With each new generation of mobile communication, the mobile traffic data are increasing exponentially. However, there are limitations to the capacity of base stations. As a result, it become necessary to manage mobil...
详细信息
The automated classification of breast cancer histopathological images is one of the important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of small inter-class and large intra-class va...
详细信息
The automated classification of breast cancer histopathological images is one of the important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of small inter-class and large intra-class variances in breast cancer histopathological images, extracting features for breast cancer classification is difficult. To address this problem, an improved autoencoder (AE) network using a Siamese framework that can learn the effective features from histopathological images for CAD breast cancer classification tasks was designed. First, the inputted image is processed at multiple scales using a Gaussian pyramid to obtain multi-scale features. Second, in the feature extraction stage, a Siamese framework is used to constrain the pre-trained AE so that the extracted features have smaller intra-class variance and larger inter-class variance. Experimental results show that the proposed method classification accuracy was as high as 97.8% on the BreakHis dataset. Compared with commonly used algorithms in breast cancer histopathological classification, this method has superior, faster performance.
Reconstruction of gene regulatory networks from gene expression profile have been an important challenge task in system biology for decades. Recently, with the advancement of single cell RNA-seq technology, the studie...
详细信息
With substantial recent developments in aviation technologies, unmanned aerial vehicles (UAVs) are becoming increasingly integrated in commercial and military operations internationally. Research on the applications o...
详细信息
With substantial recent developments in aviation technologies, unmanned aerial vehicles (UAVs) are becoming increasingly integrated in commercial and military operations internationally. Research on the applications of aircraft data is essential in improving safety, reducing operational costs, and developing the next frontier of aerial technology. Having an outlier detection system that can accurately identify anomalous behavior in aircraft is crucial for these reasons. This article proposes a system incorporating a long short-term memory (LSTM) deep learning autoencoder-based method with a novel dynamic thresholding algorithm and weighted loss function for anomaly detection of a UAV dataset, in order to contribute to the ongoing efforts that leverage innovations in machine learning and data analysis within the aviation industry. The dynamic thresholding and weighted loss functions showed promising improvements to the standard static thresholding method, both in accuracy-related performance metrics and in speed of true fault detection.
Metamodel-assisted optimization is a frequently applied approach for structural design optimization problems. Here, a data-driven metamodel approximates the computationally expensive simulation results of first princi...
详细信息
Metamodel-assisted optimization is a frequently applied approach for structural design optimization problems. Here, a data-driven metamodel approximates the computationally expensive simulation results of first principle models, e.g., finite element analyses. A significant drawback of typical metamodels is the limited amount of information that can be predicted due to their generally low-dimensional model output. Consequently, the metamodel usually does not predict the distribution of the desired quantity. This work presents a metamodel approach capable of predicting the spatial and temporal distribution of quantities for structural processes. This increases the modeling capability and makes more information available for the optimization. The autoencoder compresses the spatial distribution into a couple of features. The proposed methodology is applied to a three-stage forming process.
The timely and quantitative evaluation of the degradation is crucial for traction inverter systems in railway applications. The implementation in the industry is impeded by two major challenges including the varying o...
详细信息
In the context of discrete-event systems (DES), the terms detection and diagnosis refer to two distinct stages of handling faults and anomalies. Both steps are critical for ensuring the reliable and safe operation of ...
详细信息
In the context of discrete-event systems (DES), the terms detection and diagnosis refer to two distinct stages of handling faults and anomalies. Both steps are critical for ensuring the reliable and safe operation of complex systems. In this paper, we propose the use of autoencoders for fault detection in an automated production system with sensors and actuators delivering discrete binary signals that can be modeled as DES. We train an autoencoder exclusively on data representing normal behavior. The model learns to encode typical patterns and reconstruct input data with low loss. A predetermined threshold, determined by the characteristics of the training data, is set for the reconstruction error. During normal behavior, the autoencoder is expected to achieve low reconstruction error below this threshold. When a fault occurs, the autoencoder strives to accurately reconstruct faulty data, leading to a higher error. The detection of a reconstruction error exceeding the threshold signals a potential fault in the system. The results of applying our method to the Factory IO software sorting system demonstrate the significant contribution and the interest of this method for detecting faults.
A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity o...
详细信息
A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by using separable convolutional layers. In the proposed structure of the dual autoencoder, the first autoencoder aims to denoise the image, while the second one aims to enhance the quality of the denoised image. The research includes Gaussian noise (Gaussian blur), Poisson noise, speckle noise, and random impulse noise. The advantages of the proposed neural network are the number reduction in the trainable parameters and the increase in the similarity between the denoised or deblurred image and the original one. The similarity is increased by decreasing the main square error and increasing the structural similarity index. The advantages of a dual autoencoder network with separable convolutional layers are demonstrated by a comparison of the proposed network with a convolutional autoencoder and dual convolutional autoencoder.
Mode shape is a dynamic characteristic that plays an important role in civil engineering. In this paper, an approach to predict the mode shape of a bridge is proposed using a convolutional neural network (CNN) and an ...
详细信息
Mode shape is a dynamic characteristic that plays an important role in civil engineering. In this paper, an approach to predict the mode shape of a bridge is proposed using a convolutional neural network (CNN) and an autoencoder. First, a large mode shape database of a bridge is established by the finite element method for training networks. Second, a mode shape tensor is formed based on the mode-shape results. Then, an autoencoder is trained to encode the tensor to a three-dimensional latent-space representation and restore it from the representations. The CNN can output the representation directly rather than the mode shape to reduce the training difficulty and improve the accuracy. The CNN takes 18 bridge design parameters and an original shape tensor, which is constructed based on 16 geometric parameters. An evaluation of the test set shows that the approach can predict the first three order mode shapes well, with the accuracy of 0.92, 0.83 and 0.79, while performs poorly in the fourth and fifth orders, with the accuracy of 0.68 and 0.63. In addition, the spatial distribution of the latent space representation is explored. The necessity of an autoencoder and the original shape tensor is demonstrated.
Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data ...
详细信息
Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users' characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.
暂无评论