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FRWCAE: joint faster-RCNN and Wasserstein convolutional auto-encoder for instance retrieval

APPLIED INTELLIGENCE 2020年第7期50卷 2208-2221页

作者： Zhang, Yi-yang Feng, Yong Liu, Da-jiang Shang, Jia-xing Qiang, Bao-hua Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400030 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Optoelect Informat Proc Guilin 541004 Peoples R China

Based on the powerful feature extraction capability of deep convolutional neural networks, image-level retrieval methods have achieved superior performance compared to the hand-crafted features and indexing algorithms. However, people tend to focus on foreground objects of interest in images. Locating objects accurately and using object-level features for retrieval become the essential tasks of instance search. In this work, we propose a novel instance retrieval method FRWACE, which combines the Faster R-CNN framework for object-level feature extraction with a brand-new Wasserstein convolutional auto-encoder for dimensionality reduction. In addition, we propose a considerate category-first spatial re-rank strategy to improve instance-level retrieval accuracy. Extensive experiments on four large datasets Oxford 5K, Paris 6K, Oxford 105K and Paris 106K show that our approach has achieved significant performance compared to the state-of-the-arts.

关键词： Instance-level retrieval convolutional auto-encoder Wasserstein distance Dimensionality reduction

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Dispersion compensation of Lamb waves based on a convolutional auto-encoder

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期198卷

作者： Zhang, Han Hua, Jiadong Tong, Tong Zhang, Tian Lin, Jing Beihang Univ Sch Reliabil & Syst Engn Xueyuan Rd 37 Beijing Peoples R China Beihang Univ Ningbo Inst Technol Adv Mfg Ctr Ningbo 315100 Peoples R China Beihang Univ Sci & Technol Reliabil & Environm Engn Lab Xueyuan Rd 37 Beijing Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Lamb wave-based detection has become a promising technique for structural health monitoring in plate-like structures. However, the dispersion effect of Lamb waves makes the wave packets elongated, which degrades the resolution for damage identification. Hence, it is necessary to develop effective methods for the dispersion removal of Lamb waves. Recently, deep learning has drawn much attention for Lamb wave detection due to its powerful feature extraction capability. Most current deep learning models are developed directly for damage classification or location estimation tasks on the Lamb wave signals, rather than signal processing for further manipulation. Therefore, in this paper, a novel approach based on a convolutional auto-encoder is proposed for the dispersion compensation of Lamb waves. The convolutional auto-encoder model is utilized to construct the mapping relationship between the dispersive signal and the time of flight of the wave packet. The dispersion compensated signal is reconstructed by combining the estimated time of flight from the network with the waveform of the excitation signal. Numerical and experimental validations on aluminum and composite plates are implemented to verify the effectiveness of the proposed method. Compared with conventional methods for dispersion compensation, the results demonstrate that our proposed method not only separates the overlapped wave packets but also enables dispersion compensation of multimodal and multi-packet Lamb wave signals. In addition, this method is still applicable in the case of inaccurate dispersion data due to the generalizability of the data-driven model.

关键词： Lamb wave Dispersion compensation convolutional auto-encoder Deep learning Structural health monitoring

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Fast and Robust Loop-Closure Detection via convolutional auto-encoder and Motion Consensus

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第6期18卷 3681-3691页

作者： Ma, Jiayi Wang, Shenyue Zhang, Kaining He, Zheng Huang, Jun Mei, Xiaoguang Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Wuhan Univ Sch Comp Wuhan 430072 Peoples R China

Loop-closure detection is an indispensable module in the visual simultaneous localization and mapping (vSLAM) system. It typically consists of three main steps: image representation, loop-closure candidate selection, and loop-closure event verification. This article proposes a novel approach for loop-closure detection. In particular, we first introduce a lightweight convolutional auto-encoder network trained by the deep perceptual similarity loss for image representation. We then propose an image-to-sequence selection approach based on place sequence division and distance-weighted voting for loop-closure candidate selection. Furthermore, we propose a motion vector consensus constraint to improve locality preserving matching, which can be used for efficient loop-closure event verification that is robust for various complex environments. Extensive experiments have been conducted on four publicly available datasets. The results demonstrate that our method is able to achieve better recall performance than the state-of-the-art and meet the real-time requirement of vSLAM systems.

关键词： Image representation Feature extraction Visualization Training Informatics Real-time systems Image reconstruction convolutional auto-encoder locality preserving matching loop-closure detection place recognition simultaneous localization and mapping (SLAM)

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Bridge anomaly detection based on reconstruction error and structural similarity of unsupervised convolutional auto-encoder

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年第4期23卷 2221-2237页

作者： Teng, Shuai Liu, Zongchao Luo, Wenjun Chen, Gongfa Cheng, Li Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou Peoples R China Guangzhou Univ Res Ctr Wind Engn & Engn Vibrat Guangzhou Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong 999077 Peoples R China Guangdong Univ Technol Sch Civil & Transportat Engn 100 Waihuan Xi Rd Guangzhou 510006 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Kowloon Hong Kong 999077 Peoples R China

