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IEEE International Conference on Communications (ICC)

作者： Olowononi, Felix O. Rawat, Danda B. Kamhoua, Charles A. Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA US Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665426718

Advancements in information and communication technologies have contributed significantly to the optimization of next generation wireless communications networks. Moreover, wireless communications play a huge role in electronic warfare and the emergence of new technologies continue to provoke a corresponding revolution in battlefield operations. Nevertheless, the widened attack surface that result from the increased wireless networking of battlefield devices lure attackers like jammers and eavesdroppers who seek to explore the vulnerability of the network through the physical layer. Consequently, it is imperative to direct research efforts not only towards advancing spectral and energy efficiency but also secure wireless communications. In order to address the challenges, we leverage on the recent developments in the use of machine learning (ML) for wireless communications to propose a novel approach for secure waveform transmission in battlefield operations. Specifically, we use the learning-based end-to-end representation of communication systems where the transmitter and receiver are represented by two deep neural networks (DNN). The goal is to prevent the eavesdropper from accessing the communications between the transmitter and the receiver without necessarily a feedback mechanism as in the conventional communication system. In comparison to traditional optimization methods that are usually iterative in nature, the advantages of employing deep learning (DL) includes its ability to address the challenges of statistically inclined methods. Using simulations, we compare the proposed method with other methods to ascertain its credibility.

关键词： Battlefield communications deep autoencoder Wireless security

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deep 3D reconstruction:methods,data,and challenges

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Frontiers of Information Technology & Electronic Engineering 2021年第5期22卷 652-672页

作者： Caixia LIU Dehui KONG Shaofan WANG Zhiyong WANG Jinghua LI Baocai YIN Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Multimedia Laboratory School of Computer ScienceUniversity of SydneySydney NSW 2006Australia

Three-dimensional(3D)reconstruction of shapes is an important research topic in the fields of computer vision,computer graphics,pattern recognition,and virtual *** 3D reconstruction methods usually suffer from two bottlenecks:(1)they involve multiple manually designed states which can lead to cumulative errors,but can hardly learn semantic features of 3D shapes automatically;(2)they depend heavily on the content and quality of images,as well as precisely calibrated *** a result,it is difficult to improve the reconstruction accuracy of those methods.3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep ***,while these methods have various architectures,in-depth analysis and comparisons of them are unavailable so *** present a comprehensive survey of 3D reconstruction methods based on deep ***,based on different deep learning model architectures,we divide 3D reconstruction methods based on deep learning into four types,recurrent neural network,deep autoencoder,generative adversarial network,and convolutional neural network based methods,and analyze the corresponding methodologies ***,we investigate four representative databases that are commonly used by the above methods in ***,we give a comprehensive comparison of 3D reconstruction methods based on deep learning,which consists of the results of different methods with respect to the same database,the results of each method with respect to different databases,and the robustness of each method with respect to the number of ***,we discuss future development of 3D reconstruction methods based on deep learning.

关键词： deep learning models Three-dimensional reconstruction Recurrent neural network deep autoencoder Generative adversarial network Convolutional neural network

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Unsupervised Representation Learning Method for UAV's Scene Perception

Unsupervised Representation Learning Method for UAV's Scene ...

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9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Yao Kaidi Ma Zhaowei Lei Jinhong Shen Sibo Zhu Yulin PLA Unit 63981 Wuhan Hubei Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781538665657

In view of the application requirements for VAV in forest rescue and border patrol, this paper takes a single small VAV equipped with visual sensors as the research object. Combined the encoder and decoder structure in the field of deep learning, a deep coding algorithm for scene perception is put forward. Based on the autoencoder, the algorithm achieves the dimension reduction and scene reconstruction by increasing the number of layers in the coding network. Based on the ROS environment, this paper sets up a simulation experiment data acquisition system and verifies the effectiveness of the dimensionality reduction algorithm.

关键词： scene representation deep autoencoder UAV perception

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Self-Supervised Speech Enhancement for Arabic Speech Recognition in Real-World Environments

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TRAITEMENT DU SIGNAL 2021年第2期38卷 349-358页

作者： Dendani, Bilal Bahi, Halima Sari, Toufik Univ Badji Mokhtar Annaba Comp Sci Dept Annaba 23000 Algeria Univ Badji Mokhtar Annaba Labged Lab Annaba 23000 Algeria

Mobile speech recognition attracts much attention in the ubiquitous context, however, background noises, speech coding, and transmission errors are prone to corrupt the incoming speech. Therein, building a robust speech recognizer requires the availability of a large number of real-world speech samples. Arabic language, like many other languages, lacks such resources;to overcome this limitation, we propose a speech enhancement step, before the recognition begins. For the speech enhancement purpose, we suggest the use of a deep autoencoder (DAE) algorithm. A two-step procedure is suggested: in the first step, an overcomplete DAE is trained in an unsupervised way, and in the second one, a denoising DAE is trained in a supervised way leveraging the clean speech produced in the previous step. Experimental results performed on a real-life mobile database confirmed the potentials of the proposed approach and show a reduction of the WER (Word Error Rate) of a ubiquitous Arabic speech recognizer. Further experiments show an improvement of the perceptual evaluation of speech quality (PESQ), and the short-time objective intelligibility (STOI) as well.

