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denoising autoencoder multilayer perceptron spiking neural network for isonicotinic acid yield prediction on real industrial dataset

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Ren, Pinze Wang, Yitian Wang, Zisheng Peng, Dandan Liu, Chenyu Han, Te Tsinghua Univ Dept Chem Engn Beijing Peoples R China Univ Calif San Diego Sch Comp Sci San Diego CA USA Tsinghua Univ Dept Ind Engn Beijing Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Northwestern Polytech Univ Mech & Elect Engn Xiamen Peoples R China Beijing Inst Technol Ctr Energy & Environm Policy Res Beijing Peoples R China

Isonicotinic acid (INA) has attracted considerable interest as a crucial pharmaceutical intermediate, especially for the production of the anti-tuberculosis drug isoniazid. Nonetheless, industrial production of INA encompasses intricate procedures that are highly sensitive to process parameters, leading to yield variability. Hence, an efficient prediction model for forecasting INA yield is essential for enhancing production yields and ensuring the consistency of INA in pharmaceutical manufacturing processes. To address this challenge, the present study developed a brain-inspired spiking neural network (SNN) tailored to the prediction of INA yield. Specifically, we propose a novel denoising autoencoder multilayer perceptron based spiking neural network (DAEMLP-SNN) for this purpose. The SNN is designed to accurately emulate the dynamic behavior of biological neurons while maintaining low power consumption, thereby ensuring high biological plausibility. Drawing upon the principles of autoencoders, our research constructs a denoising autoencoder SNN capable of extracting meaningful latent features and compressing high-dimensional industrial data. Moreover, we concatenated the extracted features with the original data, thereby creating a more comprehensive representation of the input. This enriched input was then fed into the multilayer perceptron SNN, which markedly enhances the robustness and precision of INA yield predictions. Experimental findings demonstrated the superior performance of DAEMLP-SNN, as it consistently achieved accurate predictions across diverse process parameters.

关键词： INA yield prediction denoising autoencoder Spiking Neural Network Industrial data process

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denoising autoencoder for Partial Discharge Identification in Instrument Transformers 31

Denoising Autoencoder for Partial Discharge Identification i...

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31st International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Crivelaro, Matheus Goes Rodrigues, Douglas Gifalli, Andre Papa, Joao Paulo Gonzales, Carlos Guilherme de Souza, Andre Nunes da Silva, Gustavo Vinicius Silveira Neto, Erasmo Sao Paulo State Univ Dept Elect Engn Bauru SP Brazil Sao Paulo State Univ Dept Comp Bauru SP Brazil ISA CTEEP Specialized Maintenance Ctr Bauru SP Brazil High Voltage Equipments HVEX Dept Elect Engn Itajuba Brazil

ISBN: (纸本)9798350391893;9798350391886

Analyzing Partial Discharge (PD) signals is crucial to assessing the health of insulation in high-voltage systems. Nevertheless, noise often distorts these signals, hindering the ability to obtain precise information. This paper proposes a novel deep-learning approach using two denoising autoencoders (DAEs) to learn data representations and eliminate noise during reconstruction. By leveraging DAEs' capacity to capture essential features within the latent space, this method enhances the analysis of PD signals and yields more accurate results. This paper investigates the effectiveness of two deep-learning architectures for denoising partial discharge signals in high-voltage insulation systems. Experimental results carried out on a PD dataset demonstrated the efficiency of the Linear AE model in removing noise in sets A, B, and C suggesting that DAEs hold great promise in PD signal denoising.

关键词： partial discharge signal denoising autoencoder deep learning

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Weak ultrasonic guided wave signal recognition based on one-dimensional convolutional neural network denoising autoencoder and its application to small defect detection in pipelines

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MEASUREMENT 2025年 242卷

作者： Wu, Jing Yang, Yingfeng Lin, Zeyu Lin, Yizhou Wang, Yan Zhang, Weiwei Ma, Hongwei Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Guangdong Prov Key Lab Intelligent Disaster Preven Dongguan 523808 Peoples R China Dongguan Univ Technol Sch Environm & Civil Engn Dongguan 523808 Peoples R China

