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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Siho Nam, Haewoon Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350313277

Low Probability of Intercept (LPI) radar signals play a vital role in electronic warfare by maintaining informational superiority. Classifying these LPI radar waveforms is a key capability but remains a challenging task due to strong noise interference. Traditional signal processing techniques often show limitations in effectively removing complex noise signals. While deep learning-based modulation classification has exhibited superior performance, its effectiveness is compromised in the presence of significant noise. In this study, we propose a deep learning-based denoising method using the U2-Net for LPI radar signals, followed by modulation classification using a Convolutional Neural Network (CNN). We further compare the performance of U2-Net with other denoising models such as U-Net and denoising autoencoder. Experimental results demonstrate that the U2-Net outperforms other methods, achieving over 90% classification accuracy for signals with a signal-to-noise ratio above -14dB. © 2023 IEEE.

关键词： denoising autoencoder Low Probability of Intercept (LPI) radar time frequency analysis U-Net U2-Net

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NrGe-DTL: a computational framework for cancer drug response prediction based on deep transfer learning from combined denoised genomic profiles and chemical structure embedding of drugs

NrGe-DTL: a computational framework for cancer drug response...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yuchen Lian, Linghang Yang, Xuhua Zhejiang University of Technology Coll. of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350337488

In recent years, precision medicine has been consistently studied and employed in cancer treatment. One of the main challenges in precision medicine is accurately predicting a cancer patient's response to a specific drug(s) using computational models. Due to the heterogeneity of cancer, the distribution between the drug response profiles based on cell lines and real samples is often different, which leads to out-of-data distribution problems that affect putting the existing predictive models into practical application. To overcome this limitation, a novel framework to predict cancer drug response based on transfer learning from combined genomic profiles and chemical structure embedding of drugs is proposed in this paper. This framework features a denoising auto-encoder (DAE) module designed to remove noise from genomics profiles and a graph convolutional network (GCN) employed to extract the embedding of pharmaceutical chemical structures. Through the experiments, our model outperforms recently published methods on Genomics of Drug Sensitivity in Cancer (GDSC) datasets, and gives promising results on personalized drug response prediction on TCGA pan-cancer datasets, especially for the response of two commonly used anti-cancer drugs―Gemcitabine and Cisplatin. In summary, our study designed a computational framework with the potential capability of predicting personalized cancer drug response, which has been preliminarily validated for its effectiveness. © 2023 IEEE.

关键词： chemical structure information denoising autoencoder drug response precision medicine transfer learning

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A Data-Driven Reliability Assessment Method for Composite Power Systems 3

A Data-Driven Reliability Assessment Method for Composite Po...

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3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Liu, Zeyu Zhang, Bingchen Li, Qiang Zhao, Feng Liu, Di Hou, Kai Tianjin University Key Laboratory of Smart Grid of Ministry of Education Tianjin300072 China State Grid Information and Telecommunication Group Co. Ltd Beijing102211 China

ISBN: (纸本)9798350318180

A data driven approach for reliability assessment of composite power systems have been proposed in our paper. A Multi-Layer Extreme Learning Machine (MELM) is trained to graph the relations among system states versus minimum load curtailment. This approach is a computationally efficient alternative to the traditional optimal power flow (OPF) optimizations. Moreover, the pretraining process using denoising autoencoder (DAE) is utilized to improve the feature extraction and predictive performance. By conducting case studies on the RTS-79 system, the proposed methodology was evaluated. Results show that the proposed method is efficient and effective in enhancing the assessment of reliability of composite power systems. © 2023 IEEE.

关键词： composite systems data-driven denoising autoencoder multilayer exterme learning machine reliability assessment

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Prediction model of bearing fault remaining useful life based on weighted variable loss degradation characteristics

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096122-096122页

作者： Yu, Tianyi Li, Shunming Lu, Jiantao Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China Nantong Inst Technol Sch Automot Engn Nantong 226002 Peoples R China

In the prediction of bearing fault remaining useful life (RUL), the identification and feature extraction of early bearing faults are very important. In order to improve the accuracy of early fault RUL prediction, a bearing fault RUL prediction model based on weighted variable loss degradation characteristics is proposed. The model is composed of a stack denoising autoencoder (SDAE) module guided by variable loss, a signal-to-noise feature adaptive weighting module and a long-short term memory (LSTM) degradation characteristics extraction and regression output module. Firstly, this model improves the ability of SDAE model to extract weak fault features by ascending dimension learning and variable loss function. Then, an adaptive weighting matrix is generated according to the test signal to modulate the weight vector of SDAE. Finally, the hidden layer features of SDAE were input into LSTM model to extract the bearing state degradation features and realize the RUL prediction of bearing faults. The experimental results show that the proposed model can accurately predict the RUL of the test data in the early fault stage and the fault development stage. The proposed model can give early fault warning to the bearing state.

