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Research on the Fault Diagnosis Method of Automotive Charging Pile Based on the Improved MLP with SAE

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第3期20卷 1333-1345页

作者： Han, Yuanxun Ren, Mingzhu Ma, Qingliang Zhong, Chaochun Chen, Jing Liang, Xuming Zhou, Shaoyuan Zhejiang Inst Qual Sci Hangzhou 310007 Zhejiang Peoples R China Guangzhou Inst Measurement & Testing Technol Guangzhou 510000 Guangdong Peoples R China

In order to improve the situation that the fault data set of electric vehicle charging pile has unbalanced data distribution under each fault and the small amount of data leads to the inconspicuous data features, this paper proposes a method of SAE-MLP model for fault diagnosis of charging pile fault data. This paper firstly utilizes AE to realize the augmentation of the original data, which solves the problem of inconspicuous features, and utilizes the MLP neural network which is good at handling small sample data to classify the faults on the AE processed data. Combining the features of both SAE and MLP two models can effectively improve the accuracy of fault diagnosis of the data in this paper compared with the previous methods of small sample data processing, and minimizes computational costs.

关键词： Charging pile Faults diagnosis Multi-layer perceptions sparse autoencoder

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Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning

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APPLIED ENERGY 2024年 358卷

作者： Zhao, Bo Zhang, Weige Zhang, Yanru Zhang, Caiping Zhang, Chi Zhang, Junwei Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr NANTEC Beijing 100044 Peoples R China

Lithium-ion batteries age continuously during usage due to their characteristics and the influence of various external factors, but as degradation deepens, it can lead to an apparent decrease in battery safety and reliability. Therefore, predicting remaining useful life from the current to end state and preventing possible dangerous incidents are essential for battery health management. A novel method from the perspective of providing aging features reference and balance prognostics speed and precision is proposed. Firstly, the used dataset is preprocessed in many ways. Then, through feature engineering, 79 features are extracted in the dataset from three perspectives: direct, evolution, statistics, and most representative 16 features are filtered to form the final feature set based on the method of combining discretization and importance. Feature engineering is established to find crucial information within the dataset that correlates highly with remaining life and represents most battery recession paths. Finally, the sparse autoencoder and Transformer integrated approach is proposed to build the life prediction model, and it can learn the temporal relationship between the feature set and remaining cycles quickly and accurately. According to the forecasting results of 62 batteries with different aging conditions, the error can reach 7.43% by only using the feature sequence under 30 cycle lengths. For early life prediction, the error of using the first 100 cycles data is at most 2.6%.

关键词： Lithium-ion batteries Remaining useful life Feature engineering sparse autoencoder Transformer

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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 823-845页

作者： Muaadh A.Alsoufi Maheyzah Md Siraj Fuad A.Ghaleb Muna Al-Razgan Mahfoudh Saeed Al-Asaly Taha Alfakih Faisal Saeed Faculty of Computing Universiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia DAAI Research Group College of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11345Saudi Arabia

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected ***,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion *** addition,IoT devices generate a high volume of unstructured *** intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data *** the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack *** numerous research domains,deep learning techniques have demonstrated their capability to precisely detect *** study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT ***,a sparse autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed ***,the Convolutional Neural Network(CNN)technique is employed to create a binary classification *** proposed SAE-CNN approach is validated using the Bot-IoT *** proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of *** addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

关键词： IoT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network

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From Seeing to Recognising-An Extended Self-Organizing Map for Human Postures Identification

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第9期9卷 7899-7906页

作者： He, Xin Zielinska, Teresa Dutta, Vibekananda Matsumaru, Takafumi Sitnik, Robert Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu 8080135 Japan Warsaw Univ Technol WUT Fac Power & Aeronaut Engn PL-00665 Warsaw Poland Warsaw Univ Technol Fac Mechatron PL-00661 Warsaw Poland

