In recent years the growing amount of data generated in industrial processes has enabled the development of data-driven decision-making systems, placing strong interest on artificial intelligence, particularly when th...
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ISBN:
(纸本)9783031624940;9783031624957
In recent years the growing amount of data generated in industrial processes has enabled the development of data-driven decision-making systems, placing strong interest on artificial intelligence, particularly when the world of Internet-of-Things is considered. Managing and monitoring this data flow is crucial in machining processes, where the health of the system is assessed through the analysis of different sources, such as vibration, temperature, electric and acoustic signals. Currently, the main theme in tackling this task is anomaly detection: identifying an anomalous state is equivalent to having adequate control of the process and being able to make decisions accordingly, which, if achieved in the early stages, gives an incredible return, avoiding failures and production interruptions. In this paper an approach combining Wavelet Packet Decomposition and autoencoders for anomaly detection of CNC machining processes is presented. To this end, acoustic emission signals of a real-world use case are considered. To prove the effectiveness of the proposed system, a comparison with an Isolation Forest algorithm, a well-known benchmark in this field, is made. The results show an improvement of nearly 10% in terms of F1-score and accuracy, as well as the advantages of the encoding procedure.
Modeling, prediction, and recognition tasks depend on the proper representation of the objective curves and surfaces. Polynomial functions have been proved to be a powerful tool for representing curves and surfaces. U...
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Modeling, prediction, and recognition tasks depend on the proper representation of the objective curves and surfaces. Polynomial functions have been proved to be a powerful tool for representing curves and surfaces. Until now, various methods have been used for polynomial fitting. With a recent boom in neural networks, researchers have attempted to solve polynomial fitting by using this end-to-end model, which has a powerful fitting ability. However, the current neural network-based methods are poor in stability and slow in convergence speed. In this article, we develop a novel neural network-based method, called Encoder-X, for polynomial fitting, which can solve not only the explicit polynomial fitting but also the implicit polynomial fitting. The method regards polynomial coefficients as the feature value of raw data in a polynomial space expression and therefore polynomial fitting can be achieved by a special autoencoder. The entire model consists of an encoder defined by a neural network and a decoder defined by a polynomial mathematical expression. We input sampling points into an encoder to obtain polynomial coefficients and then input them into a decoder to output the predicted function value. The error between the predicted function value and the true function value can update parameters in the encoder. The results prove that this method is better than the compared methods in terms of stability, convergence, and accuracy. In addition, Encoder-X can be used for solving other mathematical modeling tasks.
Accurate forecasting of air pollutant PM2.5(particulate matter with diameter less than 2.5 mu m) is beneficial to society. However, the non-linear spatio-temporal correlations, multi-feasible forecast values and incom...
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Accurate forecasting of air pollutant PM2.5(particulate matter with diameter less than 2.5 mu m) is beneficial to society. However, the non-linear spatio-temporal correlations, multi-feasible forecast values and incomplete training data due to stochasticity make it challenging for discriminative deep learning approaches to forecasting PM2.5 data. In this paper, a generative modeling approach is proposed to overcome the challenges in forecasting PM2.5 data by considering it as an ill-posed inverse problem. To strengthen its applicability, the proposed approach is theoretically validated. Furthermore, based on the proposed generative modeling, an autoencoder-based generative adversarial network (GAN) named Air-GAN is developed. Air-GAN combines a convolutional neural network- long short-term memory (CNN-LSTM) based Encoder with a conditional Wasserstein GAN (WGAN) to capture non-linear correlations in the data distribution via inverse mapping from the forecast distribution. The condition vector to conditional WGAN is the novelty in Air-GAN, which employs this inverse learning and allows the WGAN's Generator to generate accurate forecast estimates from noise distribution. The condition vector is composed of two elements: (1) the category label of the best correlated meteorological parameter with the PM2.5 data, assigned using an efficient classifier and (2) the output of the CNN-LSTM-based Encoder which is the latent representation of the forecast. The extensive evaluation of Air-GAN for predicting the real-time PM2.5 data of Delhi demonstrates its superior performance with an average inference error of 5.3 mu g/m(3), which achieves 31.7% improvement over the baseline approaches. The improved performance of Air-GAN demonstrates its efficiency to forecast stochastic PM2.5 data by generalizing to out-of-distribution data.
Providing timely assistance to students in intelligent tutoring systems is a challenging research problem. In this study, we aim to address this problem by determining when to provide proactive help with autoencoder b...
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ISBN:
(纸本)9783031363351;9783031363368
Providing timely assistance to students in intelligent tutoring systems is a challenging research problem. In this study, we aim to address this problem by determining when to provide proactive help with autoencoder based feature learning and a deep reinforcement learning (DRL) model. To increase generalizability, we only use domain-independent features for the policy. The proposed pedagogical policy provides next-step proactive hints based on the prediction of the DRL model. We conduct a study to examine the effectiveness of the new policy in an intelligent logic tutor. Our findings provide insight into the use of DRL policies utilizing autoencoder based feature learning to determine when to provide proactive help to students.
This study proposes a method for improved image compression using a combination of discrete cosine transformation (DCT) and autoencoder. Images typically contain large amounts of data that require significant storage ...
