检索结果-内蒙古大学图书馆

Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration

IEEE SENSORS JOURNAL 2021年第20期21卷 22833-22845页

作者： Okafor, Nwamaka U. Delaney, Declan T. Univ Coll Dublin Sch Elect & Elect Engn Dublin D04 V1W8 4 Ireland

IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. Data collected using IoT sensors are often plagued with missing values occurring as a result of sensor faults, network failures, drifts and other operational issues. Missing data can have substantial impact on in-field sensor calibration methods. The goal of this research is to achieve effective calibration of sensors in the context of such missing data. To this end, two objectives are presented in this paper. 1) Identify and examine effective imputation strategy for missing data in IoT sensors. 2) Determine sensor calibration performance using calibration techniques on data set with imputed values. Specifically, this paper examines the performance of variational autoencoder (VAE), Neural Network with Random Weights (NNRW), Multiple Imputation by Chain Equations (MICE), Random Forest-based Imputation (missForest) and K-Nearest Neighbour (KNN) for imputation of missing values on IoT sensors. Furthermore, the performance of sensor calibration via different supervised algorithms trained on the imputed dataset were evaluated. The analysis showed VAE technique to outperform the other methods in imputing the missing values at different proportions of missingness on two real-world datasets. Experimental results also showed improved calibration performance with imputed dataset.

关键词： Sensors Calibration Artificial neural networks Mice Task analysis Sensor phenomena and characterization Measurement uncertainty Calibration imputation Internet of Things (IoT) missing data neural network regression sensors variational autoencoder XGBoost

来源：评论

学校读者我要写书评

暂无评论

Human Laughter Generation using Hybrid Generative Models

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2021年第5期15卷 1590-1609页

作者： Mansouri, Nadia Lachiri, Zied El Manar Univ Natl Engn Sch Tunisia Tunis 1002 Tunisia

Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.

关键词： Laughter Synthesis variational autoencoder autoencoder Objective and Subjective Evaluation

来源：评论

学校读者我要写书评

暂无评论

Deep CNN transferred from VAE and GAN for classifying irritating noise in automobile

引用

NEUROCOMPUTING 2021年 452卷 395-403页

作者： Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

Noise from automobiles, such as buzzing, squeaking, and rattling (BSR) noises, is a key factor in automobile quality assessment. It is necessary to classify these noises for appropriate handling and prevention. Although many researchers have conducted studies to classify noise, they suffer from several problems: difficulty in extracting appropriate features, insufficient data to train a classifier, and weak robustness to surrounding noise. This paper proposes a method called latent semantic controlling generative adversarial networks (LSC-GAN) to solve these problems. To capture the features of data, a variational autoencoder (VAE), an autoencoder with approximate inference in a latent Gaussian model, learns the data representation by projecting them into the latent space according to their features and reconstructing the projected data. Because the generator and the discriminator of the LSC-GAN are trained simultaneously, the capacity to extract the characteristics of the data is improved and a knowledge space of classifiable data is also expanded with insufficient data. While data are generated by the generator, the encoder projects them back to the latent space according to their characteristics to advance the ability to extract features. Finally, the encoder is trained to the classifier, which is trained to classify BSR noises. The proposed classifier outperforms other models and achieves an accuracy of 96.68%. We confirm using a confusion matrix that the proposed model classifies the types of insufficient class better than other models. Our proposed model classifies data with accuracy of 94.68%, even if the data contains surrounding noise, which means it is more robust to BSR with surrounding noise than other models. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Deep learning Transfer learning variational autoencoder Generative adversarial network BSR noise

来源：评论

学校读者我要写书评

暂无评论

Structural relational inference actor-critic for multi-agent reinforcement learning

引用

NEUROCOMPUTING 2021年 459卷 383-394页

作者： Zhang, Xianjie Liu, Yu Xu, Xiujuan Huang, Qiong Mao, Hangyu Carie, Anil Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China Okinawa Inst Sci & Technol Grad Univ 1919-1 Tancha Onna Okinawa 9040495 Japan Huawei Noahs Ark Lab 3 Xinxi Rd Beijing 100085 Peoples R China VIT AP Sch Comp Sci Amaravathi 522237 India

