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Spectrum Sensing Meets ISAC: An Spectrum Detection Scheme for ISAC Services Based on Improved denoising auto-encoder and CNN

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3381-3381页

作者： Li, Yuebo Song, Hengguo Ren, Xiaoyang Zhang, Zhiyue Cheng, Sichao Jing, Xiaojun Beijing Univ Posts & Telecommun Informat & Commun Engn Beijing 100876 Peoples R China China Acad Informat & Commun Technol Informat & Commun Ctr Beijing 100876 Peoples R China

Integrated Sensing and Communications (ISAC) has attracted increasing attention due to more efficient utilization of both radio spectrum and hardwares. However, ISAC can only relieve the shortage of the spectrum, especially in the situation of exponential growth of wireless terminals. Efficient spectrum utilization strategy is still an important direction for the continuous evolution of wireless communication technology. As such, spectrum sensing (SS) is discussed in ISAC scenarios, and a novel cooperative SS scheme is proposed by as an improved auto-encoder for more efficient spectrum utilization. More specifically, the parameters of each local spectrum spectrum-sensing network are encoded and sent to the central server, and the local network parameters are decoded, fused, and returned to each local node at the central server. The simulations are designed, and the experiment results demonstrate the effectiveness of the proposed scheme.

关键词： Integrated Sensing and Communications spectrum sensing denoising auto-encoder

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COLLABORATIVE FILTERING ALGORITHM BASED ON DEEP denoising auto-encoder AND ATTENTION MECHANISM

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COMPUTING AND INFORMATICS 2025年第1期44卷 176-201页

作者： Han, Zixuan Shi, Leilei Sun, Qiang Huang, Xiuliang Lei, Bing Liu, Lu Lu, Yao Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China Jiangsu Univ Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang Peoples R China Univ Exeter Dept Comp Sci Exeter England Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

The burgeoning of e-commerce and online platforms has led to an explosion in data volume and diversity of user preferences, making effective recommendation systems crucial for personalizing user experiences. While collaborative filtering algorithms are traditionally favoured for their ability to leverage user-item interactions, they grapple with data sparsity and noise challenges. To tackle these challenges, Various approaches have emerged in recent years to tackle these chal lenges. Recent strides in deep learning, particularly autoencoders and neural networks, have shown promise in addressing these issues. However, limitations persist, such as suboptimal feature extraction and the underutilization of combined nonlinear and linear latent features in traditional autoencoders, as well as the overlooked impact of active users in recommendations. Addressing these research gaps, this study introduces a novel recommendation algorithm that synergizes a deep denoising autoenco der with an attention mechanism, aiming to refine recommendation performance by mitigating data sparsity and enhancing feature extraction. This fusion approach innovatively combines nonlinear and linear latent features and incorporates a neural attention mechanism, significantly improving the precision and personalization of recommendations. Ultimately, the proposed algorithm's effectiveness is assessed and benchmarked against state-of-the-art approaches, demonstrating its potential to revolutionize recommendation systems by offering more accurate and user-tailored suggestions.

关键词： Deep learning denoising auto-encoder collaborative filtering attention mechanism recommendation system

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denoising auto-encoder Based Image Enhancement For High Resolution Sonar Image

Denoising Auto-Encoder Based Image Enhancement For High Reso...

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IEEE OES International Symposium on Underwater Technology (UT)

作者： Kim, Juhwan Song, Seokyong Yu, Son-Cheol Pohang Univ Sci & Technol POSTECH Dept Creat IT Engn Pohang 37673 Gyeongbuk South Korea

ISBN: (纸本)9781509052660

A typical sonar image has a plenty of random noise compared to an optical image. Due to poor picture quality, there is a large restriction on recognizing any object. Pattern recognition is exceedingly difficult not only in computer image processing but even in human eyes. Numerous researchers have attempted to apply various types of average filters to sonar images, and have also removed noise by using multiple images in succession. However, each of the algorithms has a limitation in that the resolution of the image itself is degraded or the image of the object is difficult to remove noise. Finally, We performed sonar image noise reduction with the auto-encoder algorithm based on convolutional neural network, which as recently been attracting attention. With the algorithm, we obtained sonar images of superior quality with only a single continuous image. We simply learned a ton of sonar images in a neural network of auto-encoder structures, and then we could get the results by injecting the original sonar images. We verified the results of image enhancement using the acoustic lens based multibeam sonar images.

关键词： sonar image noise reduction image enhancement denoising auto-encoder machine learning

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denoising auto-encoder based State Estimation of Distribution Systems Under Natural Disaster 8

Denoising Auto-Encoder based State Estimation of Distributio...

