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12th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Hamdi, Slim Bouindour, Samir Loukil, Kais Snoussi, Hichem Abid, Mohamed LM2S Univ Technol Troyes 12 Rue Marie CurieCS 42060 F-10004 Troyes France Univ Sfax ENIS Natl Engn Sch CES Lab BP 3038 Sfax Tunisia

ISBN: (纸本)9789897583957

In the context of abnormal event detection in videos, only the normal events are available for the learning process, therefore the implementation of unsupervised learning method becomes paramount. We propose to use a new architecture denoted Two-Stream Fully convolutional Networks (TS-FCNs) to extract robust representations able to describe the shapes and movements that can occur in a monitored scene. The learned FCNs are obtained by training two convolutional auto-encoders (CAEs) and extracting the encoder part of each of them. The first CAE is trained with sequences of consecutive frames to extract spatio-temporal features. The second is learned to reconstruct optical flow images from the original images, which provides a better description of the movement. We enhance our (TS-FCN) with a Gaussian classifier in order to detect abnormal spatio-temporal events that could present a security risk. Experimental results on challenging dataset USCD Ped2 shows the effectiveness of the proposed method compared to the state-of-the-art in abnormal events detection.

关键词： Deep Learning Anomaly Detection convolutional auto-encoder

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A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第4期31卷 833-845页

作者： Liu, Chengbao Tan, Jie Wang, Xuelei Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Because the data generated in the complex industrial manufacturing processes is multi-sourced and heterogeneous, it brings a challenge for addressing decision-making optimization problems embedded in the whole manufacturing processes. Especially, for inconsistent lithium-ion cell screening as such a special problem, it is a tough issue to fuse data from multiple sources in a lithium-ion cell manufacturing process to screen cells for relieving the inconsistency among cells in a battery pack with multiple cells configured in series, parallel, and series-parallel. This paper proposes a data-driven decision-making optimization approach (DDDMO) for inconsistent lithium-ion cell screening, which takes into account three dynamic characteristic curves of cells, thus ensuring that the screened cells have consistent electrochemical characteristics. The DDDMO method uses the convolutional auto-encoder to extract features from different characteristics curves of lithium-ion cells through multi-channels and then the features in different channels are combined into fusion features to build a feature base. It also proposes an effective sample generation approach for imbalanced learning using the conditional generative adversarial networks to enhance the feature base, thereby efficiently training a classifier for inconsistent lithium-ion cell screening. Finally, industrial applications verify the effectiveness of the proposed approach. The results show that the missing rate of inconsistent lithium-ion cells drops by an average of 93.74% compared to the screening performance in the single dynamic characteristic of cells, and the DDDMO approach has greater accuracy for screening cells at lower time costs than the existing methods.

关键词： Multi-source data fusion Imbalanced learning convolutional auto-encoder Generative adversarial networks Inconsistent lithium-ion cell screening

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automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2020年第2期58卷 291-306页

作者： Souadih, Kamal Belaid, Ahror Ben Salem, Douraied Conze, Pierre-Henri Univ Abderrahmane Mira Med Comp Lab LIMED Bejaia 06000 Bejaia Algeria Lab Med Informat Proc LaTIM UMR 1101 Inserm 22 Ave Camille Desmoulins F-29238 Brest France CHRU Cavale Blanche Neuroradiol Dept Blvd Tanguy Prigent UBO F-29609 Brest France IMT Atlantique Technopole Brest Iroise F-29238 Brest France

Recent clinical research studies in forensic identification have highlighted the interest in sphenoid sinus anatomical characterization. Their pneumatization, well known as extremely variable in degrees and directions, could contribute to the radiologic identification, especially if dental records, fingerPrints, or DNA samples are not available. In this paper, we present a new approach for automatic person identification based on sphenoid sinus features extracted from computed tomography (CT) images of the skull. First, we present a new approach for fully automatic 3D reconstruction of the sphenoid hemisinuses which combines the fuzzy c-means method and mathematical morphology operations to detect and segment the object of interest. Second, deep shape features are extracted from both hemisinuses using a dilated residual version of a stacked convolutional auto-encoder. The obtained binary segmentation masks are thus hierarchically mapped into a compact and low-dimensional space preserving their semantic similarity. We finally employ the l(2) distance to recognize the sphenoid sinus and therefore identify the person. This novel sphenoid sinus recognition method obtained 100% of identification accuracy when applied on a dataset composed of 85 CT scans stemming from 72 individuals. automatic Forensic Identification using 3D Sphenoid Sinus Segmentation and Deep Characterization from Dilated Residual auto-encoders

关键词： Sphenoidal sinus convolutional auto-encoder 3D segmentation Fuzzy C-means Mathematical morphology Forensic identification

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Classification of electromyographic hand gesture signals using machine learning techniques

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NEUROCOMPUTING 2020年 401卷 236-248页

作者： Jia, Guangyu Lam, Hak-Keung Liao, Junkai Wang, Rong Kings Coll London Dept Engn London WC2R 2LS England

