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Controlled-source electromagnetic signal detection using a hybrid deep learning model: Convolutional and long short-term memory neural networks

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Computers and Geosciences 2025年 204卷

作者： Liu, Yecheng Li, Diquan Li, Jin Hu, Yanfang Liu, Zijie Zhang, Xian School of Geosciences and Info-Physics Central South University Changsha 410083 China Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor Ministry of Education Central South University Changsha 410083 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha 410081 China Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha 410205 China

Controlled-source electromagnetic (CSEM) method using a periodic transmitted signal source suppresses random noise by superimposing and averaging the recorded signal over multiple periods. However, it still faces great challenges in strong interference environments. Here we present a CSEM signal detection method based on a hybrid architecture of convolutional neural network (CNN) and long short-term memory neural network (LSTM). Our method improves the quality of the recorded signal by filtering out all the noise periods and retaining the useful signal periods. The core lies in combining the spatial feature extraction capability of CNN with the time series modeling capability of LSTM, which can deeply excavate the feature differences between noise and CSEM useful signal. Meanwhile, a classification model of signal and noise is constructed using a large-scale training dataset. This hybrid model exhibits superior performance compared to other deep learning models such as CNN or LSTM. Also, we propose a novel signal detection mechanism that not only maintains the periodicity of CSEM signal, but also greatly enhances processing efficiency. The results of synthetic and measured data demonstrate that our method can obtain useful signals from CSEM data containing different noise types and significantly improve the quality of sounding curves. In particular, our method is useful for CSEM signal detection in strong interference. © 2025 Elsevier Ltd

关键词： Controlled-source electromagnetic method Deep learning Noise signal detection

Detection of Deepfake videos using long distance attention

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arXiv 2021年

作者： Lu, Wei Liu, Lingyi Luo, Junwei Zhao, Xianfeng Zhou, Yicong Huang, Jiwu School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100195 China School of Cyber Security University of Chinese Academy of Sciences Beijing100195 China Department of Computer and Information Science University of Macau 999078 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video contents and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and inter-frame inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively. The two components are designed using a novel long distance attention mechanism. The one component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves the state-of-the-art performance, and the proposed long distance attention method can effectively capture pivotal parts for face forgery. Copyright © 2021, The Authors. All rights reserved.

关键词： Time domain analysis

Polar codes with memory

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arXiv 2019年

作者： Zhou, Wenyue Liu, Qiang Shen, Yifei Zhou, Xiaofeng Zhang, Chuan Xu, Yaohua Li, Liping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China National Mobile Communications Research Laboratory Southeast University Nanjing China

Polar codes with memory (PCM) are proposed in this paper: a pair of consecutive code blocks containing a controlled number of mutual information bits. The shared mutual information bits of the succeeded block can help the failed block to recover. The underlying polar codes can employ any decoding scheme such as the successive cancellation (SC) decoding (PCM-SC), the belief propagation (BP) decoding (PCM-BP), and the successive cancellation list (SCL) decoding (PCM-SCL). The analysis shows that the packet error rate (PER) of PCM decreases to the order of PER squared while maintaining the same complexity as the underlying polar codes. Simulation results indicate that for PCM-SC, the PER is comparable to (less than 0.3 dB) the stand-alone SCL decoding with two lists for the block length N = 256. The PER of PCM-SCL with L lists can match that of the stand-alone SCL decoding with 2L lists. Two hardware decoders for PCM are also implemented: the in-serial (IS) decoder and the low-latency interleaved (LLI) decoder. For N = 256, synthesis results show that in the worst case, the latency of the PCM LLI decoder is only 16.1% of the adaptive SCL decoder with L = 2, while the throughput is improved by 13 times compared to it. Copyright © 2019, The Authors. All rights reserved.

关键词： Decoding

New Framework for Code-Mapping-based Reversible Data Hiding in JPEG Images

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arXiv 2020年

作者： Du, Yang Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Anhui Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Communication & Electronic Engineering East China Normal University Shanghai200241 China

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

Simultaneous Prediction of SOH and RUL for Lithium-ion Batteries Using Transferable Knowledge Sharing Network

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IEEE Transactions on Power Electronics 2025年

作者： Zhong, Kai Liu, Zhihao Tian, Jiaqiang Fan, Chao Li, Mince Wang, Yujie Liu, Xinghua Wang, Peng Anhui University Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Hefei230601 China Anhui University School of Eelectrical Engineering and Automation Anhui230039 China Hefei Comprehensive National Science Center Institute of Energy Hefei230601 China University of Science and Technology of China Department of Automation Hefei230027 China Xi'an University of Technology School of Electrical Engineering Xi'an710048 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

The state of health (SOH) and remaining useful life (RUL) prediction of lithium-ion batteries are critical for the safe operation and maintenance optimization of electrical systems. However, existing methods face challenges in generalization ability and prediction accuracy, including the limitations of single-task learning for joint SOH and RUL prediction, sensitivity to complex operating conditions, and the impact of cross-domain data distribution differences. To address these challenges, this paper proposes a dual-representation transferable knowledge sharing network (DRTKSN) to predict SOH and RUL for batteries. The proposed algorithm is based on the framework of multi task learning (MTL) and Transfer Learning. By extracting and fusing dual-representation data features, the framework effectively captures the latent patterns of battery aging characteristics. It employs parameter and network structure sharing mechanisms to enhance the adaptability and generalization of cross-domain features. The proposed model achieves information collaboration between SOH and RUL tasks through a shared network, effectively reducing computational costs and improving prediction accuracy. Experimental results demonstrate that the proposed framework exhibits excellent robustness and transferability in complex degradation scenarios, significantly outperforming baseline models. © 1986-2012 IEEE.