Multi-agent reinforcement learning (MARL) is essential for a wide range of high-dimensional scenarios and complicated tasks with multiple agents. Many attempts have been made for agents with prior domain knowledge and predefined structure. However, the interaction relationship between agents in a multi-agent system (MAS) in general is usually unknown, and previous methods could not tackle dynamical activities in an ever-changing environment. Here we propose a multi-agent Actor-Critic algo-rithm called Structural Relational Inference Actor-Critic (SRI-AC), which is based on the framework of centralized training and decentralized execution. SRI-AC utilizes the latent codes in variational autoen-coder (VAE) to represent interactions between paired agents, and the reconstruction error is based on Graph Neural Network (GNN). With this framework, we test whether the reinforcement learning learners could form an interpretable structure while achieving better performance in both cooperative and com-petitive scenarios. The results indicate that SRI-AC could be applied to complex dynamic environments to find an interpretable structure while obtaining better performance compared to baseline algorithms. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-agent systems Deep reinforcement learning variational autoencoder Actor-critic Graph neural network

来源：评论

学校读者我要写书评

暂无评论

Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models

引用

IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第6期40卷 1687-1701页

作者： Pardo, Arturo Streeter, Samuel S. Maloney, Benjamin W. Gutierrez-Gutierrez, Jose A. McClatchy, David M. Wells, Wendy A. Paulsen, Keith D. Lopez-Higuera, Jose M. Pogue, Brian W. Conde, Olga M. Univ Cantabria Photon Engn Grp Santander 39006 Spain Inst Invest Sanitaria Valdecilla IDIVAL Santander 39011 Spain Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Massachusetts Gen Hosp Radiat Drug Treatment Design Lab Boston MA 02114 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI Zaragoza 50018 Spain

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

关键词： Optical imaging Nonlinear optics Biomedical optical imaging Imaging Adaptive optics Pathology Optical scattering Biomedical optical imaging breast cancer tissue optical properties modeling pathology deep learning dimensionality reduction variational autoencoder convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Generating Responses With a Given Syntactic Pattern in Chinese Dialogues

引用

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 2888-2898页

作者： Zhou, Yi Zheng, Xiaoqing Huang, Xuanjing Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Recently, many efforts have been devoted to generating responses expressing a specific emotion or relating to a given topic in a controlled manner. However, limited attention has been given to generating responses with a specified syntactic pattern, which makes it possible to imitate someone's way of speaking in dialogue. To fulfill this goal, we propose two models to generate syntax-aware responses: a gross-constraint and a specific-constraint model. The former controls the syntactic patterns of generated responses at sentence-level, while the latter works at smaller language units, such as words or phrases, being capable of manipulating the syntactic structures of responses in a more subtle manner. The extensive experimental results on two different datasets show that both the two models not only can generate meaningful responses with a specific and coherent structure but also improve on the diversity of generated responses, with similar gains in readability, relevance, and diversity as measured by human judges.

关键词： Syntactics Process control Speech processing Computer architecture Chatbots Training Task analysis Controlled text generation stylized dialogue response generation syntactic patterns variational autoencoder deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Passive Sonar Target Classification Using Deep Generative β-VAE

引用

IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 808-812页

作者： Chandran, Satheesh C. Kamal, Suraj Mujeeb, A. Supriya, M. H. Cochin Univ Sci & Technol Dept Elect Kochi 682022 India Cochin Univ Sci & Technol Int Sch Photon Kochi 682022 India

The intrinsic complexity associated with passive sonar data makes the task of target recognition extremely challenging. The conventional classifier architectures based on hand-engineered feature transforms often fail miserably to disentangle the high-dimensional non-linear structures in the observed target records. Although the modern deep learning algorithms through hierarchical feature learning yield acceptable success rates, they often require tremendous amounts of data when trained in a supervised manner. An unsupervised generative framework utilizing a variational autoencoder (VAE) is proposed in this work in order to create better disentangled representations for the downstream classification task. The disentanglement is further enforced using a hyperparameter beta. For the purpose of better segregating the spectro-temporal features, an intermediate non-linearly scaled time-frequency representation is also employed in conjunction with beta-VAE. Experimental analysis of various classifier configurations yields encouraging results in terms of data efficiency and classification accuracy on target records collected from various locations of the Indian Ocean.