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8th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Du, Ying Liu, Yadong Wang, Xuhong Yan, Yingjie Fang, Jian Zhang, Hang Jiang, Xiuchen Shanghai Jiao Tong Univ State Energy Smart Grid Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Guangzhou Power Supply Bur Co Ltd Elect Power Test & Res Inst Guangzhou Peoples R China

ISBN: (纸本)9781728159317

Under natural disasters, confirming the failure location of distribution systems is the basis for post-disaster repair work. We can estimate the state of distribution systems by collecting and analyzing real-time data from sensing devices installed in the distribution systems. However, due to the destructive nature of the natural disaster, the collected data often in a missing mode. The reason for the missing data is not only the damage of monitored object but also the damage of the monitoring devices and the failure of communication systems. As a result, the collected data are in the form of limited credibility, leading to the difficulty of failure location for distribution systems under nature disaster. In this paper, a denoising auto-encoder based state estimation method is proposed to solve the problem. By adding a process of missing data randomly in the training phase, the scenario of the monitored object damage can be identified, and then the state of distribution systems can be estimated effectively. The superior performance of the proposed method is verified through simulation and experiments.

关键词： disaster failure location denoising auto-encoder data missing

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A Stacked Multi-Granularity Convolution denoising auto-encoder

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IEEE ACCESS 2019年 7卷 83888-83899页

作者： Yang, Yun Cao, Lijuan Liu, Qing Yang, Po Yunnan Univ Natl Pilot Sch Software Kunming 650091 Yunnan Peoples R China Kunming Key Lab Data Sci & Intelligent Comp Kunming 650500 Yunnan Peoples R China Liverpool John Moores Univ Dept Comp Sci Liverpool L3 5UX Merseyside England

With the development of big data, artificial intelligence has provided many intelligent solutions to urban life. For instance, an image-based intelligent technology, such as image classification of diseases, is widely used in daily life. However, the image in real life is mostly unlabeled, so the performance of many image-based intelligent models shows limitations. Therefore, how to use a large amount of unlabeled image data to build an efficient and high-quality model for better urban life has been an urgent research topic. In this paper, we propose an unsupervised image feature extraction method that is referred to as a stacked multi-granularity convolution denoising auto-encoder (SMGCDAE). The algorithm is based on a convolutional neural network (CNN), yet it introduces a multi-granularity kernel. This approach resolved issues with image unicity by extracting a diverse category of high-level features. In addition, the denoising auto-encoder ensures stability and improves the classification accuracy by extracting more robust features. The algorithm was assessed using three image benchmark datasets and a series of meningitis images, achieving higher average accuracy than other methods. These results suggest that the algorithm is capable of extracting more discriminative high-level features and thus offers superior performance compared with the existing methodologies.

关键词： Unsupervised learning feature extraction denoising auto-encoder convolutional neural network

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Operating performance assessment based on stacked performance-relevant enhanced denoising auto-encoder for industrial processes

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第4期102卷 1509-1521页

作者： Liu, Yan Ma, Zhe Wang, Fuli Ma, Ruicheng Chu, Fei Li, Xinghua Guan, Changliang Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Liaoning Univ Sch Math & Stat Shenyang Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China Zijin Zhikong Xiamen Technol Co Ltd Xiamen Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

As an effective way to ensure the economic benefits of enterprises, process operating performance assessment has attracted more and more attention from industry and academia in recent years. In this paper, a stacked performance-relevant enhanced denoising autoencoder (SPEDAE) network is designed for the operating performance assessment of industrial processes. Compared to the original denoising auto-encoder (DAE), each performance-relevant enhanced denoising auto-encoder (PEDAE) not only reconstructs the input features in the output layer, but also strives to reconstruct the original input data and the performance grade labels simultaneously. Then the SPEDAE is formed by stacking multiple PEDAEs layer by layer. Through this improved training strategy, SPEDAE can avoid accumulated information loss during the deep feature extraction process, improve the robustness of the network, and extract features closely related to the operating performance, thereby better completing the assessment task. The effectiveness of the proposed assessment method is validated on the case of gold cyanide leaching process. Compared with several methods, the proposed SPEDAE has the highest accuracy and reaches 99.85%, which demonstrates its superiority in operating performance assessment.