This study presents a novel bridge anomaly detection approach that employs the reconstruction error and structural similarity of an unsupervised convolutional auto-encoder. The presence of structural damage in a bridge typically results in changes in its vibration signals, and thus, the use of these signals for structural damage detection (SDD) has been widely investigated, with many methods relying on supervised learning. However, such existing SDD methods based on the supervised learning require prior knowledge of the damage states and cannot process monitoring data in real-time, thereby limiting their application to in-service bridges. To address this challenge, the authors propose the use of a convolutional auto-encoder as the reconstruction algorithm for real-time vibration signals. The auto-encoder is trained using normal signals and then used to reconstruct new inputs (either normal or abnormal). Two damage indicators (reconstruction error and structural similarity) are then calculated based on the reconstruction results and clustered to detect abnormal signals. The proposed approach was applied to the detection of various abnormalities in the old ADA Bridge, the results were 100% accurate, and about a 10% increase in accuracy was observed when compared to other control experiments. These results demonstrate the effectiveness of the proposed approach, with the auto-encoder achieving excellent reconstruction results for normal signals and clear discrepancies for abnormal signals. The proposed method was also validated on a cable-stayed bridge and an arch bridge, demonstrating its wide applicability in bridge anomaly detection.

关键词： Bridge anomaly detection unsupervised learning convolutional auto-encoder reconstruction error structural similarity

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A secure and robust images encryption scheme using chaos game representation, logistic map and convolutional auto-encoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第30期83卷 74413-74440页

作者： Ramezanipour, Nafise Moattar, Mohammad Hossein Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Iran

Secure transfer of digital images is an important issue in modern image communications. The complexity of encryption algorithms and their key space is a guarantee for security and robustness issues, and the algorithms are competing in this regard while considering time complexity constraints. In this paper, it is tried to increase the encryption complexity and unpredictability of the encryption scheme using different phases of chaos game representation (GCR), logistic map diffusion, and convolutional auto-encoder-based image representation. In the proposed scheme, the original image's pixels are first turned into a binary sequence, after which the CGR algorithm is applied to create a sequence of coordinates in the CGR space. These coordinates are used as the first key, and the pixels of the original image are aligned with this key. Additionally, the second key is generated using the chaotic logistic map to increase the complexity of the key. After that, a randomly weighted convolutional auto-encoder is applied, and the encrypted image resulting from the first two stages is fed to the network for simultaneous diffusion and compression for further complexity increment. The applied convolutional auto-encoder does not require training, and it offers a significant amount of key space, which helps robustness against various attacks. Resulting from the experiments, the average entropy of the encrypted images is relatively high, while the considerable value of the NPCR and UACI criteria denotes the robustness of the approach against differential attacks. This algorithm has demonstrated superiority on both grayscale and color images.

关键词： Image encryption Logistic map Chaos game representation convolutional auto-encoder

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One-dimensional residual convolutional auto-encoder for fault detection in complex industrial processes

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第18期60卷 5655-5674页

作者： Yu, Jianbo Liu, Xing Tongji Univ Sch Mech Engn 4800 CaoAn Rd Shanghai 201804 Peoples R China

Fault detection and diagnosis have always been the key techniques for safe and reliable operation of industrial processes. However, the high dimension and noise of process variables have brought great challenges to the fault detection model. In recent years, due to the powerful feature extraction ability, deep learning has been widely applied in process fault detection and diagnosis. However, these deep neural networks (DNNs) often need a large amount of label data for supervised training, or show poor performance in learning features under unsupervised-learning condition. This paper proposes a new DNN model, a one-dimension residual convolutional auto-encoder (1DRCAE), where unsupervised learning is used to extract representative features from complex industrial processes. 1DRCAE effectively integrates the one-dimensional convolutional kernel with an auto-encoder and is embedded residual learning block for effective feature extraction from one-dimensional data. The two statistics and squared prediction error are generated in the feature space and residual space of 1DRCAE, respectively. Finally, the feasibility and superiority of 1DRCAE are verified on a simulation process, Tennessee Eastman process, Fed-batch fermentation penicillin process, and a real-life case. The convolutional auto-encoder technique provides a new way for feature learning and fault detection on complex industrial processes.

关键词： Industrial process fault detection deep learning convolutional auto-encoder residual learning

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Yarn-dyed Fabric Defect Detection using U-shaped De-noising convolutional auto-encoder 9

Yarn-dyed Fabric Defect Detection using U-shaped De-noising ...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Zhang, Hong-wei Tan, Quan-lu Lu, Shuai Ge, Zhi-qiang Gu, De Xian Polytech Univ Sch Elect Informat Xian 710048 Peoples R China Zhejiang Univ Inst Ind Proc Control Hangzhou 310027 Peoples R China Beijing Univ Chem Technol Inst Math Beijing 100029 Peoples R China Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781728159225

Practical factors such as high labor cost of labelling defect samples and scarcity of defect samples make it difficult for supervised machine learning models to solve the problem of yam-dyed fabric defect detection. To solve this problem, this paper proposes an unsupervised yam-dyed fabric defect detection method based on U-shaped de-noising convolutional auto-encoder (UDCAE). Firstly, for tested samples of yam-dyed fabric, the training dataset was constructed by collecting the non-defect yarn-dyed fabric samples. Then, the non-defect dataset is utilized to model and train the proposed UDCAE model. Finally, the defective area can be quickly detected by calculating the residual between the original tested yarn-dyed fabric image and its reconstructed item correspondingly. The experiment results show that the proposed method can accurately detect defects of yarn-dyed fabrics with different patterns.

