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检索条件"主题词=convolutional autoencoder"

共 405 条记录，以下是71-80 订阅

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APPLICATION OF A convolutional autoencoder TO HALF SPACE RADAR HRRP RECOGNITION 15

APPLICATION OF A CONVOLUTIONAL AUTOENCODER TO HALF SPACE RAD...

引用

International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yu, Shi-Hao Xie, Yong-Jun Beihang Univ Sch Elect Informat & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781538652183

A Winner-Take-All convolutional autoencoder is applied to improve the performance on the half space radar high resolution range profile(HRRP) target recognition. Feature extraction is significantly important to the radar target recognition based on the HRRP. Conventional deep models of representation learning used for HRRP target recognition commonly use the vanilla autoencoder and deep belief net (DBN), moreover, the simulated HRRP samples used in these related work are mostly under the free space condition which is different from the real world situation. In this paper, convolution architecture autoencoder, which is more efficient in spatial feature extraction and sparse coding, is proposed. Furthermore, the half space HRRP samples, which is much more close to the real world situation and is quite different from the free space HRRP samples, is used as the dataset. Half space simulated HRRP data is used to apply the convolutional architecture on the ground target recognition and got an accuracy promotion about 7% compared to conventional vector-based module.

关键词： HRRP recognition convolutional autoencoder Half space

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RESEARCH ON ULTRASONIC IMAGE COMPRESSION IN LOGGING WHILE DRILLING: AN ASYMMETRIC convolutional autoencoder

RESEARCH ON ULTRASONIC IMAGE COMPRESSION IN LOGGING WHILE DR...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhang, Wei Li, Zhipeng Qiu, Ao Luo, Bin Shi, Yibing Sun, Hu Li, Yanjun Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China China Oilfield Serv Co Welltech Res & Design Inst Beijing Peoples R China Southwest Petr Univ Sch Mech Engn Chengdu Peoples R China

ISBN: (纸本)9798350320107

Logging while drilling (LWD) is an efficient technique that provides real-time information about the reservoir while drilling. However, due to the limited transmission rate of the cables, only low-dimensional information can be transferred rather than high-dimensional logging images. This paper proposes an image compression method called the asymmetric convolutional autoencoder (ACAE) that can map raw logging data into low-dimensional embedding vectors for real-time transmission through the cables. The trained ACAE consists of two primary components: an encoder deployed onto the downhole computing module and a decoder deployed onto the ground computer. To decode low-dimensional embedded information with higher quality, attention modules are utilized in the decoder to suppress noise and attain smoother logging images. Experimental results demonstrate that our method can effectively compress original logging data with a small compression rate and reconstruct logging images.

关键词： logging while drilling image compression convolutional autoencoder attention

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Anomaly Detection with convolutional autoencoder for Predictive Maintenance 9

Anomaly Detection with Convolutional Autoencoder for Predict...

引用

9th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Tian, Ruiqi Liboni, Luisa Capretz, Miriam Western Univ ECE Dept London ON Canada

ISBN: (纸本)9798350320886

Predictive maintenance is set to prevent downtime and failures of equipment and processes to meet the quality and availability requirements of several industrial, commercial, and even residential activities. This paper proposes CAE-AD, a novel convolutional autoencoder anomaly detection method that relies only on normal operation data for training the intelligent classifier. The method also accommodates a sliding window algorithm for generating the input from sensor readings, which accounts for the dynamic characteristics of the data. The anomaly detection is accomplished by comparing the convolutional autoencoder reconstruction error to a threshold value to segregate between normal and anomalous predictions. The threshold value is found by minimizing the False Positive Rates and False Negative Rates. Using a benchmark water pump sensor time-series data, the model successfully classified all water pump breakdowns and correctly identified 98.8% of anomalous data and 94.8% of normal data using a chosen best window length of the past 37 sensor readings.