The letter presents a dedicated method for recognizing human postures using classification and clustering options. The ultimate goal of the research is to recognise human actions based on posture sequences. Such a task imposes expectations on the developed method. For this purpose, a sparse autoencoder combined with a Self-Organized Map (SOM) is proposed. SOM is equipped with an additional layer of post-labeling or clustering. This entire structure is called the extended SOM. Two task-oriented modifications are applied to improve SOM performance - a dedicated angular distance measure and a neighbourhood function for updating the SOM weights. The research contribution is the concept of extended SOM, which is trained using unlabeled data and classifies or clusters the human postures. The sparse autoencoder preserves the characteristics of the data while reducing its dimensionality. Better classification efficiency of the developed method is demonstrated compared to other representative methods. Ablation studies illustrate how the introduced modifications improve classification results. The developed method is characterised by good resolution in distinguishing postures. A discussion of the concept's usefulness is provided at the end of the letter.

关键词： Human postures improved self-organizing-map sparse autoencoder neighbourhood function

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A Physics-Guided MFL Deformed Defect Recovery Method

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第2期21卷 2113-2124页

作者： Jiang, Lin Zhang, Huaguang Liu, Jinhai Xiao, Qi Shen, Xiangkai Xu, Hang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery. Note to Practitioners-The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a

关键词： Feature extraction Pipelines Training Deformable models Testing Deformation Numerical models Deformed defect recovery physical features sparse autoencoder pre-trained model MFL

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A Deep Neural Network Model with Attribute Network Representation for lncRNA-Protein Interaction Prediction

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CURRENT BIOINFORMATICS 2024年第4期19卷 341-351页

作者： Wei, Meng-Meng Yu, Chang-Qing Li, Li-Ping You, Zhu-Hong Lei-Wang, Lei- Xijing Univ Sch Informat Engn Xian Peoples R China Longdong Univ Coll Agr & Forestry Qingyang Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Guangxi Acad Sci Guangxi Key Lab Human Machine Interact & Intellige Nanning Peoples R China

Background LncRNA is not only involved in the regulation of the biological functions of protein-coding genes, but its dysfunction is also associated with the occurrence and progression of various diseases. Various studies have shown that an in-depth understanding of the mechanism of action of lncRNA is of great significance for disease treatment. However, traditional wet testing is time-consuming, laborious, expensive, and has many subjective factors which may affect the accuracy of the *** Most of the methods for predicting lncRNA-protein interaction (LPI) rely on a single feature, or there is noise in the feature. To solve this problem, we proposed a computational model, CSALPI based on a deep neural *** Firstly, this model utilizes cosine similarity to extract similarity features for lncRNA-lncRNA and protein-protein, denoising similar features using the sparse autoencoder. Second, a neighbor enhancement autoencoder is employed to enforce neighboring nodes to be represented similarly by reconstructing the denoised features. Finally, a Light Gradient Boosting Machine classifier is used to predict potential *** To demonstrate the reliability of CSALPI, multiple evaluation metrics were used under a 5-fold cross-validation experiment, and excellent results were achieved. In the case study, the model successfully predicted 7 out of 10 disease-associated lncRNA and protein *** The CSALPI can be an effective complementary method for predicting potential LPIs from biological experiments.

关键词： lncRNA-protein interactions lncRNA protein cosine similarity sparse autoencoder neighbor enhancement autoencoder

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Reconstructing Waddington Landscape from Cell Migration and Proliferation

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2025年 1-14页

作者： Han, Yourui Chen, Bolin Bi, Zhongwen Zhang, Jianjun Hu, Youpeng Bian, Jun Kang, Ruiming Shang, Xuequn Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Big Data Storage & Management Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Math & Stat Xian 710072 Peoples R China Xi An Jiao Tong Univ Xian Childrens Hosp Affiliated Childrens Hosp Dept Gen Surg Xian 710003 Peoples R China Rewise Hangzhou Informat Technol Co Ltd Hangzhou 310000 Peoples R China

