In order to improve the intelligent energy efficiency management of ships, evaluate the fuel utilization efficiency of marine diesel engine. In this paper, a fuel consumption model of marine diesel engine based on aut...
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ISBN:
(数字)9781665408530
ISBN:
(纸本)9781665408530;9781665408523
In order to improve the intelligent energy efficiency management of ships, evaluate the fuel utilization efficiency of marine diesel engine. In this paper, a fuel consumption model of marine diesel engine based on autoencoder and deep neural network is established, and the autoencoder is used to perform nonlinear dimensionality reduction on the data to obtain more valuable data features, thereby improving the accuracy of the model. The model is verified and compared using the sailing parameters, environmental parameters and fuel consumption of the actual ship during normal sailing. The accuracy rate of the model established in this paper reaches 95.19%, and the results show that the model in this paper can meet the prediction and evaluation analysis of the energy consumption of the marine diesel engine.
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative rel...
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ISBN:
(纸本)9798400701245
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.
This paper presents a novel approach to compensate for sensor long-term drift by combining an autoencoder with a long short-term neural network (LSTM). Specifically, an autoencoder is utilized to model the sensor'...
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ISBN:
(纸本)9798350303872
This paper presents a novel approach to compensate for sensor long-term drift by combining an autoencoder with a long short-term neural network (LSTM). Specifically, an autoencoder is utilized to model the sensor's long-term drift response, and the learned latent space representation is then fed into the input of the subsequent LSTM networks for length estimation. The proposed algorithm is experimentally validated using a single kirigami flexible sensor stretched by a moving platform for length change estimation. The results demonstrate that, compared to a standard LSTM, the proposed algorithm achieves a 76% reduction in root mean square error for length estimation, with a corresponding improvement in the coefficient of determination R2 from -3.09 to 0.77.
In the era of Big Data, more and more IoT devices are generating huge amounts of high-dimensional, real-time and dynamic data streams. As a result, there is a growing interest in how to cluster this data effectively a...
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ISBN:
(纸本)9781450399449
In the era of Big Data, more and more IoT devices are generating huge amounts of high-dimensional, real-time and dynamic data streams. As a result, there is a growing interest in how to cluster this data effectively and efficiently. Although a number of popular two-stage data stream clustering algorithms have been proposed, these algorithms still have some problems that are difficult to solve in the face of real-world data streams: poor handling of high-dimensional data streams and difficulty in effective dimensionality reduction;a slow clustering process that makes it difficult to meet real-time requirements;and too many manually defined parameters that make it difficult to cope with evolving data streams. This paper proposes an autoencoder-based fast online clustering algorithm for evolving data stream(AFOCEDS). The algorithm uses a stacked denoising autoencoder to reduce the dimensionality of the data, a multi-threaded approach to improve response speed, and a mechanism to automatically update parameters to cope with evolving data streams. The experiments on several realistic data streams show that AFOCEDS outperforms other algorithms in terms of effectiveness and speed.
Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to ...
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Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples. Inspired by the success of geodesic distance approximators such as ISOMAP, we propose to use a minimum spanning tree (MST), a graph-based algorithm, to approximate the local neighborhood structure and generate structure-preserving distances among data points. We use this MST-based distance metric to replace the euclidean distance metric in the embedding function of autoencoders and develop a new graph regularized autoencoder, which outperforms a wide range of alternative methods over 20 benchmark anomaly detection datasets. We further incorporate the MST regularizer into two generative adversarial networks and find that using the MST regularizer improves the performance of anomaly detection substantially for both generative adversarial networks. We also test our MST regularized autoencoder on two datasets in a clustering application and witness its superior performance as well.
Outlier detection technologies play an important role in various application domains. Most existing outlier detection algorithms have difficulty detecting outliers that are mixed within normal object regions or around...