关键词： Anomaly detection convolutional autoencoder sliding window predictive maintenance

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A convolutional autoencoder Based Method with SMOTE for Cyber Intrusion Detection

A Convolutional Autoencoder Based Method with SMOTE for Cybe...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： She, Xinyi Sekiya, Yuji Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665439022

The advancement of internet applications over the last decade makes network security a priority, in which Intrusion Detection System (IDS) is responsible for detecting threat in network throughput. Nevertheless, current IDS encounter challenges because (i) the training dataset is seriously class-imbalance, (ii) conventional method requires a lot of hand-crafted feature selection. To address these problems, we propose a novel method that combines data augmentation method as well as convolutional autoencoder to build up an IDS. Our method uses SMOTE to balance the distribution of each attack in the training dataset, then uses a convolutional autoencoder to detect the intrusion, which is free of hand-crafted feature design. Experimentally, we show that our proposed method outperforms conventional method both in binary and multi-class classification in terms of accuracy. We also have an ablation study on how the ratio of minority class in dataset effect the f1-score of the model.

关键词： Network Security Intrusion Detection convolutional autoencoder SMOTE

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Stacked causal convolutional autoencoder based speech compression method 32

Stacked causal convolutional autoencoder based speech compre...

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32nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Bekiryazici, Tahir Aydemir, Gurkan Gurkan, Hakan Bursa Tekn Univ Elekt Elekt Muhendisligi Bolumu Bursa Turkiye

ISBN: (纸本)9798350388978;9798350388961

This study proposes a speech compression method based on one-dimensional convolutional autoencoder and residual vector quantization. The proposed method offers different compression ratios at low bit rates. Speech quality evaluation metric (PESQ) was used to test the performance of the proposed method. Experimental results show that the proposed method achieves a PESQ value of 1.903 for 2.5 kbps and 2.24 for 5 kbps.

关键词： Speech compression residual vector quantization convolutional autoencoder deep learning

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Feature Extraction for Class Imbalance Using a convolutional autoencoder and Data Sampling 33

Feature Extraction for Class Imbalance Using a Convolutional...

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IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Salekshahrezaee, Zahra Leevy, Joffrey L. Khoshgoftaar, Taghi M. Florida Atlantic Univ Boca Raton FL 33431 USA

ISBN: (纸本)9781665408981

Training a machine learning algorithm from a class-imbalanced dataset is an inherently challenging task. The task becomes more challenging when compounded by high dimensionality (a high number of features). Feature extraction is a data reduction process that transforms features into linear or non-linear combinations of the original features, resulting in a smaller and richer set of attributes. Data sampling is a popular approach for addressing class imbalance. In this paper, our proposed method requires the implementation of feature extraction before data sampling, based on the idea that richer high-level features facilitate more efficient sampling and hence, produce better classification results. We use principal component analysis (PCA) and convolutional autoencoder (CAE) as the feature extraction techniques and synthetic minority oversampling technique (SMOTE) as the data sampling technique. In evaluating the performance of the random forest classifier on a credit card fraud dataset, our results show that CAE is a better feature extraction technique than PCA. The combination of CAE followed by SMOTE yields the best F1-score of 90.5%.

关键词： feature extraction SMOTE class imbalance PCA convolutional autoencoder

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A Watermarked Image Search Method Using Compressed Representation by a convolutional autoencoder

A Watermarked Image Search Method Using Compressed Represent...

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International Workshop on Advanced Imaging Technology (IWAIT)

作者： Imamura, K. Watanabe, K. Kuroda, H. Fujimura, M. Kanazawa Univ Kakuma Machi Kanazawa Ishikawa 9201192 Japan Nagasaki Univ Bunkyou Machi Nagasaki 8528521 Japan

ISBN: (数字)9781510643659

ISBN: (纸本)9781510643659

Digital watermarking is the main method by which prevent illegal copying of proprietary images. In addition, to reduce the amount of damage caused by illegal distribution of proprietary images, a fast search technique for illegally distributed images on the Internet is necessary. We have proposed a two-stage search method for digital watermarked images. In the present paper, we propose a new high-efficiency pre-search algorithm for proprietary images that are suspected of being copied and distributed on the Internet. The proposed algorithm is capable of quickly identifying illegal copies from large image groups using mainly the correlation coefficient of compressed representations between the original image and a target image by convolutional autoencoder. The proposed search method has tolerance for attacks such as scaling, image compression, color reduction, and intensity conversion. The performance of the proposed search algorithm is evaluated through computer simulations.