关键词： remaining useful life prediction early failure denoising autoencoder long-short term memory bearing failure

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Feature extraction and classification of heart sound using 1D convolutional neural networks

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2019年第1期2019卷 1页

作者： Li, Fen Liu, Ming Zhao, Yuejin Kong, Lingqin Dong, Liquan Liu, Xiaohua Hui, Mei Beijing Inst Technol Sch Opt & Photon Beijing Key Lab Precis Optoelect Measurement Inst Beijing 100081 Peoples R China

We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using mel-frequency cepstral coefficients, and the proposed 1D CNN model has higher classification accuracy precision, higher F-score, and better classification ability than backpropagation neural network (BP) model. In addition, the improved 1D CNN has a classification accuracy rate of 99.01%.

关键词： Auscultation Convolutional neural networks (CNNs) denoising autoencoder Heart disease risk

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Detection and reconstruction of measurements against false data injection and DoS attacks in distribution system state estimation: A deep learning approach

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MEASUREMENT 2023年第1期210卷

作者： Raghuvamsi, Y. Teeparthi, Kiran Natl Inst Technol Andhra Pradesh Dept Elect Engn Tadepalligudem 534101 India

In a smart grid, the presence of advanced measurement devices and communication channels is significantly vulnerable due to cyberattacks such as false data injection attacks (FDIAs), and denial-of-service (DoS) attacks. To tackle these cyberattacks, a data-driven approach namely, the attention-based temporal convolutional denoising autoencoder is proposed which combines the advantages of the attention mechanism and temporal convolutional network to capture spatio-temporal information. This model identifies the FDIA location and also replaces the corresponding measurements with the reconstructed values. The robustness of the proposed technique is evaluated with different levels of FDIAs as well as missing data caused by DoS attacks. The simulations have been performed on IEEE 13-bus and IEEE 37-bus distribution systems and their reconstruction results along with the classification metrics are presented. Finally, the model's effectiveness is compared with other denoising autoencoder approaches and ML/DL approaches. From the simulations, the results show that the proposed model outperforms in reconstruction and classification.

关键词： False data injection attack DoS attack Distribution system state estimation denoising autoencoder Temporal convolutional network Smart meters

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Deep Learning-based Integrated Framework for stock price movement prediction

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APPLIED SOFT COMPUTING 2023年 133卷

作者： Zhao, Yanli Yang, Guang Wuhan Business Univ Sch Business Adm Wuhan Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China

Stock market prediction is a very important problem in the economics field. With the development of machine learning, more and more algorithms are applied in the stock market to predict the stock price movement. However, stock market prediction is regarded as a challenging task for the noise and volatility of stock market data. Therefore, in this paper, a novel hybrid model SA-DLSTM is proposed to predict stock market and simulation trading by combine a emotion enhanced convolutional neural network (ECNN), the denoising autoencoder (DAE) models, and long short-term memory model (LSTM). Firstly, user-generated comments on Internet were used as a complement to stock market data, and ECNN was applied to extract the sentiment representation. Secondly, we extract the key features of stock market data by DAE, which can improve the prediction accuracy. Thirdly, we take the timeliness of emotion on stock market into consideration and construct more reliable and realistic sentiment indexes. Finally, the key features of stock data and sentiment indexes are fed into LSTM to make stock market prediction. Experiment results show that the prediction accuracy of SA-DLSTM are superior to other compared models. Meanwhile, SA-DLSTM has a good performance both in return and risk. It can help investors make wise decisions. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Stock market prediction Sentiment analysis Long short-term memory denoising autoencoder

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A novel health indicator by dominant invariant subspace on Grassmann manifold for state of health assessment of lithium-ion battery

