IET Signal Processing最新文献_第8页

GLAD: Global–Local Approach; Disentanglement Learning for Financial Market Prediction GLAD：全球-地方方法；金融市场预测的解纠缠学习

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2023-11-10 DOI: 10.1049/2023/6623718

Humam M. Abdulsahib, Foad Ghaderi

{"title":"GLAD: Global–Local Approach; Disentanglement Learning for Financial Market Prediction","authors":"Humam M. Abdulsahib, Foad Ghaderi","doi":"10.1049/2023/6623718","DOIUrl":"10.1049/2023/6623718","url":null,"abstract":"<div>\u0000 <p>Accurate prediction of financial market trends can have a great impact on maximizing profits and avoiding risks. Conventional methods, e.g., regression or SVR, or end-to-end training approaches, coined as deep learning algorithms, have restraints as a consequence of capturing noisy and unnecessary data. Financial market’s data are composed of stock’s price time series that are correlated, and each time series has both global and local dynamics. Inspired by recent advancements in disentanglement representation learning, in this paper, we present a promising model for predicting financial markets that learn disentangled representations of features and eliminate those features that cause interference. Our model uses the informer encoder to extract features, capturing global–local patterns by using the time and frequency domains, augmenting the clean features with time and frequency-based features, and using the decoder to predict. To be more specific, we adopt contrastive learning in the time and frequency domains to learn both global and local patterns. We argue that our methodology, disentangling and learning the influential factors, holds the potential for more accurate predictions and a better understanding of how time series move and behave. We conducted our experiments using the S&P 500, CSI 300, Hang Seng, and Nikkei 225 stock market datasets to predict their next-day closing prices. The results showed that our model outperformed existing methods in terms of prediction error (mean squared error and mean absolute error), financial risk measurement (volatility and max drawdown), and prediction net curves, which means that it may enhance traders’ profits.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2023 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/6623718","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms 基于改进硬阈值追踪算法的稀疏信号恢复

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2023-11-03 DOI: 10.1049/2023/9937696

Li-Ping Geng, Jin-Chuan Zhou, Zhong-Feng Sun, Jing-Yong Tang

引用次数: 0

RF Signal Feature Extraction in Integrated Sensing and Communication 集成传感与通信中的射频信号特征提取

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2023-10-28 DOI: 10.1049/2023/4251265

Xiaoya Wang, Songlin Sun, Haiying Zhang, Qiang Liu

{"title":"RF Signal Feature Extraction in Integrated Sensing and Communication","authors":"Xiaoya Wang, Songlin Sun, Haiying Zhang, Qiang Liu","doi":"10.1049/2023/4251265","DOIUrl":"10.1049/2023/4251265","url":null,"abstract":"<div>\u0000 <p>Because of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its security problems at present. Whether using machine learning methods or current deep learning-based target fingerprint identification, its performance is based on how well the radio frequency features (RFF) are extracted. Since the received signal is affected by various factors, we believe that we should first find the intrinsic features that can describe the properties of the target, which is the key to enhance the RF fingerprint recognition. In this paper, we try to analyze the intrinsic characteristics of the components that influenced the signal by the transmitting source and derive a mathematical formula to describe the RF characteristics. We propose a method using dynamic wavelet transform and wavelet spectrum (DWTWS) to enhance RFF features. The performance of the proposed method was evaluated by experimental data. Using a support vector machine classifier, the recognition accuracy is 99.6% for 10 individuals at a signal-to-noise ratio (SNR) of 10 dB. In comparison with the dual-tree complex wavelet transform (DT-CWT) feature extraction method and the wavelet scattering transform method, the DWTWS method has increased the interclass distance of different individuals and enhanced the recognition accuracy. The DWTWS method is superior at low SNR, with performance improvements of 53.1% and 10.7% at 0 dB.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2023 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/4251265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Li-Ion Battery State of Health Estimation Based on Short Random Charging Segment and Improved Long Short-Term Memory 基于短时随机充电段和改进长短期记忆的锂离子电池健康状态估计

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2023-10-23 DOI: 10.1049/2023/8839034

Aina Tian, Zhe Chen, Zhuangzhuang Pan, Chen Yang, Yuqin Wang, Kailang Dong, Yang Gao, Jiuchun Jiang

