Investigating optical properties (OPs) is crucial in the field of biophotonics. Various techniques are available for deriving OPs, with inverse Monte Carlo simulations (IMCS) being the most advanced for ex-vivo contex...
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ISBN:
(纸本)9781510673380;9781510673397
Investigating optical properties (OPs) is crucial in the field of biophotonics. Various techniques are available for deriving OPs, with inverse Monte Carlo simulations (IMCS) being the most advanced for ex-vivo contexts. However, identifying the spectral behavior of each microscopic absorber and scatterer responsible for generating these OPs requires further experimentation. To tackle this issue, a customized autoencoder neural network (ANN) is suggested. The ANN computes OPs from measurements, where the bottleneck corresponds to the number of absorbers and scatterers. The presented ANN functions asymmetrically and computes the final OPs using a linear combination of absorbers and scatterers. Consequently, the decoder's weight corresponds to the constituent's OPs spectral behavior. Validation was conducted by utilizing intralipid as a scatterer and ink as an absorber. The employment of the decoder weights facilitated the successful extraction of the spectral shape of every constituent.
Monitoring systems produce and transmit large amounts of data. For an efficient transmission, data is often compressed and autoencoders are a widely adopted neural network-based solution. However, this processing step...
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ISBN:
(纸本)9798350383638;9798350383645
Monitoring systems produce and transmit large amounts of data. For an efficient transmission, data is often compressed and autoencoders are a widely adopted neural network-based solution. However, this processing step leads to a loss of information that may negatively impact the performance of downstream tasks, such as anomaly detection. In this work, we propose a loss function for an autoencoder that addresses both compression and anomaly detection. Our key contribution is the inclusion of a regularization term based on information-theoretic quantities that characterize an anomaly detector processing compressed signals. As a result, the proposed approach allows for a better use of the communication channel such that the information preserved by the compressed signal is optimized for both detection and reconstruction, even in scenarios with lightweight compression. We tested the proposed technique with ECG signals affected by synthetic anomalies and the experiments demonstrated an average 17% increase in the probability of detection across three standard detectors. Additionally, we proved that our approach is generalizable to image data.
Monitoring systems generate and transmit large volumes of data to facilities capable of effectively performing multiple tasks. Data is often compressed and autoencoders have emerged as a promising neural network-based...
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ISBN:
(纸本)9789464593617;9798331519773
Monitoring systems generate and transmit large volumes of data to facilities capable of effectively performing multiple tasks. Data is often compressed and autoencoders have emerged as a promising neural network-based approach. This work focuses on a scenario in which the receiver performs reconstruction and anomaly detection tasks. We examine how two autoencoder-based compression strategies administer the trade-off between reconstruction and anomaly detection. The experiments consider two scenarios: ECG time series and CIFAR-10 images. Each dataset is corrupted by five anomalies with different intensities and assessed with two detectors. We highlight the pros and cons of the two approaches showing that that their efficacy depends on a specific anomaly and setting.
autoencoders, as a generative self-supervised learning, have received more and more attention in recent years in image, video, and other media-related information processing. However, Graph autoencoder (GAE) has yet t...
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ISBN:
(纸本)9798350390155;9798350390162
autoencoders, as a generative self-supervised learning, have received more and more attention in recent years in image, video, and other media-related information processing. However, Graph autoencoder (GAE) has yet to achieve the capability demonstrated by contrastive learning in the taskcentered on attribute networks. The main limitation lies in the fact that traditional autoencoder architectures require pretext tasks that align with downstream tasks, resulting in limited expressive power of the encoder. In this paper, we propose a novel separable-task generative self-supervised learning framework capable of providing high-quality representations, Split Masked autoencoder (SMAE), which unleashes the encoder's ability to extract representations through an intelligent design. Our approach focuses on unlocking the potential of the encoder by introducing encoding transfer and feature replacement strategies, thereby enabling self-supervised pretext tasks to achieve atomic separation and fully unleash the encoder's feature representation potential. We conducted extensive experiments on widely-used graph classification datasets, and the results demonstrate that SMAE outperforms state-of-the-art baselines in terms of graph classification accuracy and generation quality. Furthermore, our experimental findings show that prediction at the representation layer is more effective than original graph layer reconstruction in the field of masked graph autoencoders.
Lithofacies classification is an indispensable procedure in well logging and seismic data interpretation. We propose a novel deep classified autoencoder learning approach to identify lithofacies for high-dimensional d...
