves better or comparable HAD results compared to six other comparison methods. The average AUC of HAD-ASDR on these five data sets is 0.9817 higher than that of the comparison methods, resulting in an improvement of 0.0253. The experimental results demonstrate its superior performance and stability.

关键词： anomaly detection hyperspectral image attention mechanism generative adversarial network convolutional auto-encoder

来源：评论

学校读者我要写书评

暂无评论

A new intelligent fault diagnosis framework for rotating machinery based on deep transfer reinforcement learning

引用

CONTROL ENGINEERING PRACTICE 2023年第1期134卷

作者： Yang, Daoguang Karimi, Hamid Reza Pawelczyk, Marek Politecn Milan Dept Mech Engn Milan Italy Silesian Tech Univ Dept Measurements & Control Syst Gliwice Poland

The advancement of artificial intelligence algorithms has gained growing interest in identifying the fault types in rotary machines, which is a high-efficiency but not a human-like module. Hence, in order to build a human-like fault identification module that could learn knowledge from the environment, in this paper, a deep reinforcement learning framework is proposed to provide an end-to-end training mode and a human-like learning process based on an improved Double Deep Q Network. In addition, to improve the convergence properties of the Deep Reinforcement Learning algorithm, the parameters of the former layers of the convolutional neural networks are transferred from a convolutional auto-encoder under an unsupervised learning process. The experiment results show that the proposed framework could efficiently extract the fault features from raw time-domain data and have higher accuracy than other deep learning models with balanced samples and better performance with imbalanced samples.

关键词： Deep reinforcement learning convolutional auto-encoder Fault diagnosis Double deep Q network Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A study of deep convolutional auto-encoders for anomaly detection in videos

引用

PATTERN RECOGNITION LETTERS 2018年 105卷 13-22页

作者： Ribeiro, Manasses Lazzaretti, Andre Eugenio Lopes, Heitor Silverio Catarinense Fed Inst Educ Sci & Technol Rod SC 135 Km 125 BR-89560000 Videira SC Brazil Fed Univ Technol Parana UTFPR Av Sete Setembro 3165 BR-80230901 Curitiba PR Brazil

The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and challenges. convolutional Neural Networks have achieved the state-of-the-art performance for object recognition in recent years, since they learn features automatically during the training process. From the anomaly detection perspective, the convolutional autoencoder (CAE) is an interesting choice, since it captures the 2D structure in image sequences during the learning process. This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score. By exploring the CAE architecture, we also propose a method for aggregating high-level spatial and temporal features with the input frames and investigate how they affect the CAE performance. An easy-to-use measure of video spatial complexity was devised and correlated with the classification performance of the CAE. The proposed methods were evaluated by means of several experiments with public-domain datasets. The promising results support further research in this area. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Deep learning convolutional auto-encoder Anomaly detection Object recognition Feature extraction

来源：评论

学校读者我要写书评

暂无评论

auto-encoder Learning-Based UAV Communications for Livestock Management

引用

DRONES 2022年第10期6卷 276-276页

作者： Alanezi, Mohammed A. Mohammad, Abdullahi Sha'aban, Yusuf A. Bouchekara, Houssem R. E. H. Shahriar, Mohammad S. Univ Hafr Al Batin Dept Comp Sci & Engn Technol Hafar al Batin 31991 Saudi Arabia Ahmadu Bello Univ Dept Comp Engn Zaria 810001 Nigeria UCL Dept Elect & Elect Engn London WC1E 7JE England Univ Hafr Al Batin Dept Elect Engn Hafar al Batin 31991 Saudi Arabia

The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8PSK, 16PSK and 16QAM, without prior knowledge.

关键词： unmanned aerial vehicle convolutional auto-encoder livestock farming deep neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：