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 130卷

作者： Zhang, Ying Li, Yan-Fu Zhang, Ming Wang, Huan Univ Sci & Technol Beijing Sch Mech Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Tsinghua Univ Inst Qual & Reliabil Beijing 100084 Peoples R China Aston Univ Coll Engn & Phys Sci Birmingham B4 7ET England

The precise estimation of the state of health (SoH) in Lithium-ion batteries (LiBs) relies heavily on a reliable health indicator (HI). Conventional indicators are often constructed by directly concatenating features from multiple sources. It overlooks significant non-linear and correlative information inherent in raw signals. To address this limitation, this paper introduces an innovative approach for SoH estimation in LiBs. Deep features extracted from signals of various sensors are obtained using denoising auto-encoders (DAEs). Then the dominant invariant subspaces (DIS) are calculated through the non-linear transformation of multi-source features on the Grassmann manifold. It can preserve essential and robust characteristics. The health indicator quantifies the geodesic distance of DIS using a projection metric. It provides a more comprehensive inclusion of nonlinear and correlation information. Consequently, this indicator offers heightened precision in discerning differences in health states. Validation of the proposed method is conducted using the NASA dataset. The result demonstrates its effectiveness on the SoH assessment and superiority to the state-of-the-art method.

关键词： State of health Lithium-ion battery Grassmann manifold Dominant invariant subspace Health indicator denoising autoencoder Multi-source information fusion

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AutoMF: A hybrid matrix factorization model with deep learning to select anti-viral drugs for Covid-19

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 74卷

作者： Sajadi, Seyedeh Zahra Sadjadi, Seyed Mojtaba Chahooki, Mohammad Ali Zare Yazd Univ Dept Comp Engn Yazd Iran Shahrood Univ Technol Fac Comp Engn Shahrood Iran

In the wake of the novel Covid-19 disease pandemic, the global economy has been affected and health crises are widespread. The disease is still incurable, and no effective treatment exists for it. During the Covid-19 crisis, drug repurposing has proven to be an effective treatment strategy. Drug repositioning is an approach to finding effective drugs for treating new diseases by discovering new efficacy of existing drugs. Studies on drug-virus association can reveal new efficacy. The antiviral drug repositioning problem is defined here as a matrix completion problem in which antiviral drugs go down the rows, while viruses go down the columns. We propose a hybrid model called AutoMF that identifies new drug-virus association. This new hybrid model aims to develop a matrix factorization model with deep learning and use it to predict drug-virus associations for repositioning drugs. In our model, a heterogeneous drug-virus network is used, which combines drug-virus associations, drugdrug similarity matrix, and virus-virus similarity matrix. Matrix factorization can extract latent factors from the drug-virus associations. The sparse nature of the associations may prevent the latent factors from being very effective. To solve this problem, deep learning is used to learn effective latent representations from similarity matrices to incorporate with MF to enhance their latent factor priors. Our model outperforms several recent approaches in comparison to benchmarking tests performed on the DVA dataset. In our approach, we identify antiviral drugs currently being tested in clinical trials or those used currently to treat patients.

关键词： Drug-virus association Deep learning Hybrid model Latent feature denoising autoencoder

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Noise reduction method for wind turbine gearbox vibration signals based on CVMD-DRDSAE

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第11期35卷 116146-116146页

作者： Yao, Jinbao Yue, Bohao Wang, Yizhu Li, Xiang Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China

Wind turbine gearbox fault feature extraction is difficult due to strong background noise. To address this issue, a noise reduction method combining comprehensive learning particle swarm optimization-variational mode decomposition (CLPSO-VMD) and deep residual denoising self-attention autoencoder (DRDSAE) is proposed. Firstly, the proposed CLPSO-VMD algorithm is used to decompose the noisy wind turbine gearbox vibration signals. Subsequently, the intrinsic mode functions highly correlated with the original signals are selected through the Spearman correlation coefficient and utilized for signal reconstruction, thereby filtering out high-frequency noise outside the fault frequency band in the frequency domain characterization. Secondly, the improved DRDSAE is utilized to learn the latent representations of data in the first-level denoised signal, further reducing the residual noise within the fault frequency band while retaining important signal features. Finally, the envelope spectrum highlights the weak feature of the wind turbine gearbox vibration signal. Experimental results demonstrate the effectiveness of the proposed method in denoising wind turbine gearbox vibration signals under strong noise.

关键词： wind turbine gearbox signal denoising variational mode decomposition self-attention mechanism denoising autoencoder

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