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Removal of multisource noise in airborne electromagnetic data based on deep learning

GEOPHYSICS 2020年第6期85卷 B207-B222页

作者： Wu, Xin Xue, Guoqiang He, Yiming Xue, Junjie Chinese Acad Sci Inst Geol & Geophys Key Lab Mineral Resources Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing 100029 Peoples R China China Univ Min & Technol Coll Geosci & Surveying Engn Beijing 100083 Peoples R China

Existing noise removal processes for airborne electromagnetic (AEM) data generally consist of several steps, with each using a specific method to remove a specific type of noise. To improve the efficiency of AEM denoising and reduce the impact of the subjective judgment of the operators on the processing results, we have adopted a deep learning method based on a denoising autoencoder (DAE), which enables in one single processing step the removal of multisource noise. The most common noise sources in AEM data, including motion-induced noise, nearby or moderately distant sferics noise, power-line noise, and background electromagnetic noise, will be combined with a large number of simulation responses to build a training set. The data in the training set will be used to train the deep learning DAE neural network so that the neural network could fully learn the respective characteristics of the signal and noise and further effectively distinguish the AEM response signal (useful signal) from the above noise. The field data were processed using this method, and the processing results were compared with those obtained using traditional methods. The comparison test revealed that this method is helpful to reduce the influence of subjective factors on the quality of data results and compress the entire AEM data processing time.

关键词： airborne electromagnetic multisource noise deep learning denoising autoencoder neural network

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Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer

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BMC MEDICAL GENOMICS 2018年第3期11卷 33-43页

作者： Kim, So Yeon Kim, Tae Rim Jeong, Hyun-Hwan Sohn, Kyung-Ah Ajou Univ Dept Comp Engn Suwon 16499 South Korea Baylor Coll Med Dept Mol & Human Genet Houston TX 77030 USA Texas Childrens Hosp Jan & Dan Duncan Neurol Res Inst Houston TX 77030 USA

Background: Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information. Methods: Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients. Results: The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer. Conclusions: In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.

关键词： Multi-omics Integrative analysis Random walk denoising autoencoder Pathway Breast cancer Gene expression DNA methylation

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Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Liu, Zeyu Du, Xiaofang Shi, Yuhai Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Hubei Peoples R China Xiang Yang DAAN Automobile Testing Ctr Ltd Xiangyang 441004 Hubei Peoples R China

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL *** model's efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and *** model's robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model's exceptional generalizability and potential for real-world applications in electric vehicle battery management.

关键词： Battery life degradation Health feature extraction Temporal neural network denoising autoencoder Sparrow optimization algorithm

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Demsasa: micro-video scene classification based on denoising multi-shots association self-attention

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PATTERN ANALYSIS AND APPLICATIONS 2024年第4期27卷 1-9页

作者： Gong, Rui Zhang, Yu Zhang, Yanhui Liu, Yue Guo, Jie Nie, Xiushan Shandong Jianzhu Univ Jinan 250101 Peoples R China Shandong First Med Univ Affiliated Hosp 1 Jinan 250014 Peoples R China

Due to segmentation and splicing in micro-videos when user upload videos to platform, the content of different shots in the same scene is discontinuous, which leads to the problem of large content differences between different shots. At the same time, due to the low resolution of the shooting equipment or jitter and other factors, the video has noise information. In view of the above problems, the conventional and serialized scene feature learning in micro-video cannot learn the content difference and correlation between different shots, which will weaken the semantic representation of scene features. Therefore, this paper proposes a micro-video scene classification method based on De-noising Multi-shots Association Self-attention (DeMsASa) model. In this method, the shot boundary detection algorithm segments micro- video firstly, and then the semantic representation of the multi-shots video scene is learned by de-noising, association between video frames in the same shot and the association modeling between different shots. Experiments results show that the classification performance of the proposed method is superior to the existing micro-video scene classification methods.