关键词： Multi-task learning

Energy-Efficient Virtual Machine Placement in Data Centers Via Accelerated Genetic Algorithm with Improved Fitness Computation

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SSRN

SSRN 2021年

作者： Hormozi, Elham Hu, Shuwen Ding, Zhe Tian, Yu-Chu Wang, You-Gan Yu, Zu-Guo Zhang, Weizhe School of Computer Science Queensland University of Technology GPO Box 2434 BrisbaneQLD4001 Australia School of Mathematical Sciences Queensland University of Technology GPO Box 2434 BrisbaneQLD4001 Australia Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Cyberspace Science Harbin Institute of Technology Harbin China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China Queensland University of Technology

Energy efficiency basically refers to how effectively energy can be utilised or consumed by any virtualised data centre giving cloud administrations. Since Genetic Algorithm (GA) is an acceptable method to solve the VM placement problem, this research formulates the Virtual Machine (VM) placement problem as a constrained optimisation problem. A considerable amount of residual resources is available for each new VM allocation, however they are distributed across multiple active PMs, rendering them inefficient. Our research focuses on improving a data centre’s energy and time efficiency by devising a technique to decrease PM’s residual resources and make them more valuable. As the most time consuming element of a GA is calculating the fitness function, this paper presents a new fitness function to simplify the fitness function calculation in GA. The results are compared to the standard GA and First Fit Decreasing (FFD) algorithm. The findings reveals a 8% energy savings for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA for virtualised data centres. The simulation experiments are conducted on small-, medium- and large-scale of the google data centre to demonstrate the effectiveness of our GA. © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

Multifractal temporally weighted detrended partial cross-correlation analysis to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors

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arXiv 2020年

作者： Li, Bao-Gen Ling, Dian-Yi Yu, Zu-Guo Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan411105 China School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 BrisbaneQ4001 Australia

When common factors strongly influence two cross-correlated time series recorded in complex natural and social systems, the results will be biased if we use multifractal detrended cross-correlation analysis (MF-DXA) without considering these common factors. Based on multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group and multifractal partial cross-correlation analysis (MF-DPXA) proposed by Qian et al., we propose a new method-multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA) to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors in this paper. We use MF-TWDPCCA to characterize the intrinsic cross-correlations between the two simultaneously recorded time series by removing the effects of other potential time series. To test the performance of MF-TWDPCCA, we apply it, MF-TWXDFA and MF-DPXA on simulated series. Numerical tests on artificially simulated series demonstrate that MF-TWDPCCA can accurately detect the intrinsic cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWDPCCA, we apply it on time series from stock markets and find that there exists significantly multifractal power-law cross-correlation between stock returns. A new partial cross-correlation coefficient is defined to quantify the level of intrinsic cross-correlation between two time series. Copyright © 2020, The Authors. All rights reserved.

关键词： Fractals

Nuclei Segmentation in Histopathology Images Using Rotation Equivariant and Multi-level Feature Aggregation Neural Network

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Nuclei Segmentation in Histopathology Images Using Rotation ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yiqi Chen Xuanya Li Kai Hu Zhineng Chen Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc Beijing China Chinese Academy of Sciences Institute of Automation Beijing China College of Medical Imaging and Inspection Xiangnan University Chenzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

The histopathological analysis is the gold standard for assessing the presence and many complex diseases, like tumors. As one of the essential part of tumors, the shape, staining, and tissue distribution of the nuclei plays an important role in tumor diagnosis. However, due to nuclei congestion and possible occlusion, nuclei segmentation remains challenging. In this paper, we propose an automatic and effective nuclei segmentation method in histopathology images based on rotation equivariant and multi-level feature aggregation neural network (REMFANet). First, considering the inherent rotation equivariant of digital pathological images, we introduce group equivariant convolutions to improve the performance of the automatic segmentation of pathological images. Second, to eliminate the semantic gap between shallow features and deep features in encoder-decoder structural models, we propose a multi-level feature aggregation strategy based on U-Net 3+. Specifically, (1) we design a new decoder module to restore pixel-level predictions more accurately; (2) we propose an improved long-skip connection mode to provide richer semantic information in the decoder; (3) we also construct a semantic enhancement block to enhance the robustness of lowlevel semantic information. Finally, we evaluate our REMFA-Net on the MoNuSeg dataset and compare the results with seven state-of-the-art methods. Experimental results demonstrate the superiority of the proposed method over other models for the nuclei segmentation in histopathology images.

关键词： Semantics Image segmentation Feature extraction Decoding Convolution Neural networks Training

Multiple reconstruction compression framework based on PNG image

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arXiv 2019年

作者： Lu, Zhiqing Yin, Zhaoxia Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Image compression