Ball screw is widely used in many precision *** its performance is valuable to assure safe *** it is important to study on condition-based maintenance technique of ball *** this paper,we introduce a new ball screw per...
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Ball screw is widely used in many precision *** its performance is valuable to assure safe *** it is important to study on condition-based maintenance technique of ball *** this paper,we introduce a new ball screw performance evaluation based on deep learning ***,the ball screw system acceleration performance degradation experiment is conducted to acquisition the experiment ***,the time,frequency and time-frequency domain features are extracted to represent the characteristic of vibration *** last,the denosied autoencoder deep neural network is applied to learn the deeper features and recognition the ball screw *** contrast experiments are performed,the results demonstrates the proposed method is effective in ball screw system performance evaluation.
Conventional wired systems for recording intestinal motility using strain-gauge transducers physically limit animal movement and are not ideal for long-term studies. Here, we developed a wireless recording system that...
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Conventional wired systems for recording intestinal motility using strain-gauge transducers physically limit animal movement and are not ideal for long-term studies. Here, we developed a wireless recording system that allows continuous monitoring of intestinal activity in freely moving rats. We also developed a denoising autoencoder that isolates intestinal motility signals from locomotor noise while maintaining a 10-s temporal resolution. The refined data revealed decreased intestinal motility while the rats were behaviorally active. This system has broad applications for in vivo physiological research.
Crop segmentation from the images captured in the outdoor field is a complex task in agriculture automation, let alone detecting some specific crops with one method. Cotton, as one of the four major economic crops, is...
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ISBN:
(纸本)9781467371896
Crop segmentation from the images captured in the outdoor field is a complex task in agriculture automation, let alone detecting some specific crops with one method. Cotton, as one of the four major economic crops, is of great significance to the development of the national economy. In this paper, a novel strategy based on the deep learning is utilized to establish the crop classifier in the RGB vector color space in order to realize the specific crop image segmentation. To the best of our knowledge, little research has been done to crop segmentation in the wild cotton field with digital cameras. To verify the performance of the proposed method, two specific crops (cotton plants and raw cotton) grown in the cotton field are demonstrated in this paper. Experiment results show that our method outperforms other state-of-art algorithms on cotton plants (raw cotton) segmentation in yielding the highest performance with the lowest mean square deviation. Moreover, the impact of different color spaces to the proposed method is compared. The proposed crop segmentation method can not only be used to green crop segmentation in the field, but segment specific crop like cotton.
Music embedding often causes significant performance degradation in automatic speech recognition (ASR). This paper proposes a music-removal method based on denoising autoencoder (DAE) that learns and removes music fro...
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ISBN:
(纸本)9789881476807
Music embedding often causes significant performance degradation in automatic speech recognition (ASR). This paper proposes a music-removal method based on denoising autoencoder (DAE) that learns and removes music from music-embedded speech signals. Particularly, we focus on convolutional denoising autoencoder (CDAE) that can learn local musical patterns by convolutional feature extraction. Our study shows that the CDAE model can learn patterns of music in different genres and the CDAE-based music removal offers significant performance improvement for ASR. Additionally, we demonstrate that this music-removal approach is largely language independent, which means that a model trained with data in one language can be applied to remove music from speech in another language, and models trained with multilingual data may lead to better performance.
The parametric Bayesian Feature Enhancement (BFE) and a datadriven denoising autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different...
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Specific emitter identification (SEI) is pivotal for ensuring the security of the Internet of Things (IoT). Traditional deep learning-based SEI techniques often falter in real-world applications, particularly when dis...
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Specific emitter identification (SEI) is pivotal for ensuring the security of the Internet of Things (IoT). Traditional deep learning-based SEI techniques often falter in real-world applications, particularly when distinguishing between legitimate and rogue devices amid noisy conditions and low-signal-to-noise ratios (SNRs). To surmount these challenges, we propose a novel open-set SEI (OS-SEI) strategy that utilizes a metric-enhanced denoising autoencoder (MeDAE) architecture. This advanced framework incorporates a deep residual shrinkage network, significantly augmenting the denoising autoencoder's capability, thereby bolstering its resilience against noisy environments. Further, the integration of discriminative metrics, such as center loss, markedly enhances feature discrimination, resulting in heightened accuracy of device identification. Our comprehensive experimental assessments, conducted on an automatic dependent surveillance-broadcast (ADS-B) data set, underscore the superiority of our proposed OS-SEI method over existing models. The findings confirm our approach's enhanced robustness to noise and its superior accuracy in device identification within open-set scenarios.