关键词： Micro-video scene classification Multi-shots association self-attention Shot boundary detection denoising autoencoder

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Multi-feature fusion deep networks

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NEUROCOMPUTING 2016年 218卷 164-171页

作者： Ma, Gang Yang, Xi Zhang, Bo Shi, Zhongzhi Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China

In this paper, we propose a novel deep networks, multi-feature fusion deep networks (MFFDN), based on denoising autoencoder. MFFDN significantly reduces the classification error while giving the interpretability of the hidden-layer feature representation in learning process. Comparing with the traditional denoising autoencoder, MFFDN mainly shows the following advantages: (1) minimally retaining a certain amount of "information" constrained to a given form about its input;(2) explicitly interpreting the meaning of the feature representation in one hidden layer;(3) enhancing discriminativeness of the whole networks. At last, the experiments analysis on MNIST, CIFAR-10 and SVHN prove the state-of-the-art performance improvement of MFFDN with the advantages minimally retaining "information" constraint and the interpreted hidden feature representation. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Deep networks denoising autoencoder Interpretability Discriminativeness

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Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

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BRIEFINGS IN BIOINFORMATICS 2024年第2期25卷 bbae102页

作者： Hu, Dayu Liang, Ke Dong, Zhibin Wang, Jun Zhao, Yawei He, Kunlun Natl Univ Def Technol NUDT Changsha Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Big Data Res Ctr 28 Fuxing Rd Beijing 100853 Peoples R China

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://***/DayuHuu/scEMC.

关键词： single-cell clustering skip aggregation network denoising autoencoder ZINB deep learning

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Multi-condition training for noise-robust speech emotion recognition

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ACOUSTICAL SCIENCE AND TECHNOLOGY 2019年第6期40卷 406-409页

作者： Chiba, Yuya Nose, Takashi Ito, Akinori Tohoku Univ Grad Sch Engn Aoba Ku 6-6-05 Aza Aoba Sendai Miyagi 9808579 Japan

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Efficient ensemble to combat flash attacks

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COMPUTATIONAL INTELLIGENCE 2024年第1期40卷 e12488-e12488页

作者： Kumar, Om C. U. Bhama, Ponsy R. K. Sathia Easwari Engn Coll Dept CSE Chennai 600089 Tamil Nadu India Anna Univ Dept Comp Technol MIT Campus Chennai Tamil Nadu India

Flash event generates enormous traffic and the cloud service providers use sustaining techniques like scaling and content delivery network to up their services. One of the main bottlenecks that the cloud service providers still find difficult to tackle is flash attacks. Illegitimate users send craftily designed packets to land up inside the server for wreaking havoc. As deep learning autoencoder has the potential to detect malicious traffic it has been used in this research study to develop an ensemble. Convolutional neural network is efficacious in overcoming the issue of overfitting;deep autoencoder is proficient in extracting features through dimensionality reduction. In order to obtain both these advantages it was decided to develop an ensemble keeping denoising autoencoder as the core element. The process of addressing a flash attack requires first detecting the presence of bot in malicious traffic, second studying its nature by observing its behavioral manifestations. Detection of botnet was achieved by three ensembles, namely, DAE_CNN, DAE_MLP, and DAE_XGB. But capturing its external manifested behavior is challenging, because the bot signatures are always in a state of flux. The simulated empirical study yielded an appreciable outcome. Its accuracy rate was 99.9% for all the three models and the false positive rates were 0, 0.006, and 0.001, respectively.

关键词： botnet convolutional neural network denoising autoencoder DDoS ensemble multilayer perceptron

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Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors

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SENSORS 2020年第7期20卷 1895-1895页

作者： Sigcha, Luis Costa, Nelson Pavon, Ignacio Costa, Susana Arezes, Pedro Manuel Lopez, Juan De Arcas, Guillermo Univ Politecn Madrid Grp Invest Instrumentac & Acust Aplicada I2A2 ETSI Ind Campus Sur UPMCtra ValenciaKm 7 Madrid 28031 Spain Univ Minho Sch Engn ALGORITMI Res Ctr P-4800058 Guimaraes Portugal

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson's disease (PD). The occurrence of FOG reduces the patients' quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms' evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients' homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).

关键词： IMU accelerometer convolutional neural networks LSTM consecutive windows denoising autoencoder time distributed spectral representation

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Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Zhang, Lin Wang, Jixin Chang, Rui Wang, Weigang Zhejiang Hosp Integrated Tradit Chinese & Western Hangzhou 310003 Peoples R China Zhejiang Gongshang Univ Dept Stat & Math Hangzhou 310018 Peoples R China Jining 1 Peoples Hosp Dept ICU Jining 272100 Peoples R China

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.

关键词： Hepatitis C autoencoder denoising autoencoder

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