关键词： deep learning denoising auto-encoder gold cyanide leaching operating performance assessment performance-relevant feature stacked performance-relevant enhanced denoising auto-encoder

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Single Image Super-Resolution via Perceptual Loss Guided by denoising auto-encoder 1

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15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) / 15th Pacific Rim Knowledge Acquisition Workshop (PKAW)

作者： Niu, Zhong-Han Liu, Lu-Fei Zhang, Kai-Jun Dong, Jian-Feng Yang, Yu-Bin Mao, Xiao-Jiao Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (数字)9783319973043

ISBN: (纸本)9783319973043;9783319973036

Image restoration is a difficult task due to its non-uniqueness of solution. Owing to the power of Convolution Neural Networks (CNNs), we can generate images with high PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity index) by using a per-pixel loss between the outputs and the ground-truth images. Unfortunately, these images usually suffer from over-smoothing problem. The previous perceptual loss based on high-level features was then proposed to replace the traditional per-pixel loss. But the low-level information such as color, shape and texture will be lost. In order to alleviate this problem, we design a new perceptual loss extracted from a pre-trained denoising auto-encoder with symmetric skip connections (SDAE). The encoder in SDAE is extracted as a perceptual function. We carry out the experiments on single image super-resolution to validate the proposed method. The results show that the images generated by our method have both better visual quality and higher PSNR and SSIM than the state-of-the-art methods.

关键词： Image restoration Convolutional neural network Perceptual loss denoising auto-encoder Skip connection

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Deep Adaptive denoising auto-encoder Networks for ECG Noise Cancellation via Time-Frequency Domain

Deep Adaptive Denoising Auto-Encoder Networks for ECG Noise ...

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14th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Mohammadisrab, Amir Aghaomidi, Poorya Mazloum, Jalil Akbarzadeh, Mohammad Ali Orooji, Mahdi Mokari, Nader Yanikomeroglu, Halim Tarbiat Modares Univ Tehran Iran Shahid Sattari Aeronaut Univ Sci & Technol Tehran Iran Shahid Beheshti Univ Med Sci Tehran Iran Univ Calif Davis Davis Davis CA USA Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

In this paper, we study the performance of a deep adaptive denoising auto-encoder network (DeepADAENet) for electrocardiogram (ECG) signal noise cancelation in the time-frequency domain for practical use cases. In order to achieve a higher resolution in distinguishing the noise from valuable data, the fractional Stockwell transform (FrST) is exploited to convert the ECG to the time-frequency image. The magnitude of the time-frequency version of the ECG is noise-canceled using DeepADAENet. Then, inverse FrST is utilized to return the denoised time-frequency ECG into the time domain. Furthermore, we use the MIT-BIH Apnea-ECG database (APNEA-ECG) for preparing the dataset due to various physiologies and records compared with other ECG databases. Moreover, muscle artifacts (MA), baseline wander (BW), and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) are utilized to make noisy this clean dataset. The ECG signals recorded by non-clinical devices contain more noise than clinical recording. Accordingly, by changing the coefficient and frequency of noise resources, we attempt to close the simulated noisy signal to reality. Results reveal the excellent performance of DeepADAENet compared with similar work in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD).

关键词： denoising auto-encoder Time-Frequency Image Electrocardiogram Noise Cancellation Non-Clinical Recorders

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A Novel denoising auto-encoder-Based Approach for Non-Intrusive Residential Load Monitoring

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ENERGIES 2022年第6期15卷 2290页

作者： He, Xi Dong, Heng Yang, Wanli Hong, Jun Hunan Inst Technol Dept Elect & Informat Engn Hengyang 421002 Peoples R China Hunan Univ Dept Elect & Informat Engn Changsha 410006 Peoples R China

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising auto-encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.

关键词： load disaggregation denoising auto-encoder REDD dataset TraceBase dataset machine learning

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Classification of power loads based on an improved denoising deconvolutional auto-encoder

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APPLIED SOFT COMPUTING 2020年 87卷 105959-000页

作者： Wu, Jianhua Liu, Jiahan Ma, Jian Chen, Kexu Xu, Chunhua Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China State Grid Jiangxi Elect Power Co Elect Power Sci Inst Nanchang 330096 Jiangxi Peoples R China

Machine learning has a wide range of applications in the recognition of power loads (PLs). In the light of the problems, such as poor generalization and the ease of falling into the local optima existing in the current PL classification algorithms, an improved algorithm based on the denoising deconvolutional auto-encoder was proposed to classify the field PL data. With the mirror symmetric structure of the network, the convolutional module can extract the distinctive features, while the deconvolutional module can reduce data redundancy and maintain high activation pixels. The data preprocessing accomplishes data dimensionality reduction. In order to accelerate convergence and improve classification accuracy, the unsupervised pre-training and l(2), regularization were used. The experimental results in the field data of a provincial power grid demonstrate that the proposed algorithm has a better generalization performance and a higher recognition rate than other algorithms, thus providing an efficient and objective way for PLs recognition. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Deconvolution denoising auto-encoder Unsupervised pre-training Power load recognition Power quality

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