Pipeline failures are often caused by the expansion of small defects. Structural damage to pipelines can lead to major safety accidents. When ultrasonic guided wave (UGW) technology is used for pipeline failure detection, the echoes produced by small defects manifest as weak UGW signals amidst significant noise. The low amplitude of these signals or complete drowning by noise makes them difficult to recognize. This study innovatively introduces a one-dimensional convolutional neural network denoising autoencoder (1DCNN-based DAE) for noise reduction in UGW signals using deep learning. To improve the conventional DAE, the model incorporated the Parametric Rectified Linear Unit (PReLU) activation function and a CNN for enhanced feature extraction, resulting in the proposed 1DCNN-based DAE. The model is trained on an extensive dataset of mixed signals with strong noise and their corresponding clean signals, enabling autonomous denoising in an unsupervised manner. Additionally, this paper proposes the application of the window-shifted power spectrum method for analyzing the denoised signals to identify and locate pipeline defects. The method involves traversing the signal with a window to intercept fragments, calculating their power, and plotting the power spectrum curve. Defects are then located based on the peak positions of this curve. Numerical simulation and experimental signals were used to validate the proposed method. Simulation results showed that the proposed 1DCNN-based DAE effectively improved the signal-to-noise ratio (SNR) of UGW mixed signals from -9 dB to 21.63 dB, representing an improvement of up to 30.63 dB. Experimental results demonstrated that the method accurately detected weak UGW signals from small defective pipes with a 2 % cross-section loss rate, achieving over 90 % recognition confidence and less than 1.5 % axial positioning error rate. In summary, the proposed 1DCNN-based DAE can effectively improve the SNR of the signal, reduce the noise i

关键词： Ultrasonic guided waves denoising autoencoder Convolutional neural networks Defect recognition Pipelines

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VLSI Architecture Design for Compact Shortcut denoising autoencoder Neural Network of ECG Signal

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第4期72卷 1621-1633页

作者： Lai, Shin-Chi Wang, Szu-Ting Morsalin, S. M. Salahuddin Lin, Jia-He Hsia, Shih-Chang Chang, Chuan-Yu Sheu, Ming-Hwa Natl Formosa Univ Dept Automat Engn Huwei 632301 Yunlin County Taiwan Natl Formosa Univ Smart Machinery & Intelligent Mfg Res Ctr Huwei 632301 Yunlin County Taiwan Natl Formosa Univ Program Smart Ind Technol Res & Dev Huwei 632301 Taiwan Natl Yunlin Univ Sci & Technol Dept Elect Engn Touliu 64002 Yunlin County Taiwan Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu 64002 Yunlin County Taiwan

The Electrocardiogram (ECG) test detects and records cardiac-related electrical activity of the heart. The ECG test identifies and documents cardiac-related electrical activity in the heart. The use of ECG signals for cardiovascular disease nursing as a crucial component of preoperative evaluation is increasing. ECG signals need to denoise and display in a clear waveform due to the numerous noises. We have introduced Compact Shortcut denoising Auto-encoder (CS-DAE) neural network, which reduces the noise from ECG signals. The Compact Shortcut approach compresses the features passed through the shortcut layers, which lowers the operation's memory needs and improves the noise reduction impact. In addition, the encoder and decoder process the Pixel-Unshuffled and Pixel-Shuffled, which effectively mitigates the feature loss caused by down-sampling and up-sampling operations. As a result, the CS-DAE algorithm decreases the computation and required memory size while maintaining higher accuracy. We have used MITDB and NSTDB datasets for training and testing the proposed CS-DAE model, resulting in the average Percentage of Root Mean Square Difference (PRD) being 46.30% and the improvement of Signal-to-Noise Ratio (SNRimp) being 10.50. In addition, we have designed VLSI architect ure for the proposed CS-DAE neural network to accelerate low hardware cost and less computation. The TUL PYNQTM-Z2 development platform runs the Verilog code, which is used for VLSI architecture and has the lowest power consumption of 1.65W.

关键词： Electrocardiography Noise reduction Decoding Noise Neural networks Convolution Very large scale integration Heart Hardware Signal to noise ratio Electrocardiogram compact shortcut denoising autoencoder neural network ECG signals shortcut layers pixel-unshuffled and pixel-shuffled VLSI architecture hardware design

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Elimination of Random Mixed Noise in ECG Using Convolutional denoising autoencoder With Transformer Encoder

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第4期28卷 1993-2004页

作者： Chen, Meng Li, Yongjian Zhang, Liting Liu, Lei Han, Baokun Shi, Wenzhuo Wei, Shoushui Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Univ Shandong Prov Hosp Dept Cardiol Jinan 250061 Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional denoising autoencoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

关键词： Electrocardiography Convolution Transformers Feature extraction Logic gates Adaptive filters Wiener filters denoising autoencoder ECG denoising multi-head self-attention point-wise convolution random mixed noise transformer encoder

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A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 196卷

作者： Lu, Yu Yang, Tao Zhao, Chong Chen, Wen Zeng, Rong China West Normal Univ Sch Comp Sci Nanchong Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu Peoples R China China West Normal Univ Sch Elect Informat Engn Nanchong Peoples R China