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Lithofacies classification is an indispensable procedure in well logging and seismic data interpretation. We propose a novel deep classified autoencoder learning approach to identify lithofacies for high-dimensional data and complex problems. Deep autoencoder (DAE) is an unsupervised learning method via layerwise pretraining multiple autoencoders. It can learn deep data features automatically and reconstruct the original data with a small error. Introducing sparse constraint (i.e., sparse autoencoder) potentiates the learning ability of autoencoder. On this foundation, additional regularization terms constructed by labeled samples are considered in the new DAE approach in order to boost the performance. The new method can adaptively preserve the most significant input features and remove insensitive properties to decrease computational complexity. At the same time, we embed the class information into the loss function of autoencoder to measure intraclass similarity and improve the classification accuracy. Several experiments on well data and seismic data show that the proposed method achieves promising results. Compared with the traditional deep autoencoder (DAE), the proposed method is more competitive in terms of classification accuracy and robustness.
The accuracy of water conservation assessments is crucial for formulating water resource management and ecological protection policies. However, existing methods overly rely on expert judgment and struggle with precis...
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ISBN:
(纸本)9798331540913;9798331540906
The accuracy of water conservation assessments is crucial for formulating water resource management and ecological protection policies. However, existing methods overly rely on expert judgment and struggle with precision when handling high-dimensional data. To address this, we propose a deep autoencoder-based method for evaluating water conservation functions. For the Gannan water conservation area, we developed a comprehensive evaluation index system and integrated multi-source data to create an ecological dataset. By employing a deep autoencoder model combined with convolutional neural networks and a joint training strategy, we achieved feature extraction and dimensionality reduction, mapping high-dimensional data to a low-dimensional latent space. Subsequently, we used the K-means clustering algorithm to validate the model's classification performance. The clustering accuracy and FMI based on AE-CNN extracted latent variables were significantly higher than those obtained by clustering the raw data directly. This demonstrates that the model effectively extracts data features and significantly enhances classification accuracy, providing robust support for ecological protection and water management.
The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image datasets. This paper aims to fill thi...
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ISBN:
(纸本)9798350359329;9798350359312
The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image datasets. This paper aims to fill this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary focus on autoencoders. autoencoders are widely employed for learning and constructing a new representation of a dataset, particularly for dimensionality reduction. They are also often used for generative model learning, as seen in variational autoencoders. When dealing with mixed tabular data, qualitative variables are often encoded using a one-hot encoder with a standard loss function (MSE or Cross Entropy). In this paper, we analyze the drawbacks of this approach, especially when categorical variables are imbalanced. We propose a novel metric to balance learning: a Multi-Supervised Balanced MSE. This approach reduces the reconstruction error by balancing the influence of variables. Finally, we empirically demonstrate that this new metric, compared to the standard MSE: i) outperforms when the dataset is imbalanced, especially when the learning process is insufficient, and ii) provides similar results in the opposite case.
In the evolving landscape of the Internet of Things (IoT), the susceptibility to cyber threats is widespread, emphasizing the critical need for robust security measures. This paper introduces an innovative anomaly det...
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ISBN:
(纸本)9798350309492;9798350309485
In the evolving landscape of the Internet of Things (IoT), the susceptibility to cyber threats is widespread, emphasizing the critical need for robust security measures. This paper introduces an innovative anomaly detection system based on a hybrid LSTM-autoencoder approach. Focused on protocol headers analysis in Packet Capture (PCAP) datasets, for robust anomaly detection, our model demonstrates high F1-score in anomalies detection with 99% and 96% on CICIDS2017 and on real network traffic, respectively. Refining our strategy, we address the intricacies of IoT environments, presenting a significant leap forward in intrusion detection for IoT networks.
Log anomaly detection serves as an effective approach for identifying threats. autoencoder-based detection methods address positive and negative sample imbalance issues and have been extensively adopted in practical a...
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ISBN:
(纸本)9789819709441;9789819709458
Log anomaly detection serves as an effective approach for identifying threats. autoencoder-based detection methods address positive and negative sample imbalance issues and have been extensively adopted in practical applications. However, most existing methods necessitate a sliding window to adapt to the autoencoder's base network, leading to information confusion and diminished resilience. Furthermore, detection results may be worthless when a single log comprises numerous unbalanced log records. In response, we propose TAElog, a novel framework employing a transformer-based autoencoder designed to extract precise information from logs without the need for sliding windows. TAElog also incorporates a new loss calculation that computes both high-dimensional metrics and divergence information, enhancing detection performance in intricate situations with diverse and unbalanced log records. Moreover, our framework covers preprocessing to increase the compatibility between text and numeric logs. To verify the effectiveness of TAElog, we evaluate its performance against other methods on both textual and numerical logs. Additionally, we assess various preprocessing and loss computation approaches to determine the optimal configuration within our method. Experimental results demonstrate that TAElog not only achieves superior accuracy rates but also boasts increased processing speed.
Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS...
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Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)***,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it *** data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and *** pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this *** data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder *** characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical *** use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.
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