关键词： Arabic language deep autoencoder deep learning self-supervised speech enhancement speech recognition ubiquitous systems

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CONTROL ENGINEERING PRACTICE 2021年 113卷

作者： Guo, Zijian Shang, Chao Ye, Hao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Establishing a quantitative similarity between different datasets has gained prevalence and significance in many applications of process control. In industrial practice, process data are usually multi-dimensional, nonlinearly correlated, and with unknown time-varying distribution, which raise immense challenge for reasonably evaluating similarity. To address this issue, a novel similarity metric based on deep autoencoder (DAE) and the Wasserstein distance is proposed in this paper. Specifically, DAE is used to first capture nonlinear relationship embedded in multivariate process data, and the reconstruction error acts as an indicator to reveal discrepancy between two datasets. After that, the similarity is characterized by evaluating the gap between reconstruction error distributions using Wasserstein distance. The proposed similarity metric has wide applicability in a variety of data analytics tasks including pattern matching, fault diagnosis and mode classifications. Both simulated data and industrial data collected from a real iron-making process are utilized to carry out comprehensive case studies. It is shown that the proposed similarity metric not only enjoys better rationality and sensitivity than generic similarity metrics, but also effectively improves the accuracy of fault diagnosis and mode classification based on big process data.

关键词： Similarity metric deep autoencoder Wasserstein distance Unsupervised mode classification Big process data

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A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 123卷

作者： Li, Wanxiang Shang, Zhiwu Zhang, Jie Gao, Maosheng Qian, Shiqi Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China Tianjin Modern Electromech Equipment Technol Key L Tianjin 300387 Peoples R China

During the operation of rotating machinery, the occurrence of unknown fault types makes it impossible for the artificial intelligence-based fault diagnosis model to distinguish. Furthermore, due to the excessive generalization capability of the autoencoder, the unsupervised anomaly detection method based on the autoencoder is difficult to effectively distinguish normal and abnormal samples. To address the above problem, this paper proposed an unsupervised anomaly detection method based on memory augmented temporal convolutional autoencoder (MATCAE). Firstly, a novel temporal convolutional autoencoder model is constructed based on dilated causal convolution, skip connection and autoencoder to facilitate the model to learn the temporal features of the input data, thereby enhancing the model's ability to capture the complex structure of the data. Then, a memory augmented module is designed using a memory matrix and an attention mechanism to expand the distribution interval between the reconstructed samples of normal and abnormal samples and reduce the sample capacity in the overlapping area. Finally, an anomaly detection module based on Euclidean distance, cosine distance and absolute mean square error is designed to improve the reliability of the metric between the input and reconstructed samples. To verify the effectiveness of the proposed method, experimental validation is carried out on a gearbox anomaly detection dataset. The experimental results show that the proposed method has higher anomaly detection accuracy and better noise robustness than other advanced anomaly detection methods, where the average performance metric is improved by 26.86% at the highest and 2.80% at the lowest.

关键词： Rotating machinery Anomaly detection Dilated convolutional deep autoencoder Unsupervised learning Memory augmented

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Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Gonzalez-Muniz, Ana Diaz, Ignacio Cuadrado, Abel A. Garcia-Perez, Diego Perez, Daniel Univ Oviedo Elect Engn Dept Edif Dept Oeste 2Campus Viesques S-N Gijon 33204 Spain Univ Leon Escuela Ingn SUPPRESS Res Grp Campus Vegazana Leon 24007 Spain

Anomaly detection is a crucial task in the engineering systems field. However, there is usually little or no information about all possible abnormal modes in systems. Hence, a common approach is to build a model of healthy behaviour, based on normal operation data, so that anomaly detection would depend on how well new data fit this model. According to this idea, we propose a residual-error based approach consisting of: a variational autoencoder, used to model the probability density function of the system's healthy behaviour;and a two-step classification algorithm, which classifies the incoming samples based on their residuals, and reports not only their normal/anomalous nature but also that of their components. We have tested this proposal in three different engineering contexts and we have compared its performance with that of state-of-the-art approaches, demonstrating its capability to successfully detect and characterize anomalies.

