In this study, we propose a novel autoencoder framework based on orthogonal projection constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets. Orthogonal projection is useful to capture ...
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In this study, we propose a novel autoencoder framework based on orthogonal projection constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets. Orthogonal projection is useful to capture the null subspace that consists of noisy information for AD, which is explicitly ignored in the existing approaches. The exploration of double subspaces, called normal space (NS) and abnormal space (AS) can improve the discriminative manifold information. Therefore, in this study, autoencoder framework based on the OPC learning method is proposed that combines the orthogonal subspace score and the reconstruction error score in the target tasks for AD. To the best of our knowledge, this is the first study that introduces an autoencoder-based model with two orthogonal subspaces for AD. Through the orthogonality, the anomaly-free data and abnormalnnosiy information are projected into the NS and the AS, respectively. Thus, it potentially addresses the problem of the distribution of generative model by combining the abilities of two subspaces that can appropriately learn the features and establish a strict boundaries around the normal data. For image datasets, we propose a convolutional autoencoder based on OPC. Additionally, the generalization and adaptability of the proposed method in AD was investigated using vector datasets by implementing a fully-connected layer-based OPC in the encoder-decoder structure. The effectiveness of the proposed framework for AD was evaluated through the comparison with state-of-the-art approaches. (c) 2021 Elsevier B.V. All rights reserved.
This paper presents one possible approach for selecting similar days set for the artificial neural network (ANN)-based Short Term Load Forecasting method. The standard similar day forecast approach finds the best fit ...
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ISBN:
(纸本)9781665487788
This paper presents one possible approach for selecting similar days set for the artificial neural network (ANN)-based Short Term Load Forecasting method. The standard similar day forecast approach finds the best fit for the forecasted days' load based on the weather, load, or other factors. That approach is not accurate enough and finding the best fit excludes valuable information for the rest of the days in history. Moreover, if only the basic idea of this approach is used, only a narrow set of similar days is selected, which is unsuitable for ANN training. The general idea is to compare all days instead of choosing the best days. This approach provides a more flexible approach for selecting a proper training dataset for ANN. The proposed method uses an autoencoder to code all the days in history and enables comparison to the forecast day. The selection of the days is made using the Euclidian norm. The two vector distances between the forecast day code and the codes from the history are calculated using the Euclidian norm. Then the whole history is sorted by the value of the distance. Only the part with the most similar days of the initial set is used for training the ANN. Results on the test set showed that metrics improved when ANN is trained on similar days set that is selected using the proposed method. The proposed method determines enough days for the ANN training procedure and helps ANN to learn the correlation between load and other input factors optimally.
With the rapid development of modern communication technology, telecom fraud has been increasing year by year. If fraudsters can be accurately identified before they carry out their scams, it can not only protect peop...
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ISBN:
(纸本)9798350307887
With the rapid development of modern communication technology, telecom fraud has been increasing year by year. If fraudsters can be accurately identified before they carry out their scams, it can not only protect people from potential losses but also increase trust in telecom operators. Therefore, in recent years, telecom fraud detection has garnered widespread attention in both academia and industry. Although existing methods for telecom fraud detection have achieved good performance, there are still many unresolved issues for real world telecom operators. First, existing methods only focus on a single telecom scenario, while real -world telecom scenarios are diverse. Utilizing the characteristics of these different telecom scenarios can improve the effectiveness of telecom fraud detection. Second, existing methods usually use Graph Neural Networks (GNNs) to aggregate neighbor information. However, real-world telecom operators can't obtain information of users from other operators, resulting in the lacking destination node attributes, which degenerates the performance of GNNs. To address the above issues, in this paper, we propose a new model for Telecom Fraud Detection Based on Feature binning and autoencoder (TFD-FA). In TED -FA, a feature binning framework is designed to partition users into different telecom scenarios in order to reflect their unique characteristics. An autoencoder component is also designed to aggregate neighbor information. Furthermore, an imbalance classifier component is constructed to solve the problem of the significantly lower number of fraudsters compared to normal users. Extensive experiments in a real world dataset demonstrate the effectiveness of TED -FA, which outperforms the compared baseline models.
Denoising sound is essential for improving signal quality in various applications such as speech processing, sound event classification, and machine failure detection systems. This paper proposes an autoencoder method...