{"title":"Li-Ion Battery State of Health Estimation Based on Short Random Charging Segment and Improved Long Short-Term Memory","authors":"Aina Tian, Zhe Chen, Zhuangzhuang Pan, Chen Yang, Yuqin Wang, Kailang Dong, Yang Gao, Jiuchun Jiang","doi":"10.1049/2023/8839034","DOIUrl":"10.1049/2023/8839034","url":null,"abstract":"<div>\u0000 <p>Lithium-ion batteries have been used in a wide range of applications, including electrochemical energy storage and electrical transportation. In order to ensure safe and stable battery operation, the State of Health (SOH) needs to be accurately estimated. In recent years, model-based and data-driven methods have been widely used for SOH estimation, but due to the uncertainty of battery charging conditions in practice, it is difficult to obtain a fixed local segment. In this paper, the charging curve is first divided into several equal voltage difference segments based on charging segment voltage difference <i>ΔV</i> in order to solve the random charging segment problem. Time interval of equal charge voltage difference of the voltage curve, coefficient of variation and euclidean distance of the charging capacity difference curve are extracted as health features. The improved flow direction algorithmlong short term memory-based SOH assessment method is proposed and verified by the Oxford battery degradation dataset and experimental battery degradation dataset with a maximum error of 0.6%.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2023 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/8839034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135365414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Dynamic Modelling for Digital Twin Enabled Sintering Systems: An Iterative Update Data-Driven Method 数字孪生烧结系统的在线动态建模：一种迭代更新数据驱动方法

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2023-10-23 DOI: 10.1049/2023/6665657

Xuda Ding, Wei Liu, Jiale Ye, Cailian Chen, Xinping Guan

{"title":"Online Dynamic Modelling for Digital Twin Enabled Sintering Systems: An Iterative Update Data-Driven Method","authors":"Xuda Ding, Wei Liu, Jiale Ye, Cailian Chen, Xinping Guan","doi":"10.1049/2023/6665657","DOIUrl":"10.1049/2023/6665657","url":null,"abstract":"<div>\u0000 <p>The sintering process is a crucial thermochemical process in the blast furnace iron-making system. Tumble strength (TS), as a vital performance to assess sinter quality, is difficult to monitor due to the lack of timely measurement. Constructing a data-driven model for TS is an alternative for monitoring TS. However, the time-varying dynamic sintering process makes the task of modelling challenging. And the data are incomplete and insufficient in practice for modelling since there are unknown time delays in the system and lack actual TS value. The digital twin (DT) technique is a powerful tool to simulate the system dynamics with the real-time interaction between physical processes and virtual agents in cyberspace. This paper introduces a DT-enabled equivalent of the sintering system and proposes online data-driven modelling for TS monitoring. The time delay in the system is estimated for variable sequence alignment based on a modified maximum information coefficient method. The data used for modelling is enriched based on a multi-source information fusion technique. An adaptive update method is proposed to deal with the time-varying dynamics. The iterative forgetting factor-based algorithm is designed for the support vector regression method and guarantees a fast computational speed. Implementation and validation of the model on a DT-enabled sintering system show the efficiency of the proposed method. The accuracy of TS monitoring reaches 99.6% by analysis of 3 months’ data.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2023 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/6665657","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135414513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network 基于Hilbert-Huang变换和基于注意力的时空耦合网络的机器人地面媒介分类

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2023-10-23 DOI: 10.1049/2023/4721508

Jixiang Niu, Han Li, Zhenxiong Liu, Wei Liu, Hejun Xu

{"title":"Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network","authors":"Jixiang Niu, Han Li, Zhenxiong Liu, Wei Liu, Hejun Xu","doi":"10.1049/2023/4721508","DOIUrl":"10.1049/2023/4721508","url":null,"abstract":"<div>\u0000 <p>With the development of technology, mobile robots are increasingly deployed in real-world environments. To enable robots to work safely in a variety of terrain environments, we proposed a ground-type detection method based on the Hilbert–Huang transform (HHT) and attention-based spatiotemporal coupled network. Taking a dataset containing multiple sets of robot signals from a Kaggle competition as an example; we use the proposed method to classify the signals and thus achieve a terrain classification of the robot’s location. Firstly, the signal data were processed using the discrete wavelet transform for noise reduction, and all channels in the dataset were ranked by importance using the permutation importance method. Next, the instantaneous frequencies of the two most important channels were extracted using the HHT and added to the original dataset to expand the feature dimension. Then the features in the expanded dataset were extracted by the convolutional neural network, long short-term memory, and attention module. Afterward, the fully extracted features were passed into the fully connected layer for classification, and an average classification accuracy of 83.14% was obtained. The effectiveness of each part in our method was demonstrated using ablation experiments. Finally, we compared our method with some common methods in the field and found that our method obtained the highest classification accuracy, proving the superiority of the proposed method.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2023 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/4721508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135365296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials 烧结原料中化学成分数据输入的正则化多输出高斯卷积方法