关键词： Anomaly detection Novelty detection deep autoencoder Variational autoencoder Engineering systems

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A White Shark Equilibrium Optimizer with a Hybrid deep-Learning-Based Cybersecurity Solution for a Smart City Environment

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SENSORS 2023年第17期23卷 7370页

作者： Almuqren, Latifah Aljameel, Sumayh S. Alqahtani, Hamed Alotaibi, Saud S. Hamza, Manar Ahmed Salama, Ahmed S. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Comp Sci Dept POB 1982 Dammam 31441 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Unit Cybersecur Abha 61421 Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Informat Syst Mecca 24382 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

关键词： smart grids DDoS attacks cybersecurity feature selection deep autoencoder smart cities

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Reservoir Inflow Forecast Using a Clustered Random deep Fusion Approach in the Three Gorges Reservoir, China

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JOURNAL OF HYDROLOGIC ENGINEERING 2018年第10期23卷

作者： Bai, Yun Sun, Zhenzhong Zeng, Bo Long, Jianyu Li, Chuan Zhang, Jin Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Tech Univ Dresden Inst Urban Water Management D-01062 Dresden Germany

Reservoir inflow forecast plays a crucial part in programming, development, operation, and management of water resource systems. To better reveal the complex properties of daily reservoir inflow, a clustered deep fusion (CDF) approach is proposed in this paper. First, variational mode decomposition (VMD) is used to decompose the daily reservoir inflow series into multiple modes, which are clustered into different sets by fuzzy c-means according to the Xie-Beni index in view of attribute domain. In each cluster, a deep autoencoder model (DAE) is developed for deep representations of the attributes in the deep domain. DAE outputs are finally fused at the synthesis domain into the forecasting results using random forest (RF). In this way, the inflow time series may be successively observed in the attribute domain, deep domain, and synthesis domain, which results in a clearer understanding of reservoir inflow trend. The present approach is modeled and evaluated using historical data collected from the Three Gorges Reservoir, China. For comparison, two kinds of learning patternsdeep learning (VMD-DAE-RF and DAE) and shallow learning (feed-forward neural network, least-squares support regression, and RF)are applied to the same case. The results indicate that the proposed CDF model outperforms all comparison models in terms of mean absolute percentage error (6.174%), root mean-square error (1,077.428m3/s), and correlation coefficient criteria (0.987). Thus, it is concluded that deep learning in the cluster fusion architecture is more promising.

关键词： Variational mode decomposition Fuzzy c-means deep autoencoder Random forest fusion Reservoir inflow forecast

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Prediction of compressive strength of nano-silica modified engineering cementitious composites exposed to high temperatures using hybrid deep learning models

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Tanyildizi, Harun Firat Univ Technol Fac Dept Civil Engn Elazig Turkiye

This study estimated the compressive strength of nano-silica-modified engineering cementitious composites subjected to high temperatures using innovative hybrid deep learning models. The innovative hybrid models in this study were designed using autoencoder (AE)-decision tree (DT) and autoencoder (AE)-extreme learning machine (ELM). Additionally, ELM, DT, and deep AE models in this study were designed to compare the results of innovative hybrid deep learning models. The sensitivity analysis was used for the statistical assessment of the experimental results. The input variables of the models were selected as the cement amount, fly ash amount, sand amount, water amount, high-range water reducer amount, PVA (polyvinyl alcohol) fiber amount, nano-silica amount, and the degree of exposure to high temperatures. The compressive strength was used as the output variable of the models. The mixture ratio in the experimental study was 583 kg/m3 cement, 467 kg/m3 sand, 700 kg/m3 fly ash, 187 kg/ m3 water, PVA fiber (0.5 %, 1 %, 1.5 % and 2 %) and nano silica (0 %, 1 %, 2 %, 3 % and 4 %) were used. The ELM, DT, and deep AE models estimated the compressive strength of nano-silicamodified engineering cementitious composites subjected to high temperatures with 93.86 %, 77.35 %, and 86.5 % accuracy, respectively. Also, the same compressive strength was estimated with 94.28 % and 98 % accuracy using the hybrid deep AE-DT and AE-ELM models. This study found that the innovative hybrid deep AEELM model predicted compressive strength with higher accuracy than the deep AE-DT, DT, ELM, and deep AE models. Additionally, the deep AE-DT model predicted compressive strength with higher accuracy than nonhybrid models. Thus, it can be stated that innovative hybrid deep models are more advantageous than other models in estimating the compressive strength of ECC. The sensitivity analysis obtained that the PVA fiber was the most significant variable affecting the compressive strength results of nan

关键词： Engineering cementitious composites Nano-silica High temperatures deep autoencoder Decision tree Extreme learning machine Hybrid deep learning

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