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ISBN:
(纸本)9798350386813;9798350386820
This study proposes a method for improved image compression using a combination of discrete cosine transformation (DCT) and autoencoder. Images typically contain large amounts of data that require significant storage space, making them difficult to store and transmit. Compressing images is a practical solution to this issue as it reduces memory usage and enables faster transmission to the receiver. In this approach, we use DCT as a preprocessing step before training an autoencoder model to compress the image while retaining all essential information. The proposed method involves a convolutional neural network (CNN) that performs down-sampling and up-sampling operations on the input data processed by DCT. The performance of the proposed method is evaluated and compared with traditional image compression techniques such as JPEG, JPEG 2000, and BPG. Experimental results demonstrate that the proposed approach outperforms the traditional techniques in terms of compression ratio and image quality.
Recently, the autoencoder framework has shown great potential in reducing the feedback overhead of the downlink channel state information (CSI). In this work, we further find that the user equipment in practical syste...
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Recently, the autoencoder framework has shown great potential in reducing the feedback overhead of the downlink channel state information (CSI). In this work, we further find that the user equipment in practical systems occasionally moves in a relatively stable area for a long time, and the corresponding communication environment is relatively stable. A user-centric online training strategy is proposed to further improve CSI feedback performance using the above characteristics. The key idea of the proposed method is to train a new encoder for a specific area without changes to the decoder at the base station. Given that the CSI training samples are insufficient, two data augmentation strategies, including random erasing and random phase shift, are introduced to improve the neural network generalization. In addition, the proposed user-centric online training framework is extended to the multi-user scenario for considerable performance improvement via gossip learning, which is a fully decentralized distributed learning framework and can use crowd intelligence. The simulation results show that the proposed user-centric online gossip training offers a more substantial increase in the feedback accuracy and can considerably improve autoencoder generalization.
The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowerin...
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The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To achieve this objective, an efficient two-layer optimizer is used to evolve a lightweight deep autoencoder model by performing simultaneous selection for the input features, the training instances, and the number of hidden neurons. The optimized deep model is constructed guided by both the accuracy of a K-nearest neighbor (KNN) classifier and the complexity of the autoencoder model. To evaluate the performance of the proposed optimized model, it has been applied for the N-baiot intrusion detection dataset. Reported results showed that the proposed model achieved anomaly detection accuracy of 99% with a lightweight autoencoder model with on average input features around 30 and output hidden neurons of 2 only. In addition, the proposed two-layers optimizer was able to outperform several optimizers such as Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).
Emotion recognition from speech has its fair share of applications and consequently extensive research has been done over the past few years in this interesting field. However, many of the existing solutions aren'...
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Emotion recognition from speech has its fair share of applications and consequently extensive research has been done over the past few years in this interesting field. However, many of the existing solutions aren't yet ready for real time applications. In this work, we propose a compact representation of audio using conventional autoencoders for dimensionality reduction, and test the approach on two benchmark publicly available datasets. Such compact and simple classification systems where the computing cost is low and memory is managed efficiently may be more useful for real time application. System is evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS). Three classifiers, namely, support vector machines (SVM), decision tree classifier, and convolutional neural networks (CNN) have been implemented to judge the impact of the approach. The results obtained by attempting classification with Alexnet and Resnet50 are also reported. Observations proved that this introduction of autoencoders indeed can improve the classification accuracy of the emotion in the input audio files. It can be concluded that in emotion recognition from speech, the choice and application of dimensionality reduction of audio features impacts the results that are achieved and therefore, by working on this aspect of the general speech emotion recognition model, it may be possible to make great improvements in the future.
Hyperspectral X ray analysis is used in many industrial pipelines, from quality control to detection of low-density contaminants in food. Unfortunately, the signal acquired by X-ray sensors is often affected by a grea...
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Hyperspectral X ray analysis is used in many industrial pipelines, from quality control to detection of low-density contaminants in food. Unfortunately, the signal acquired by X-ray sensors is often affected by a great amount of noise. This hinders the performance of most of the applications building on top of these acquisitions (e.g., detection of food contaminants). Therefore, a good denoising pipeline is necessary. This article proposes a comparison between three different autoencoder variants: the Variational autoencoder, the Augmented autoencoder, and a plain vanilla autoencoder. All the networks are trained in an unsupervised fashion to denoise a given noisy spectrum. Focusing on the specific application of recognizing possible food contaminants, we force the latent space of the networks to have just two parameters, as suggested by the physical law of Lambert- Beer. We validate our experiments on a synthetic dataset composed of roughly 15 million spectra. Results suggest that the Augmented autoencoder is the best network configuration for this task, showing excellent performance without suffering from the nondeterministic behavior of the Variational autoencoder.
Recent studies verified that a genetic algorithm can discover efficient and innovative wind turbines by using image encoding and decoding techniques. To accelerate the optimization, in this work, ResidualRecursion Aut...
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Recent studies verified that a genetic algorithm can discover efficient and innovative wind turbines by using image encoding and decoding techniques. To accelerate the optimization, in this work, ResidualRecursion autoencoder (RRAE) is proposed to extract low-dimensional latent codes from rotors' crosssection images while maintaining reconstruction accuracy as high as possible. As a kind of neural network framework, the advantages of using RRAE are threefold: 1) RRAE can wrap over different kinds of autoencoders and improve their performance;2) RRAE is compatible with different kinds of loss functions and works well with very low-dimensional latent codes;3) RRAE is easy to use and efficient in decoding latent codes which is important to the rapid convergence of the genetic algorithm. The experiment results has shown that the reconstruction loss has decreased by 30.56% on a recursive autoencoder, 11.40% to 29.34% on different feedforward autoencoders. Two RRAE-accelerated optimizations have been carried out in this work. One has used only 14% of the calculation required by the baseline method without any deterioration in rotor performance. The other one has used 52.33% and increased the rotor performance by 7.59%.
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