The electromyogram (EMG) signals from an individual's muscles can reflect the biomechanics of human movement. The accurate classification of individual and combined finger movements using surface EMG signals is able to support many applications such as dexterous prosthetic hand control. The existing research of EMG-based hand gesture classification faces the challenges of inaccurate classification, insufficient generalization ability and weak robustness. To address these problems, this paper proposes a deep learning model that combines convolutional auto-encoder and convolutional neural network (CAE+CNN) to classify an EMG dataset consisting of 10 classes of hand gestures. The proposed method shrinks the inputs into a smaller latent space representation using CAE and the resultant compressed features are served as inputs of CNN, which reduces the redundancy of EMG signals and improves the classification accuracy and training efficiency. Besides, to enhance the robustness and generalization ability for classification, a data processing approach is proposed which combines the windowing method and majority voting of the obtained results from the classifier. In addition, comprehensive comparative study is carried out with 8 widely applied and state-of-the-art classifiers in terms of classification accuracy, robustness subject to noise and statistical analysis (sensitivity, specificity, precision, F1 Score and Matthews correlation coefficient). The results demonstrates that the integration of windowing method, CAE+CNN and majority voting achieves the best performance (99.38% test accuracy for the data without adding noise, which is 3.78% higher than the best classifier used for comparison), strongest robustness (achieved 98.13% test accuracy when Gaussian noise of level le-5 is added to the raw dataset, which is 4.07% higher than the best classifier used for comparison) and statistical properties compared to other classifiers, which shows the potential for healthcare app

关键词： convolutional auto-encoder convolutional neural networks Deep learning EMG signals classification Machine learning

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Target Classification Using convolutional Deep Learning and auto-encoder Models 4

Target Classification Using Convolutional Deep Learning and ...

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4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Zaied, Sarra Toumi, Abdelmalek Khenchaf, Ali ENSTA Bretagne Lab STICC UMR CNRS 6285 2 Rue Francois Verny F-29806 Brest 9 France

ISBN: (纸本)9781538652398

Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objects in radar images, that its needs a whole chain of treatment. To classify radar images a feature extraction method is used to detect an appropriate subspace in the original feature space, which is based on transformation of the original feature This subspace should be big enough to maintain minimal loss of information and small enough to minimize the complexity of classifier. Since the feature extractor is difficult to build in manual mode and needs to be redesigned for each application, a Deep Learning in automatic mode is used with a training process subdivided into several modules. In this paper, we lay out an approach to classify Synthetic aperture radar (SAR) and Inverse Synthetic aperture radar (ISAR) images using Deep learning techniques. At first, in order to evaluate the effect of convolution layers and the number of hidden layers of the perceptron we thought of implementing 4 configurations of CNN (convolutional neural network). In the second time, we use the CAE (convolutional auto-encoder) to learn the optimal filters that minimize the reconstruction error, after we use these filters to feed the CNN retained and evaluate the effect on performance's system.

关键词： Target recognition Deep learning ISAR images SAR images convolutional neural network convolutional auto-encoder

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A Fuzzy Support Vector Machine-Enhanced convolutional Neural Network for Recognition of Glass Defects

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2019年第6期21卷 1870-1881页

作者： Jin, Yong Zhang, Dandan Li, Maozhen Wang, Zhaoba Chen, Youxing North Univ China Sch Informat & Commun Engn Taiyuan 030051 Shanxi Peoples R China Brunel Univ London Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England

The existence of glass defects seriously affects the quality of glass products. Traditional glass defect recognition methods based on convolutional neural networks (CNNs) suffer from a long training time and low recognition accuracy. By introducing convolutional auto-encoder (CAE) into a CNN, we propose an auto-encoding convolutional neural network, and pre-train the convolution kernel by CAE, thereby reducing the training time caused by randomly initializing the convolution kernel. At the same time, in order to cope with the over-fitting problem caused by a small sample of glass defect dataset, we employ a fuzzy support vector machine (FSVM) instead of the Softmax classifier to classify glass defects. Furthermore, we propose a multi-channel auto-encoding convolutional neural network model to deal with misidentification of inclusions and tumor type defects due to small differences in feature space. The model takes a defect image and its enhanced image as input, and averages the output of each channel as the final output, thus achieving accurate recognition of glass defects. Experimental results show that the glass defect recognition method based on auto-encoding convolutional neural network can greatly reduce the training time while maintaining the same classification accuracy rate. The glass defect recognition method based on multi-channel auto-encoding convolutional neural network achieves a recognition rate of 95% for both inclusion and tumor type defects. As a result, and the overall recognition rate is increased from 92.6% using a single channel to 97%.