The widespread application of unmanned aerial vehicle (UAV) swarms has posed unique challenges for anomaly detection. Multi-modal noise from multi-source heterogeneous sensors during UAV swarm communication affects data quality, and limited data sharing between different UAV organisations restricts training a unified anomaly detection model. To address these problems, this study proposes a UAV swarm anomaly detection model based on a multi-modal denoising autoencoder and federated learning (L-MDAE). First, L-MDAE simulates noise by adding perturbations to the original data during UAV swarm communication. Second, according to the characteristics of UAV data noise, this study designs a new MSE loss function (normalised mean square error, NMSE) based on the normalised correlation coefficient. Furthermore, heterogeneous neural networks with NMSE are constructed to enhance the multi-modal noise-removal capability of the model. Finally, this study considers the UAV control node as the client and the ground control station as the server. Using a federated learning mechanism, L-MDAE is trained on a client dataset, and its parameters are integrated and distributed on the server. In this way, each UAV can effectively detect abnormal data using L-MDAE. Experimental results on five datasets, including ALFA, TLM and ITS, demonstrate that L-MDAE outperforms baseline and related models. When using ALFA, L-MDAE achieved an accuracy of 0.9919 and a swarm anomaly detection accuracy of 0.9901, approximately 2% higher than that of the baseline model.

关键词： UAV swarm Intrusion detection Federated learning denoising autoencoder

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Reducing false damage detections in guided ultrasonic wave monitoring systems using a denoising autoencoder

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NONDESTRUCTIVE TESTING AND EVALUATION 2025年第4期40卷 1610-1640页

作者： Chen, Yon Kong Bakhary, Norhisham Padil, Khairul Hazman Li, Jun Shamsudin, Mohd Fairuz Univ Teknol Malaysia Fac Civil Engn Skudai Johor Malaysia Res & Dev Dept VRG Dinamik Sdn Bhd Kuala Lumpur Malaysia Univ Teknol Malaysia Inst Noise & Vibrat Kuala Lumpur Malaysia Univ Teknol Malaysia Inst Smart Infrastruct & Innovat Construct Forens Engn Ctr Skudai Johor Malaysia Curtin Univ Ctr Infrastructural Monitoring & Protect Sch Civil & Mech Engn Bentley WA Australia Univ Teknol Malaysia Fac Mech Engn Skudai Johor Malaysia

Guided ultrasonic wave (GUW) monitoring systems for pipeline structures are gaining much attention in critical sectors such as the petrochemical, nuclear and energy sectors. However, the effects of environmental and operational conditions (EOCs), especially temperature, may generate substantial false damage detections. The temperature effect may interfere with different coherent noise sources and generate unwanted peaks that are falsely identified as damage. In this paper, a denoising autoencoder (DAE) is proposed to reduce the frequency of false damage detections in GUW monitoring systems. A DAE decodes high dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing signals at a reference temperature with the fewest false damage detections, this structure forces the DAE to learn the essential features hidden within complex data. A database of GUW signals is formed based on the experimental measurements using a six-metre-long stainless steel Schedule 20 pipe. Variations in temperature and damage severity are applied to develop the database to mimic a simple step change in damage growth under EOCs. The outcomes obtained from this study show that the proposed methodology can reduce false damage detections during GUW monitoring and is valuable for pipeline safety evaluations.

关键词： Torsional guided ultrasonic wave denoising autoencoder structural health monitoring unsupervised neural network

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Intrusion Detection in IoT Systems Using denoising autoencoder

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IEEE ACCESS 2024年 12卷 122401-122425页

作者： Alrayes, Fatma S. Zakariah, Mohammed Amin, Syed Umar Khan, Zafar Iqbal Helal, Maha Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11671 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Riyadh 11362 Saudi Arabia Prince Sultan Univ Coll Comp & Informat Sci Riyadh 11586 Saudi Arabia Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