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Outlier detection technologies play an important role in various application domains. Most existing outlier detection algorithms have difficulty detecting outliers that are mixed within normal object regions or around dense clusters. To address this problem, we propose a novel graph neural network structure called the graph autoencoder (GAE), which is capable of handling the task of outlier detection in Euclidean structured data. The GAE can perform feature value propagation in the form of a neural network that changes the distribution pattern of the original dataset, which can accurately detect outliers with low deviation. This method first converts the Euclidean structured dataset into a graph using the graph generation module, then inputs the dataset together with its corresponding graph into the GAE for training, and finally determines the top-n objects that are difficult to reconstruct in the output layer of the GAE as outliers. The results of comparing eight state-of-the-art algorithms on eight real-world datasets showed that GAE achieved the highest area under the receiver operating characteristic curve (ROC AUC) on six datasets. By comparing GAE with the autoencoder-based outlier detection algorithm, it was discovered that the proposed method improved the AUC by 16.9% on average for eight datasets. (C) 2022 Elsevier Inc. All rights reserved.
The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown th...
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The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to serve as the fault detection network. However, misclassifying faulty samples that share similar patterns to normal samples is a common drawback of AEs. In this work, a sourceaware autoencoder (SAAE) is proposed as an extension of AEs to incorporate faulty samples in the training stage. In SAAE, flexibility in tuning recall and precision trade-off, ability to detect unseen faults and applicability in imbalanced data sets are achieved. Bidirectional long short-term memory (BiLSTM) with skip connections SAAE is designed as the structure of the fault detection network. Further, a deep network with BiLSTM and residual neural network (ResNet) is proposed for the subsequent fault diagnosis step to avoid randomness imposed by the order of the input features. A framework for combining fault detection and fault diagnosis networks is also presented without the assumption of having a perfect fault detection network. A comprehensive comparison among relevant existing techniques in the literature and SAAE-ResNet is also conducted on the Tennessee-Eastman process, which shows the superiority of the proposed FDD method. (C) 2021 Elsevier B.V. All rights reserved.
Recently, with the advance in information technology, pure data-driven approaches such as machine learnings have been widely applied in status diagnosis. However, the accuracy of those predictions strongly relies on t...
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Recently, with the advance in information technology, pure data-driven approaches such as machine learnings have been widely applied in status diagnosis. However, the accuracy of those predictions strongly relies on the original data, which largely depends on the selected sensors and signal features. Furthermore, for unsupervised machine learning schemes, although it could avoid the concern of labeling in training, it lacks a quantified evaluation of the prediction results. These concerns significantly limit the effectiveness of modern machine learning and thus should be investigated. Meanwhile, ball bearings are fundamental key machine elements in rotating machinery and their condition monitoring should be critical for both quality control and longevity assessment. In this paper, by utilizing ball bearing failure diagnosis as the main theme, the flow of feature selection and evaluation, as well as the evaluation flow for multiple failure diagnosis, is developed for accessing the status of bearings in their imbalance, lubrication, and grease contamination levels based on unsupervised machine learning. The experimental results indicated that with proper feature selection, the failure identification could be more definite. Finally, a novel model based on the second norm to quantify the classification level of each cluster in hyperspace is proposed as the measure for unsupervised machine learning as the basis for performance evaluation and optimization of unsupervised machine learning schemes and should benefit related machine reliability evaluation studies and applications.
Depth map estimation from a single RGB image is a fundamental computer vision and image processing task for various applications. Deep learning based depth map estimation has improved prediction accuracy compared with...
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Depth map estimation from a single RGB image is a fundamental computer vision and image processing task for various applications. Deep learning based depth map estimation has improved prediction accuracy compared with traditional approaches by learning huge numbers of RGB-D images, but challenging issues remain for distorted and blurry reconstruction in object boundaries because the features are not enforced during training. This paper presents a multi-view attention autoencoder embedded in a deep neural network to emphasize self-representative features, which provide robust depth maps by simultaneously accentuating useful features and reducing redundant features to improve depth map estimation performance. Qualitative and quantitative experiments were conducted to verify the proposed network effectiveness, which can be utilized for three-dimensional scene reconstruction and understanding.
Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the de...
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Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this *** the pipeline in the human brain for visual signal processing,DIM adopts a two-stage *** the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the *** by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each *** evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.
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