Stacked Denoising Autoencoder Research Articles

An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging

With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capability, we propose a fusion model that integrates the functions of the stacked denoising autoencoder (SDAE) and the Transformer model in order to improve the performance of RUL prediction. Firstly, the health factors under three different conditions are extracted from the measurement data as model inputs. These conditions include constant current and voltage, random discharge, and the application of principal component analysis (PCA) for dimensionality reduction. Then, SDAE is responsible for denoising and feature extraction, and the Transformer model is utilized for sequence modeling and RUL prediction of the processed data. Finally, accurate prediction of the RUL of the four battery cells is achieved through cross-validation and four sets of comparison experiments. Three evaluation metrics, MAE, RMSE, and MAPE, are selected, and the values of these metrics are 0.170, 0.202, and 19.611%, respectively. The results demonstrate that the proposed method outperforms other prediction models in terms of prediction accuracy, robustness, and generalizability. This provides a new solution direction for the daily life prediction research of lithium-ion batteries.

Cavitation fault diagnosis of centrifugal pump based on RIME-SDAE

In order to fully extract the fault characteristics of different cavitation fault states of centrifugal pumps, reduce the influence of hyperparameter settings on the identification and classification results of machine learning algorithms, this paper designs a network model based on the rime optimization algorithm (RIME) to improve the stacked denoising autoencoder (SDAE) network, which is named as RIME-SDAE. First of all, Singular Value Decomposition (SVD) is used to denoise the X, Y and Z signals of the triaxial vibration sensor, and time-domain, frequency-domain and time-frequency features are extracted to construct a signal feature set. Secondly, the three-dimensional feature indicators are analyzed and selected to be merged into the input dataset, and SDAE is trained with the RIME is used to determine the model parameters of SDAE synchronously. The feasibility of the proposed method is verified by the signals collected in the actual test, and the test results show that the accuracy on the test set reaches more than 98 %.

Life prediction of IGBT module for nuclear power plant rod position indicating and rod control system based on SDAE-LSTM

To reduce the losses caused by aging failure of insulation gate bipolar transistor (IGBT), which is the core components of nuclear power plant rod position indicating and rod control (RPC) system. It is necessary to conduct studies on its life prediction. The selection of IGBT failure characteristic parameters in existing research relies heavily on failure principles and expert experience. Moreover, the analysis and learning of time-domain degradation data have not been fully conducted, resulting in low prediction efficiency as the monotonicity, time correlation, and poor anti-interference ability of extracted degradation features.This paper utilizes the advantages of the stacked denoising autoencoder(SDAE) network in adaptive feature extraction and denoising capabilities to perform adaptive feature extraction on IGBT time-domain degradation data; establishes a long-short-term memory (LSTM) prediction model, and optimizes the learning rate, number of nodes in the hidden layer, and number of hidden layers using the Grey Wolf Optimization (GWO) algorithm; conducts verification experiments on the IGBT accelerated aging dataset provided by NASA PCoE Research Center, and selects performance evaluation indicators to compare and analyze the prediction results of the SDAE-LSTM model, PSO-LSTM model, and BP model. The results show that the SDAE-LSTM model can achieve more accurate and stable IGBT life prediction.

Surrogate model-based multi-objective optimization algorithm for train-ballast turnout system: Balancing safety and vibration damping in parameter design

In rail transportation structural parameter design, multi-objective optimal algorithms (MOOAs) have advanced significantly in safety, comfort, and aerodynamics domain. However, the vital factor of environment vibration remains neglected, impeding depot cover technology advancements. To address this challenge, with a focus on both safety and vibration damping. Firstly, the mechanistic models are constructed to establish a surrogate modeling database. Subsequently, a surrogate model is proposed, integrating stacked denoising autoencoders (SdAEs), self-attention (SA), and the gated recurrent unit (GRU). In addition, A novel MOOA, Dynamic Population-Multi-Objective Harris Hawks Optimization-Crowding Distance-Elitist Learning Strategy (D-MOHHO-CD-ELS), is proposed and its efficacy is validated against five existing MOOAs using the CTP1∼CTP7 test functions. Moreover, the train-ballast No.12 turnout system is considered as an engineering case. Importantly, the accuracy of both the mechanistic model and surrogate model is thoroughly validated. Among them, the performance of the data preprocessing and prediction algorithms is substantiated through a comparison of the proposed SdAEs-SA-GRU model against simulation results and eight other algorithms. Furthermore, the surrogate-based D-MOHHO-CD-ELS method, applied to tackle a multi-objective optimization problem with nine optimization parameters and four optimization objectives, demonstrates an efficiency improvement of 1 to 2 orders of magnitude compared to traditional methods. Consequently, following optimization, the wheel load reduction rate, derailment coefficient, capsizing coefficient and vibration damping effect are improved by 15.5%, 11.1%, 5.5% and 24.8% respectively, which reveals the effectiveness of enhancements in both the safety and damping efffect. the study offers a novel perspective for parameter design of TBT.

Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder

In practical engineering, data often lack labels, resulting in difficulty in fault diagnosis. Because stack-denoising autoencoders possess robust feature extraction capabilities and resistance to interference, an automatic and unsupervised bearing fault diagnosis method based on the stack-denoising autoencoder without an output layer was proposed in this study. As the stacked denoising autoencoder is an unsupervised algorithm, this approach can reduce reliance on manually labeled data labels. Therefore, this study proposed a new method for automatic fault diagnosis. First, the bearing fault features of the rolling bearing were extracted using the stack denoising autoencoder without an output layer. Meanwhile, the dimensions of the features were directly reduced to two or three dimensions by several hidden layers, thereby reducing manual experience. Second, the labels extracted from the clustering model were selected as inputs for different classifier models to automatically identify different types of faults. Two open-source rolling bearing datasets under various conditions were used to validate the classification performance of the proposed method. Finally, its effectiveness was verified using the experimental results. Various indicators were used to evaluate the performance of the proposed method, and the results showed an automatic bearing fault diagnosis accuracy of up to 90% when using different models and working conditions. Among the two datasets, the classification model achieved the highest accuracies of 0.99667 and 0.97143 and the lowest accuracies of 0.98000 and 0.90476, respectively.

Stacked Denoising Autoencoder Based Parkinson’s Disease Classification using Improved Pigeon-Inspired Optimization Algorithm

One of the most common neurological conditions caused by gradual brain degeneration is Parkinson's disease (PD). Although this neurological condition has no known treatment, early detection and therapy can help patients improve their quality of life. An essential patient's health record is made of medical images used to control, manage, and treat diseases. However, in computer-based diagnostics, disease classification is a difficult task. To overcome this problem, this paper introduces a stacked denoising Autoencoder (SDA) for Parkinson's disease classification. The main aim of this paper is to derive an optimal feature selection design for an effective PD classification. Improved Pigeon-Inspired Optimization (IPIO) algorithm is introduced to enhance the performance of the classifier. Thus, the classification result improved by the optimal features and also increased the sensitivity, accuracy, and specificity in the medical image diagnosis. The proposed scheme is implemented in PYTHON and compared with traditional feature selection models and other classification approaches. The experimental outcomes show that the proposed method yields a superior classification of PD than the current state-of-the-art method

A Fusion Network with Stacked Denoise Autoencoder and Meta Learning for Lateral Walking Gait Phase Recognition and Multi-Step-Ahead Prediction.

Lateral walking gait phase recognition and prediction are the premise of hip exoskeleton application in lateral resistance walk exercise. In this work, we presented a fusion network with stacked denoise autoencoder and meta learning (SDA-NN-ML) to recognize gait phase and predict gait percentage from IMU signals. Experiments were conducted to detect the four lateral walking gait phases and predict their percentage in 10 healthy subjects across different speeds. The performance of SDA-NN-ML and other widely used algorithms including Support Vector Machine (SVM), Adaptive Boosting (AdaBoost) and Long Short Term Memory (LSTM) were evaluated. The cross-subject recognition accuracy of SDA-NN-ML (89.94%) decreased by 4.62% compared to the training accuracy, which outperformed SVM (8.60%), AdaBoost (5.61%), and LSTM (7.12%). For real-time and cross-subject prediction of gait phase percentage, the RMSE of SDA-NN-ML (0.2043) outperformed that of a single regression network (0.2426). With a signal noise ratio of 100:30, the cross-subject recognition accuracy decreased by a mere 5.70%, while the prediction result (RMSE) of SDA-NN-ML increased by 0.0167 when compared to the noise-free results. SDA-NN-ML demonstrates a stable multi-step-ahead prediction ability with an accuracy higher than 82.50% and an RMSE of less than 0.23 when the ahead time is less than 200 ms. The results demonstrated that the proposed method has high accuracy and robust performance in lateral walking gait recognition and prediction.

Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network

This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. In this study, to enhance the reliability of experimental results, numerous experiments were conducted under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to draw conclusions. The research results showed that the SDAE model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDAE model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values

Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors

Transmission lines are directly exposed to the natural environment and are prone to failure due to meteorological factors. A novel approach for diagnosing transmission line faults under various meteorological conditions has been introduced. This method, known as SDA-ISSA-XGBoost, combines the power of Stacked Denoising Autoencoder (SDA), an improved Sparrow Search Algorithm (ISSA) enhanced with chaotic mapping sequences, adaptive weights, improved iterative local search, and a random differential mutation strategy, and eXtreme Gradient Boosting (XGBoost). The process begins with SDA, which extracts essential features from the initial meteorological data. Subsequently, ISSA is applied to optimize the parameters of the XGBoost model. This results in the ISSA-XGBoost fault diagnosis model. The performance of this model is compared to PSO-XGBoost and SSA-XGBoost. The experimental findings demonstrate that the ISSA-XGBoost model achieves an impressive fault diagnosis accuracy of 94.39%, surpassing both PSO-XGBoost and SSA-XGBoost by 6.54% and 3.74%, respectively.

Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.

Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN)

In order to improve the recognition precision and accuracy of tool wear monitoring, an unsupervised deep neural network (DNN) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector. On this basis, principal component analysis (PCA) and T-distributed random neighbor embedding (t-SNE) are used to reduce the dimensionality of the features twice, and finally a simple two-dimensional feature matrix is obtained. Finally, the deep neural network model of SDA is established by adding SoftMax regression layer, and the tool wear monitoring results are taken as new labeled data, and the deep neural network parameters are fine-tuned by secondary backpropagation. The experimental results show that the proposed method can learn adaptively and obtain effective feature expression, and the tool wear state recognition results are highly accurate. The proposed method can effectively identify the tool wear state.

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

Inverter Fault Diagnosis for a Three-Phase Permanent-Magnet Synchronous Motor Drive System Based on SDAE-GAN-LSTM

In this study, a novel intelligent inverter fault diagnosis approach based on a stacked denoising autoencoder–generative adversarial network–long short-term memory (SDAE-GAN-LSTM) under an imbalanced sample is proposed for a three-phase permanent-magnet synchronous motor (PMSM) drive system. The proposed method can address the problem of unbalanced fault data samples and improve the accuracy of fault classification. Concretely speaking, firstly, the stacked denoising autoencoder (SDAE) is pre-trained to obtain the optimum decoder network. Afterward, a new generator of generative adversarial networks (GANs) is designed to generate high-quality samples by migrating the pre-trained optimal decoder network to the hidden layer and output layer of the generator of GANs. Additionally, a new model of long short-term memory (LSTM) based on the second discriminator of the GANs is presented for fault diagnosis. The generator of GANs is cross-trained using the reconstruction error gained by SDAE and the fault diagnosis error obtained by LSTM, resulting in the generation of high-quality samples for fault discrimination. Simulation and experimental results demonstrate the effectiveness of the proposed fault diagnosis approach, and the average fault identification accuracy reaches 98.63%.

A concatenation approach-based disease prediction model for sustainable health care system

In the present world, due to many factors like environmental changes, food styles, and living habits, human health is constantly affected by different diseases, which causes a huge amount of data to be managed in health care. Some diseases become life-threatening if they are not cured at the starting stage. Thus, it is a complex task for the healthcare system to design a well-trained disease prediction model for accurately identifying diseases. Deep learning models are the most widely used in disease prediction research, but their performance is inferior to conventional models. In order to overcome this issue, this work introduces the concatenation of Inception V3 and Xception deep learning convolutional neural network models. The proposed model extracts the main features and produces the prediction result more accurately than traditional predictive models. This work analyses the performance of the proposed model in terms of accuracy, precision, recall, and f1-score. It compares the proposed model to existing techniques such as Stacked Denoising Auto-Encoder (SDAE), Logistic Regression (LR), MLP, MLP with attention mechanism (MLP-A), Support Vector Machine (SVM), Multi Neural Network (MNN), and Hybrid Convolutional Neural Network (CNN)-Random Forest (RF).

Passenger flow prediction and management method of urban public transport based on SDAE model and improved Bi-LSTM neural network

In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction model combining Stacked Denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field.

Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder

In recent years, extreme weather has occurred frequently, and the risk of heavy rainfall and flooding faced by the people has risen. It is therefore an urgent requirement to carry out applied research on heavy rainfall and flooding risk assessment. We took Henan Province, where a major flood disaster occurred in 2021, as an example to analyze the impact factors of urban flooding and conduct a risk assessment. Indicators were first selected from population, housing, and the economy, and correlation analysis was used to optimize the indicator system. Then, a deep clustering network model based on a stacked denoising autoencoder (SDAE) was constructed, the feature information implied in the disaster indicators was abstracted into potential features through the coding and decoding of the network, and a small number of potential features were used to express the complex relationship between the disaster indicators. The results of the study show that the high-risk areas of flood damage in Henan Province in 2021 account for 2.3%, the medium-risk areas account for 9.4%, and the low-risk areas account for 80.3%. These evaluation results are in line with the actual situation in Henan Province, and the division of the grade in some areas is more reasonable compared with the entropy weighting method, which is a commonly used method of disaster assessment. The new model does not need to calculate weights to cope with changes in indicators and disaster conditions. The research results can provide scientific reference for urban flood risk management, disaster prevention and mitigation, and regional planning.

Gearbox Fault Diagnosis Based on Optimized Stacked Denoising Auto Encoder and Kernel Extreme Learning Machine

The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods.

Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids

Multi-agent deep reinforcement learning (MA-DRL) method provides a groundbreaking approach to tackling computational problems in power systems, particularly for distributed energy resources that have been widely adopted to advance energy sustainability. This paper presents a novel optimal energy management based on proposed MA-DRL method. This method employs deep neural network to learn strategy based on stacked-denoising auto-encoders and multi-agent deep deterministic policy gradient learning capability. The MA-DRL method is adopted to find the optimal strategy of the optimal energy management problem under the Markov decision process framework. This method aims to coordinate multiple energies and achieve optimal operation over a variety of hourly dispatches while taking into account the distinct properties of electric and thermal energies. The primary challenge of the planning and operation of multiple energy carrier microgrids (MECMs) is determining the optimal interaction between renewable energy resources, energy storage systems, power-to-thermal conversion systems, and upstream power grid in order to improve overall energy utilization efficiency. The presented robust method can adaptively derive the optimal operation for MECMs through centralized learning and decentralized implementation. The optimization problem is employed in this study to concurrently reduce the total emissions and the operating costs while considering engineering design constraints. Finally, to demonstrate the efficiency of the proposed method, it is verified on an integrated modified IEEE 33-bus and 8-node gas systems.

Improved deep reinforcement learning for car-following decision-making

Accuracy improvement of Car-following (CF) model has attracted much attention in recent years. Although a few studies incorporate deep reinforcement learning (DRL) to describe CF behaviors, proper design of reward function is still an intractable problem. This study improves the deep deterministic policy gradient (DDPG) car-following model with stacked denoising autoencoders (SDAE), and proposes a data-driven reward representation function, which quantifies the implicit interaction between ego vehicle and preceding vehicle in car-following process. The experimental results demonstrate that DDPG-SDAE model has superior ability of imitating driving behavior: (1) validating effectiveness of the reward representation method with low deviation of trajectory; (2) demonstrating generalization ability on two different trajectory datasets (HighD and SPMD); (3) adapting to three traffic scenarios clustered by a dynamic time warping distance based k-medoids method. Compared with Recurrent Neural Networks (RNN) and intelligent driver model (IDM), DDPG-SDAE model shows better performance on the deviation of speed and relative distance. This study demonstrates superiority of a novel reward extraction method fusing SDAE into DDPG algorithm and provides inspiration for developing driving decision-making model.

Blink talk: A machine learning-based method for women safety using EEG and eye blink signals

Women's safety is currently thought to be a big issue in both urban and rural settings. A variety of smart gadgets and software were created to provide women with security. There are a lot of smart gadgets and applications on the market, but they don't offer a good answer and are too expensive. In this research, a novel machine learning-based Blink Talk method has been proposed for women safety. EEG based on some blink talk algorithms for eye blink detection. Initially, the EEG and eye blink signals are pre-processed using Discrete wavelet transform (DWT). The pre-processed signals are fed into Stacked Denoising autoencoder (SDAE) for extracting the features. In the next phase, the extracted features are used to classify the emotions of women through Multiclass Support vector machine (MSVM). The classification results are sad, happy, normal, and fear; finally, fear emotion is converted into text using GSM to the saved contacts and nearby police station and GPS scan the near radius surrounding people to send the alert and help request message for the help needed person. The experimental results reveal that the suggested approach provides high accuracy range of 98.04%. then the traditional machine learning techniques.