关键词： image search digital watermarking convolutional autoencoder local binary pattern

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HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON SEMI-SUPERVISED DUAL-BRANCH convolutional autoencoder WITH SELF-ATTENTION

HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON SEMI-SUPERVISED ...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Feng, Jie Ye, Zhanwei Li, Di Liang, Yuping Tang, Xu Zhang, Xiangrong Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9781728163741

Deep learning method shows its powerful classification performance with sufficient available data. However, the labeled data is limited in hyperspectral images (HSIs). Semi-supervised algorithms have unique advantages on dealing with this problem. Therefore, a semi-supervised convolutional neural network is proposed in this paper. It consists of two branches, which use limited labeled samples and a large number of unlabeled samples, respectively. The first branch includes an encoder-decoder model to extract contextual information of unlabeled samples. The other one uses the similar construction except extra classification layers to extract discriminative features of labeled samples. In order to fuse contextual and discriminative information, we cascade the features of low-level layers from different branches. Furthermore, self-attention is added to the first branch, which focuses more on the global information for classification. The experiment results show that the proposed model provides a competitive result compared with state-of-the-art methods.

关键词： hyperspectral images classification semi-supervised learning convolutional autoencoder self-attention mechanism

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Risk Prediction of Diabetic Nephropathy via Interpretable Feature Extraction from EHR sing convolutional autoencoder 40

Risk Prediction of Diabetic Nephropathy via Interpretable Fe...

引用

Conference on Medical Informatics Europe (MIE)

作者： Katsuki, Takayuki Ono, Masaki Koseki, Akira Kudo, Michiharu Haida, Kyoichi Kuroda, Jun Makino, Masaki Yanagiya, Ryosuke Suzuki, Atsushi IBM Res Tokyo Tokyo Japan Dai Ichi Life Insurance Co Ltd Business Proc Planning Dept Tokyo Japan Dai Ichi Life Insurance Co Ltd IT Business Proc Planning Dept Tokyo Japan Fujita Hlth Univ Div Endocrinol & Metab Dept Internal Med Toyoake Aichi Japan Fujita Hlth Univ Div Med Informat Syst Toyoake Aichi Japan

ISBN: (纸本)9781614998525;9781614998518

This paper describes a technology for predicting the aggravation of diabetic nephropathy from electronic health record (EHR). For the prediction, we used features extracted from event sequence of lab tests in EHR with a stacked convolutional autoencoder which can extract both local and global temporal information. The extracted features can be interpreted as similarities to a small number of typical sequences of lab tests, that may help us to understand the disease courses and to provide detailed health guidance. In our experiments on real-world EHRs, we confirmed that our approach performed better than baseline methods and that the extracted features were promising for understanding the disease.

关键词： Electronic Health Record Risk Prediction Diabetic Nephropathy Kidney Disease convolutional autoencoder Feature Extraction

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Non-Invasive Continuous Real-Time Blood Glucose Estimation Using PPG Features-based convolutional autoencoder with TinyML Implementation

Non-Invasive Continuous Real-Time Blood Glucose Estimation U...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Ali, Noor Faris Aldhaheri, Alyazia Wodajo, Bethel Alshamsi, Meera Alshamsi, Shaikha Atef, Mohamed United Arab Emirates Univ Elect & Commun Engn Dept Al Ain 15551 U Arab Emirates

ISBN: (纸本)9798350330991;9798350331004

In this paper, we developed a convolutional autoencoder for non-invasive continuous monitoring of blood glucose levels (BGL) using four photoplethysmography (PPG) features. The model was specifically designed to account for temporal relations among consecutive PPG segments' features and transient outliers encountered in real-time operation. By means of Tiny Machine Learning (TinyML), the model was embedded in an edge device, Arduino Nano 33 BLE Sense, for real-time continuous predictions of BGL. On a PC, the model was tested using a public dataset of 33 subjects and achieved a mean absolute error (MAE) 5.55 mg/dL, standard error of prediction (SEP) 7.18 mg/dL, and 97.57% success rate in zone A of Clarke error grid (CEG). On the edge, the model was tested on new 8 subjects and obtained a MAE 5.16 mg/dL and 100% of predicted BGL falling into zone A. Overall, the integration of the proposed model and the feature set resulted in substantial gains in terms of applicability, effectiveness, efficiency, and interpretability on both cloud and edge infrastructures.

关键词： Blood Glucose PPG Machine Learning convolutional autoencoder TinyML MCU

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