关键词： Task analysis Target recognition Acoustics Signal processing algorithms Oceans Machine learning algorithms Decoding Passive sonar target recognition deep learning variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Adaptive map matching based on dynamic word embeddings for indoor positioning

引用

NEUROCOMPUTING 2023年第1期553卷

作者： Lan, Xinyue Zhang, Lijia Xiao, Zhuoling Yan, Bo Univ Elect Sci & Technol China Sch Informat & Commun Engn 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China UESTC Sch Informat & Commun Engn Chengdu Peoples R China

Map matching has been widely used in various indoor localization technologies. However, conventional map matching technologies based on probabilistic models, such as particle filter (PF), still have a series of limitations, such as underutilization of map information, poor generalization, and relatively low precision. To improve the performance of PF-based map matching technique, this paper proposes MapDem, a novel map matching model fusing dynamic word embeddings and variational autoencoder (VAE) to improve matching performance significantly. The key to our approach is to extract map information using dynamic word embeddings to represent each reachable point on the map as word vectors with allowable oriented trajectory information. The same point has different representations on different trajectories so that MapDem can adaptively learn the contextual information of map for position estimation. Unlike traditional particle filters, MapDem focuses on the learning of particle sets distribution by a statistical model, variational autoencoder (VAE), followed by estimating position with combined current and previous sequence information. Extensive experiments have been conducted with 610 trajectories in three real-world scenarios. Numerical results demonstrate the adaptability of MapDem which works equally well in all three different scenarios, outperforming traditional particle filters by 18% on average.

关键词： Map matching Dynamic word embeddings Particle filter variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data

引用

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045028页

作者： Jadhav, Shreejit Shrivastava, Mihir Mitra, Sanjit Interuniv Ctr Astron & Astrophys IUCAA Post Bag 4 Pune 411007 India Swinburne Univ Technol Ctr Astrophys & Supercomp Hawthorn Vic 3122 Australia ARC Ctr Excellence Gravitat Wave Discovery OzGrav Melbourne Australia Indian Inst Technol IIT Kharagpur India

The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of speed, parameter space coverage, and search sensitivity. However, the opaque nature of DL models severely harms their reliability. In this work, we meticulously develop a DL model stage-wise and work towards improving its robustness and reliability. First, we address the problems in maintaining the purity of training data by deriving a new metric that better reflects the visual strength of the 'chirp' signal features in the data. Using a reduced, smooth representation obtained through a variational auto-encoder (VAE), we build a classifier to search for compact binary coalescence (CBC) signals. Our tests on real LIGO data show an impressive performance of the model. However, upon probing the robustness of the model through adversarial attacks, its simple failure modes were identified, underlining how such models can still be highly fragile. As a first step towards bringing robustness, we retrain the model in a novel framework involving a generative adversarial network (GAN). Over the course of training, the model learns to eliminate the primary modes of failure identified by the adversaries. Although absolute robustness is practically impossible to achieve, we demonstrate some fundamental improvements earned through such training, like sparseness and reduced degeneracy in the extracted features at different layers inside the model. We show that these gains are achieved at practically zero loss in terms of model performance on real LIGO data before and after GAN training. Through a direct search on similar to 8.8 days of LIGO data, we recover two significant CBC events from GWTC-2.1 (Abbott et al 2022 2108.01045 [gr-qc]), GW190519_153544 and GW190521_074359. We also report the search sensitivity obtained from an injection study.

关键词： gravitational wave deep learning cbc search variational autoencoder generative adversarial network robust and reliable model

来源：评论

学校读者我要写书评

暂无评论

Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine-Learning-Based Technique

ADVANCED INTELLIGENT SYSTEMS

引用

ADVANCED INTELLIGENT SYSTEMS 2023年第7期5卷

作者： Kim, Yuseok Park, Suk Hee Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

Herein, a highly productive and defect-free 3D-printing system enforced by deep-learning (DL)-based anomaly detection and reinforcement-learning (RL)-based optimization processes is developed. Unpredictable defect factors, such as machine setting errors or unexpected material flow, are analyzed by image-based anomaly detection implemented using a variational autoencoder DL model. Real-time detection and in situ correction of defects are implemented by an autocalibration algorithm in conjunction with the DL system. In view of productivity enhancement, the optimized set of diversified printing speeds can be generated from virtual simulation of RL, which is established using a physics-based engineering model. The RL-simulated parameter set maximizes printing speed and minimizes deflection-related failures throughout the 3D-printing process. With the synergistic assistance of DL and RL techniques, the developed system can overcome the inherent challenging intractability of 3D printing in terms of material property and geometry, achieving high process productivity.

关键词： deep learning defect control reinforcement learning variational autoencoder 3D printing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：