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ISBN:
(纸本)9798350323078
Denoising sound is essential for improving signal quality in various applications such as speech processing, sound event classification, and machine failure detection systems. This paper proposes an autoencoder method to remove two types of noise, Gaussian white noise, and environmental noise from water flow, from induction motor sounds. The method is trained and evaluated on a dataset of 246 sounds from the Machinery Fault Database (MAFAULDA). The denoising effectiveness is measured using the mean square error (MSE), which indicates that both noise types can be significantly reduced with the proposed method. The MSE is below or equal to 0.15 for normal operation sounds and misalignment sounds. This improvement in signal quality can facilitate further processing, such as induction motor operation classification. Overall, this work presents a promising approach for denoising machine sounds using an autoencoder, with potential for application in other industrial settings.
The seizure early warning devices based on multichannel EEG signals is one of the most used assisted-living strategies for drug-resistant epileptic patients. One of the challenges in the development of these devices i...
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During the past decades, significant progress has been made in the field of artificial neural networks to process images (Convolutional Neural Networks), audio signals (Temporal Convolutional Networks), or textual inf...
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Due to the escalating utilization of communication networks and the prevailing occurrence of cyber attacks, intrusion detection systems (IDSs) have emerged as imperative components in network security. Machine learnin...
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ISBN:
(纸本)9798350310900
Due to the escalating utilization of communication networks and the prevailing occurrence of cyber attacks, intrusion detection systems (IDSs) have emerged as imperative components in network security. Machine learning (ML) and deep learning (DL) based IDSs have gained popularity due to their detection capability and adaptability. However, this type of schemes are susceptible to adversarial attacks, which involve minor perturbations to attack features causing misclassification. autoencoders (AEs) have proven effective in mitigating adversarial attacks in computer vision, but their capacity for enhancing IDSs remains relatively unexplored. In this paper, we focus on the use of AEs to detect adversarial network flows. Specifically, we propose an AE-enhanced IDS (AE-IDS) that leverages the power of AEs to improve the robustness of IDSs against adversarial attacks. Our experimental results indicate that AE-IDS outperforms the baseline schemes under investigation in terms of accuracy and detection rate. We believe that AE-IDS showcases the potential of using AEs to enhance the robustness of IDSs, providing improved security against sophisticated and evolving cyber threats.
Smart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring...
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ISBN:
(纸本)9798350311259
Smart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring of the components over time can help industries to maintain a sustainable production environment. On the contrary, a faulty system may degrade production efficiency and increase the cost. Smart packaging machines comprising several sensors can generate time series data and leverage data driven condition monitoring models to overcome faulty conditions. In this work, we have studied the application of autoencoder as a data driven condition monitoring tool for the predictive maintenance of packaging machines. The trained autoencoder on the new system's data can detect worn or degraded components over time. We have also used the Bayesian optimization algorithm to tune the hyper-parameters of the autoencoder for better predictive performance. Moreover, the reconstruction error is analyzed to identify the worn components in the packaging machine.
Orchard tree inventory has been an essential step to obtain up-to-date information for effective tree treatments and crop insurance purposes. Inventorying trees is often performed manually through fieldwork surveys, w...
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ISBN:
(纸本)9798350341393
Orchard tree inventory has been an essential step to obtain up-to-date information for effective tree treatments and crop insurance purposes. Inventorying trees is often performed manually through fieldwork surveys, which are generally time-consuming, costly, and subject to errors. Motivated by the latest advances in UAV imagery and deep learning, we propose a new framework for individual tree detection and health assessment. We adopt a divide-and-conquer approach to address the problem of orchard trees' health assessment in two stages. First, we build a tree detection model based on a hard negative mining strategy to improve object detection. In the second stage, we address the health classification problem using a new convolutional autoencoder architecture mainly designed to extract relevant features. The performed experiments demonstrate the robustness of the proposed framework for orchard tree health assessment from UAV images. In particular, our framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 98.06% for tree health assessment. Moreover, our work could be generalized for a wide range of UAV applications involving a detection/classification process.
We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics....
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We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexible nonlinear function of covariates. Our model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature - autoencoder neural networks - to incorporate information from covariates along with returns themselves. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models. (c) 2020 Elsevier B.V. All rights reserved.
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