关键词： yam-dyed fabric fabric defect detection de-noising auto-encoder convolutional auto-encoder Unet

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HRTF Representation with convolutional auto-encoder 26th

HRTF Representation with Convolutional Auto-encoder

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26th International Conference on MultiMedia Modeling (MMM)

作者： Chen, Wei Hu, Ruimin Wang, Xiaochen Li, Dengshi Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan 430072 Peoples R China Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Peoples R China Jianghan Univ Sch Math & Comp Sci Wuhan Peoples R China

ISBN: (纸本)9783030377311;9783030377304

The head-related transfer function (HRTF) can be considered as some kind of filter that describes how a sound from an arbitrary spatial direction transfers to the listener's eardrums. HRTF can be used to synthesize vivid virtual 3D sound that seems to come from any spatial location, which makes it play an important role in the 3D audio technology. However, the complexity and variation of auditory cues inherent in HRTF make it difficult to set up an accurate mathematical model with the conventional methods. In this paper, we put forward an HRTF representation modeling based on convolutional auto-encoder (CAE), which is some type of auto-encoder that contains convolutional layers in the encoder part and deconvolution layers in the decoder part. The experimental evaluation on the ARI HRTF database shows that the proposed model provides very good results on dimensionality reduction of HRTF.

关键词： HRTF Representation convolutional auto-encoder Deep auto-encoder Denoising auto-encoder

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Node-Image CAE: A Novel Embedding Method via convolutional auto-encoder and High-Order Proximities 14th

Node-Image CAE: A Novel Embedding Method via Convolutional A...

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14th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Qiao, Di Yang, Wu Wang, Wei Harbin Engn Univ Harbin Heilongjiang Peoples R China

ISBN: (纸本)9783030821364;9783030821357

The network embedding method, which mainly aims to generate low-dimensional embedding vectors for nodes in a network, has received great passion from researchers and provides excellent tools for application and research. Various models have been applied to this field to gain high-quality embedding vectors and got prodigious success, such as Matrix Factorization, Multilayer Perceptron, Recurrent Neural Network, Generative Adversarial Networks, Graph convolutional Network, and so on. However, a very powerful model-convolutional Neural Network, which plays a significant role in Digital Image Processing, Natural Language Processing, etc.-has not been widely used (except whole-graph embedding). CNN is thought inappropriate to embed nodes into vectors because traditional CNN can only work on grid-like data such as images while a graph generally has a irregular structure. And with the development of the embedding method, high-order proximities are gradually found playing a crucial role in exploring the network's underlying structure. However, many existing embedding methods can only preserve fixed-order proximities, limiting the scope of their application. In this paper, we try to explore a viable way to apply CNN on node-embedding task. We first build an image for each node that can flexibly preserve different order proximities and feed them into a variation of the convolutional Neural Network, the convolutional auto-encoder (CAE), to generate embedding vectors. Experiments show that our method outperforms state-of-the-art embedding methods.

关键词： Graph embedding High-order proximity convolutional auto-encoder

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Compressed Sensing via a Deep convolutional auto-encoder 33

Compressed Sensing via a Deep Convolutional Auto-encoder

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33rd IEEE International Conference on Visual Communications and Image Processing (IEEE VCIP)

作者： Wu, Hao Zheng, Ziyang Li, Yong Dai, Wenrui Xiong, Hongkai Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Univ Texas Hlth Sci Ctr Houston Sch Biomed Informat Houston TX 77030 USA

ISBN: (纸本)9781538644584

The nonlinear recovery is not promising in accuracy and speed, which limits the practical usage of compressed sensing (CS). This paper proposes a deep learning-based CS framework which leverages a deep convolutional auto-encoder for image sensing and recovery. The utilized auto-encoder architecture consists of three components: the fully convolutional network acts as an adaptive measurement matrix generator in the encoder;while in the decoder, the deconvolution network and refined reconstruction network are learned for intermediate and final recovery, respectively. Different from most previous work focusing on the block-wise manner to reduce implementation cost but result in blocky artifacts, our adaptive measurement matrix is applicable to any size of scene image and the decoder network reconstructs the whole image efficiently without any blocky artifacts. Moreover, dense connectivity is leveraged to combine multi-level features and alleviate the vanishing-gradient problem in the refined reconstruction network which boosts the performance on image recovery. Compared to the state-of-the-art methods, our algorithm improves more than 0.8 dB in average PSNR.

关键词： Compressed sensing Adaptive measurement matrix convolutional auto-encoder Image reconstruction

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