关键词： convolutional auto-encoder Convolution neural networks Fuzzy support vector machine Glass defect recognition Multi-channel

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Intelligent Emotion Detection Method in Mobile Edge Computing Networks

Intelligent Emotion Detection Method in Mobile Edge Computin...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Li, Zhidu Lv, Ji Wu, Dapeng Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Key Lab Opt Commun & Networks Chongqing Chongqing Peoples R China Key Lab Ubiquitous Sensing & Networking Chongqing Chongqing Peoples R China

ISBN: (纸本)9781728173276

How to detect emotions of different people in time is significant in various areas, such as healthcare, VR/AR and etc. In this paper, an intelligent emotion detection method is proposed to transmit individual emotion data in real-time with as little energy as possible in a resource-limited mobile edge computing (MEC) networks. First, a data compression method is designed based on convolutional auto-encoder (CAE) to compress emotion data on the user side. Then, to guarantee the requirements of data transmission delay and the data distortion rate simultaneously, an optimal channel allocation algorithm is proposed. Thereafter, the emotion data is recovered and analyzed by the edge personal emotion model EEGNET on the edge computing server where the real-time emotions of users can be monitored. The effectiveness of the proposed method is finally verified by extensive simulation experiments.

关键词： emotion detection convolutional auto-encoder data compression radio access management

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Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent 20

Simplifying Reinforced Feature Selection via Restructured Ch...

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20th IEEE International Conference on Data Mining (ICDM)

作者： Zhao, Xiaosa Liu, Kunpeng Fan, Wei Jiang, Lu Zhao, Xiaowei Yin, Minghao Fu, Yanjie Northeast Normal Univ Changchun Peoples R China Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781728183169

Feature selection aims to select a subset of features to optimize the performances of downstream predictive tasks. Recently, multi-agent reinforced feature selection (MARFS) has been introduced to automate feature selection, by creating agents for each feature to select or deselect corresponding features. Although MARFS enjoys the automation of the selection process, MARFS suffers from not just the data complexity in terms of contents and dimensionality, but also the exponentially-increasing computational costs with regard to the number of agents. The raised concern leads to a new research question: Can we simplify the selection process of agents under reinforcement learning context so as to improve the efficiency and costs of feature selection? To address the question, we develop a single-agent reinforced feature selection approach integrated with restructured choice strategy. Specifically, the restructured choice strategy includes: 1) we exploit only one single agent to handle the selection task of multiple features, instead of using multiple agents. 2) we develop a scanning method to empower the single agent to make multiple selection/deselection decisions in each round of scanning. 3) we exploit the relevance to predictive labels of features to prioritize the scanning orders of the agent for multiple features. 4) we propose a convolutional auto-encoder algorithm, integrated with the encoded index information of features, to improve state representation. 5) we design a reward scheme that take into account both prediction accuracy and feature redundancy to facilitate the exploration process. Finally, we present extensive experimental results to demonstrate the efficiency and effectiveness of the proposed method.

关键词： feature selection reinforcement learning convolutional auto-encoder

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Deep semantic preserving hashing for large scale image retrieval

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第17期78卷 23831-23846页

作者： Zareapoor, Masoumeh Yang, Jie Jain, Deepak Kumar Shamsolmoali, Pourya Jain, Neha Kant, Surya Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Jaypee Univ Engn & Technol Guna India India Inst Technol Roorkee Uttar Pradesh India

Hashing approaches have got a great attention because of its efficient performance for large-scale images. This paper, aims to propose a deep hashing method which can combines stacked convolutional autoencoder with hashing learning, where the input image hierarchically maps to the low dimensional space. The proposed method DCAH contains encoder-decoder, and supervisory sub-network, that generates a low dimensional binary code in a layer-wised manner of the deep conventional neural network. To optimizing the hash algorithm, we added some extra relaxations constraint to the objective function. In our extensive experiments on ultra-high dimensional image datasets, our results demonstrate that the decoder structure can improve the hashing method to preserve the similarities in hashing codes;also, DCAH achieves the best performance comparing to other states of the art approaches.

关键词： convolutional auto-encoder Image hashing Image retrieval Deep learning Similarity search Learning to hash

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M³Net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation

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PATTERN RECOGNITION 2019年 91卷 366-378页

作者： Wei, Jie Xia, Yong Zhang, Yanning Northwestern Polytech Univ Natl Engn Lab Integrated Aerospace Ground Ocean B Sch Comp Sci & Engn Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci & Engn CMCC Xian 710072 Shaanxi Peoples R China

Segmentation of the brain into gray matter, white matter, and cerebrospinal fluid (CSF) using magnetic resonance (MR) imaging plays a fundamental role in neuroimaging research and clinical settings. Due to the complexity of brain anatomy, low image quality, and insufficient training data, both traditional and deep learning segmentation methods have a limited performance. In this paper, we propose a multi model, multi-size and multi-view deep neural network (M(3)Net) for brain MR image segmentation, which uses three identical modules to segment transaxial, coronal, and sagittal MR slices, respectively. Each module consists of multi-size U-Nets and multi-size back propagation neural networks. It also uses a probabilistic atlas to explore brain anatomy and a convolutional auto-encoder (CAE) to restore MR images. The proposed M(3)Net model was evaluated against widely used segmentation methods on both synthetic and clinical studies. Our results suggest that the proposed model is able to segment Brain MR Images more accurately. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Brain image segmentation Deep learning U-Net convolutional auto-encoder Back propagation neural network Magnetic resonance imaging image

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