The Waddington landscape was initially proposed to depict cell differentiation, and has been extended to explain phenomena such as reprogramming. The landscape serves as a concrete representation of cellular differentiation potential, yet the precise representation of this potential remains an unsolved problem, posing significant challenges to reconstructing the Waddington landscape. The characterization of cellular differentiation potential relies on transcriptomic signatures of known markers typically. Numerous computational models based on various energy indicators, such as Shannon entropy, have been proposed. While these models can effectively characterize cellular differentiation potential, most of them lack corresponding dynamical interpretations, which are crucial for enhancing our understanding of cell fate transitions. Therefore, from the perspective of cell migration and proliferation, a feasible framework was developed for calculating the dynamically interpretable energy indicator to reconstruct Waddington landscape based on sparse autoencoders and the reaction diffusion advection equation. Within this framework, typical cellular developmental processes, such as hematopoiesis and reprogramming processes, were dynamically simulated and their corresponding Waddington landscapes were reconstructed. Furthermore, dynamic simulation and reconstruction were also conducted for special developmental processes, such as embryogenesis and Epithelial-Mesenchymal Transition process. Ultimately, these diverse cell fate transitions were amalgamated into a unified Waddington landscape. [GRAPHICS] .

关键词： Waddington landscape Energy indicator Cell migration and proliferation Reaction diffusion advection equation sparse autoencoder

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Heart disease prediction using autoencoder and DenseNet architecture

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EGYPTIAN INFORMATICS JOURNAL 2024年 28卷

作者： Alghamdi, Norah Saleh Zakariah, Mohammed Shankar, Achyut Viriyasitavat, Wattana Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Riyadh 11362 Saudi Arabia Univ Warwick Dept Cyber Syst Engn WMG Coventry CV74AL England Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India Lovely Profess Univ Sch Comp Sci & Engn Phagwara 144411 Punjab India Graphic Era Univ Dept Comp Sci & Engn Dehra Dun India Chitkara Univ Ctr Res Impact & Outcome Rajpura Punjab India Chulalongkorn Univ Fac Commerce & Accountancy Chulalongkorn Business Sch Bangkok Thailand

Heart disease continues to be a prominent cause of death globally, emphasizing the critical requirement for precise prediction techniques and prompt therapies. This research presents a new method that utilizes the collective capabilities of autoencoder and DenseNet architectures to predict heart illness. Our study is based on the Heart Disease UCI Cleveland dataset, which includes 13 variables that cover clinical and demographic parameters such as age, sex, cholesterol levels, and exercise-induced angina. The dataset presents issues due to its varied attribute types, including category and numerical variables. Furthermore, our approach tackles these difficulties by utilizing a dense autoencoder model, which produced exceptional outcomes. The Model attained a mean accuracy of 99.67% on the Heart Disease UCI Cleveland dataset. Further testing showed it was resilient, with a test accuracy of 99.99%. In addition, the Model demonstrated outstanding macro precision, macro recall, and macro F1 score, with percentages of 99.98%, 99.97%, and 99.96%, respectively. In addition, our results indicate that combining autoencoder and DenseNet designs shows potential for predicting cardiac disease, with substantial enhancements in accuracy and performance metrics compared to current approaches. This methodology can improve clinical decision-making and patient outcomes in cardiovascular care by accurately finding and defining complex patterns within the data. Notwithstanding these encouraging outcomes, our investigation has constraints. The specific attributes of the dataset utilized may limit the applicability of our findings. Subsequent studies could examine the suitability of our method for various datasets and analyze supplementary variables that may improve forecast precision. Furthermore, it is necessary to conduct prospective validation studies to evaluate our strategy's practical effectiveness in clinical environments.

关键词： Heart Disease Heart Disease Cleveland dataset Machine-Learning sparse autoencoder DenseNet

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Damage assessment of laminated composites using unsupervised autonomous features

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JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS 2024年第6期37卷 2123-2148页

作者： Khan, Asif Kim, Heung Soo Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Mech Engn Topi Khyber Pakhtunk Pakistan Dongguk Univ Dept Mech Robot & Energy Engn Seoul 04620 South Korea Dongguk Univ Dept Mech Robot & Energy Engn Seoul 04620 South Korea

This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms.

关键词： Laminated composites lamb waves damage assessment sparse autoencoder autonomous features

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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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