Protection against unwanted intrusions is crucial for preserving the integrity and security of connected devices in the context of Internet of Things (IoT) networks. The growing number of IoT devices has made several industries more vulnerable to cyberattacks and security breaches, including smart homes, industrial automation, and healthcare. In response to this pressing dilemma, the goal of this project is to create a novel method for intrusion detection in Internet of Things systems utilizing denoising autoencoder (DAE) models. Traditional intrusion detection methods often prove inadequate in Internet of Things scenarios due to resource restrictions, dynamic network topologies, and a diversity of communication protocols. By utilizing DAEs' unsupervised learning and feature extraction skills, our suggested approach creates a system that can identify and stop intrusion attempts in real-time. The evaluation of the study additionally makes use of the NSL-KDD and CICIDS 2017 datasets. DAE integration yields an unequaled accuracy of 99.991% when the CICIDS 2017 dataset is used, and an accuracy of 99.4% when the NSL-KDD dataset is used. The CICIDS 2017 dataset analysis reveals several notable performance measures, including an accuracy of 1.0, a precision of 0.995, and an F1-score of 0.998. Analyses of the NSL-KDD dataset also produce outstanding results, with an F1-score of 0.989, recall of 0.991, accuracy of 0.994, and precision of 0.984. The results also show how well the suggested DAE-based intrusion detection method works to stop unauthorized users from accessing IoT devices, which lowers the risk of issues with system integrity, privacy, and security. By strengthening resilience against evolving cyber threats in the networked Internet of Things landscape, this research enhances cybersecurity strategies tailored to address the unique challenges encountered by IoT ecosystems.

关键词： Internet of Things Intrusion detection system Internet of Things Intrusion detection system machine learning machine learning CICIDS2017 dataset CICIDS2017 dataset denoising autoencoder denoising autoencoder

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MDGN: Circuit design of memristor-based denoising autoencoder and gated recurrent unit network for lithium-ion battery state of charge estimation

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IET RENEWABLE POWER GENERATION 2024年第3期18卷 558-569页

作者： Wang, Jiayang Zhang, Xinghao Han, Yifeng Lai, Chun Sing Dong, Zhekang Ma, Guojin Gao, Mingyu Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Brunel Univ London Dept Elect & Elect Engn London England

Due to the highly complex and non-linear physical dynamics of lithium-ion batteries, it is unfeasible to measure the state of charge (SOC) directly. Designing systems capable of accurate SOC estimation has become a key technology for battery management systems (BMS). Existing mainstream SOC estimation approaches still suffer from the limitations of low efficiency and high-power consumption, owing to the great number of samples required for training. To address these gaps, this paper proposes a memristor-based denoising autoencoder and gated recurrent unit network (MDGN) for fast and accurate SOC estimation of lithium-ion batteries. Specifically, the DAE circuit module is designed to extract useful feature representation with strong generalization and noise immunity. Then, the gated recurrent unit (GRU) circuit module is designed to learn the long-term dependencies between high-dimensional input and output data. The overall performance is evaluated by root mean square error (RMSE) and mean absolute error (MAE) at 0, 25, and 45 & DEG;C, respectively. Compared with the current state-of-the-art methods, the entire scheme shows its superior performance in accuracy, robustness, and operation cost (referring to time cost).

关键词： circuit design denoising autoencoder gated recurrent unit memristor state of charge estimation

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Reconstruction model of three-dimensional ocean sound speed field based on Tucker-denoising autoencoder

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APPLIED ACOUSTICS 2024年 223卷

作者： Zhao, Jiabao Wang, Maofa Hu, Ning Zhu, Zhenjing Li, Houwei Wang, Yangzhen Liu, Darui Hangzhou Dianzi Univ Coll Elect & Informat Hangzhou Peoples R China Hangzhou Dianzi Univ Coll Mech Engn Hangzhou Peoples R China Hangzhou Dianzi Univ Marine Technol & Equipment Res Ctr Hangzhou Peoples R China

The reconstruction of the three-dimensional (3D) ocean sound speed field (SSF) is essential for the application of underwater sound equipment. Although traditional observation platforms equipped with sensor equipment can accurately measure the ocean sound speed, they often provide only point or line profiles, lacking comprehensive data for the entire sea area. Currently, there is limited research on reconstructing 3D ocean SSF, and existing models have yielded unsatisfactory results. This paper considers the main factors that influence ocean SSF and proposes a model that indirectly reconstructs it by utilizing temperature and salinity field data. The model is based on the Tucker decomposition algorithm and denoising autoencoder network. It takes as input temperature and salt depth data observed by Conductivity-Temperature-Depth (CTD) sensors during sea tests and outputs 3D ocean SSF data. The study compares the Tuckers-denoising autoencoder based approach with traditional methods such as kriging interpolation, Empirical Orthogonal Function (EOF), and dictionary learning techniques. The results indicate that the Tuckers-denoising autoencoder based approach outperforms traditional models by more than 38% in sparse regions of measured data, providing better approximations to actual values. This statement validates the accuracy and effectiveness of our proposed model, providing a practical and efficient approach for reconstructing 3D SSF in the ocean.

关键词： Three-dimensional ocean sound speed field Tucker algorithm denoising autoencoder Reconstruct

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