Masked autoencoder (MAE) has demonstrated its effectiveness in self-supervised point cloud learning. Considering that masking is a kind of corruption, in this work we explore a more general denoising autoencoder for p...
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Masked autoencoder (MAE) has demonstrated its effectiveness in self-supervised point cloud learning. Considering that masking is a kind of corruption, in this work we explore a more general denoising autoencoder for point cloud learning (Point-DAE) by investigating more types of corruptions beyond masking. Specifically, we degrade the point cloud with certain corruptions as input, and learn an encoder-decoder model to reconstruct the original point cloud from its corrupted version. Three corruption families (i.e., density/masking, noise, and affine transformation) and a total of 14 corruption types are investigated with traditional non-Transformer encoders. Besides the popular masking corruption, we identify another effective corruption family, i.e., affine transformation. The affine transformation disturbs all points globally, which is complementary to the masking corruption where some local regions are dropped. We also validate the effectiveness of affine transformation corruption with the Transformer backbones, where we decompose the reconstruction of the complete point cloud into the reconstructions of detailed local patches and rough global shape, alleviating the position leakage problem in the reconstruction. Extensive experiments on tasks of object classification, few-shot learning, robustness testing, part segmentation, and 3-D object detection validate the effectiveness of the proposed method. The codes are available at https://***/YBZh/Point-DAE
Deep learning models have achieved groundbreaking results in computer vision;however, their vulnerability to adversarial examples persists. Adversarial examples, generated by adding minute perturbations to images, lea...
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Deep learning models have achieved groundbreaking results in computer vision;however, their vulnerability to adversarial examples persists. Adversarial examples, generated by adding minute perturbations to images, lead to misclassification and pose serious threats to real-world applications of deep learning models. This paper proposes a simple, powerful, and efficient adversarial defense method: a Siamese network-based denoising autoencoder (Siamese-DAE). This method addresses the reduction in classification accuracy caused by the denoising process. Experiments on Chest X-ray, Brain MRI, Retina, and Skin images, using FGSM, PGD, DeepFool, CW, SPSA, and AutoAttack adversarial algorithms, demonstrate that the Siamese-DAE, trained to remove noise, effectively eliminates perturbations, leading to improved classification accuracy compared not only to the standard classification model but also to relevant denoising defense models.
Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the contex...
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Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model's input, during inference (i.e., the adversarial perturbation is injected into inputs during deployment after the model has been trained) that are specifically crafted to force the trained regression model to output an infeasible power allocation solution. In this work, we develop an autoencoder-based mitigation technique, which allows deep learning-based power allocation models to operate in the presence of adversaries without requiring retraining. Specifically, we develop a denoising autoencoder (DAE), which learns a mapping between potentially perturbed data and its corresponding unperturbed input. We test our defense across multiple attacks and in multiple threat models and demonstrate its ability to (i) mitigate the effects of adversarial attacks on power allocation networks using two common precoding schemes, (ii) outperform previously proposed benchmarks for mitigating regression-based adversarial attacks on maMIMO networks, (iii) retain accurate performance in the absence of an attack, and (iv) operate with low computational overhead. Code is publicly available at https://***/Jess-jpg-txt/DAE_for_adv_attacks_in_MIMO.
Drug-target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug-target interactions at low costs and with reasonable accuracy. Recent studies pay more attention...
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Drug-target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug-target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI prediction. Since the interaction matrix is often extremely sparse, DTI prediction performance is significantly decreased with matrix factorization-based methods. Therefore, some matrix factorization methods utilize side information to address both the sparsity issue of the interaction matrix and the cold-start issue. By combining matrix factorization and autoencoders, we propose a hybrid DTI prediction model that simultaneously learn the hidden factors of drugs and targets from their side information and interaction matrix. The proposed method is composed of two steps: the pre-processing of the interaction matrix, and the hybrid model. We leverage the similarity matrices of both drugs and targets to address the sparsity problem of the interaction matrix. The comparison of our approach against other algorithms on the same reference datasets has shown good results regarding area under receiver operating characteristic curve and the area under precision-recall curve. More specifically, experimental results achieve high accuracy on golden standard datasets (e.g., Nuclear Receptors, GPCRs, Ion Channels, and Enzymes) when performed with five repetitions of tenfold cross-validation. [GRAPHICS] .
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