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2023-10-23 DOI: 10.1049/2023/6647291

Wei Liu, Cailian Chen, Junpeng Li, Xinping Guan

{"title":"Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials","authors":"Wei Liu, Cailian Chen, Junpeng Li, Xinping Guan","doi":"10.1049/2023/6647291","DOIUrl":"10.1049/2023/6647291","url":null,"abstract":"<div>\u0000 <p>Chemical contents, the important quality indicators are crucial for the modeling of sintering process. However, the lack of these data can result in the biasedness of state estimation in sintering process. It, thus, greatly reduces the accuracy of modeling. Although there are some general imputation methods to tackle the data lackness, they rarely consider the interoutputs correlation and the negative impacts caused by incorrect prefilling. In this article, a novel sparse multioutput Gaussian convolution process (MGCP) modeling framework is proposed for data imputation. MGCP can flexibly mine the relationships between the outputs by a convolution of a sharing latent function and different Gaussian kernels. Moreover, the penalization terms are designed to weaken the false relationship between these outputs. To generalize the MGCP to a long-period case, dynamic time warping term is introduced to keep the global similarity between the original and estimated time series. Compared with several existing methods, the proposed method shows great superiority in sintering raw material contents estimation with real-world data.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2023 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/6647291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135365538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guided wave signal-based sensing and classification for small geological structure 基于导波信号的小型地质构造遥感与分类

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2023-07-27 DOI: 10.1049/sil2.12223

Hongyu Sun, Jiao Song, Shanshan Zhou, Qiang Liu, Xiang Lu, Mingming Qi

{"title":"Guided wave signal-based sensing and classification for small geological structure","authors":"Hongyu Sun, Jiao Song, Shanshan Zhou, Qiang Liu, Xiang Lu, Mingming Qi","doi":"10.1049/sil2.12223","DOIUrl":"https://doi.org/10.1049/sil2.12223","url":null,"abstract":"<p>Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non-negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three-dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third-order tensors data was constructed. Then, the TUCKER-based NTSF algorithm was employed for feature extraction and classification. To achieve multi-dimensional feature, the two-dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi-classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"17 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50145746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guided wave signal-based sensing and classification for small geological structure 基于导波信号的小型地质构造传感与分类

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2023-07-01 DOI: 10.1049/sil2.12223

Hongyu Sun, Jiao Song, Shanshan Zhou, Qiang Liu, Xiang Lu, Mingming Qi

引用次数: 0

Orthogonal frequency-division multiplexing-based signal design for a dual-function radar-communications system using circulating code array 基于正交频分复用的双功能雷达通信系统的循环码阵列信号设计

IF 1.7 4区工程技术

IET Signal Processing Pub Date : 2023-06-22 DOI: 10.1049/sil2.12231

Yu Zhou, Wen Ren, Qiuyue Zhang, Sisi Chen, Linrang Zhang

{"title":"Orthogonal frequency-division multiplexing-based signal design for a dual-function radar-communications system using circulating code array","authors":"Yu Zhou, Wen Ren, Qiuyue Zhang, Sisi Chen, Linrang Zhang","doi":"10.1049/sil2.12231","DOIUrl":"10.1049/sil2.12231","url":null,"abstract":"<p>In this study, a dual-function radar-communications (DFRC) system based on the circulating code array is presented to address the contradiction between radar and communications system in beam scanning and beam coverage. Processed orthogonal frequency-division multiplexing (OFDM) signal is transmitted by the circulating code array as the base signal to improve the data rate. Following the spatial angle of the communication receiver, the communication symbols are modulated to part of OFDM signal subcarriers occupying a specific frequency band. A significant property of the circulating code array, which provides a relationship between the baseband frequency of the base signal and the spatial angles, implements a basis for safe telecommunication transmission towards the cooperative receiver and demodulation. Moreover, the circulating code array transmits the same signal and introduces the same time interval between adjacent array elements. Therefore, the complex problems of multi-dimensional orthogonal signal design in the traditional multiple-input-multiple-output-based DFRC system design are transformed into a simple base signal design. Finally, an omnidirectional coverage pattern is obtained. Thus, whether the communication receiver is in the mainlobe or the sidelobe of the radar beam, the communication connection can be established between the designed DFRC system and the communication users. The performance of the described DFRC system is verified through theoretical analysis and simulations.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"17 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44946124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0