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Single-modal Incremental Terrain Clustering from Self-Supervised Audio-Visual Feature Learning

Single-modal Incremental Terrain Clustering from Self-Superv...

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international conference on Pattern Recognition

作者： Reina Ishikawa Ryo Hachiuma Akiyoshi Kurobe Hideo Saito Keio University Yokohama Japan

ISBN: (纸本)9781728188089;9781728188096

the key to an accurate understanding of terrain is to extract the informative features from the multi-modal data obtained from different devices. Sensors, such as RGB cameras, depth sensors, vibration sensors, and microphones, are used as the multi-modal data. Many studies have explored ways to use them, especially in the robotics field. Some papers have successfully introduced single-modal or multi-modal methods. However, in practice, robots can be faced with extreme conditions; microphones do not work well in crowded scenes, and an RGB camera cannot capture terrains well in the dark. In this paper, we present a novel framework using the multimodal variational autoencoder and the Gaussian mixture model clustering algorithm on image data and audio data for terrain type clustering. Our method enables the terrain type clustering even if one of the modalities (either image or audio) is missing at the test-time. We evaluated the clustering accuracy with a conventional multi-modal terrain type clustering method and we conducted ablation studies to show the effectiveness of our approach.

关键词： Vibrations Training Visualization Robot vision systems Predictive models Feature extraction Cameras

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Parameter estimation for a simplified model of an electrolytic capacitor in transient regimes 10

Parameter estimation for a simplified model of an electrolyt...

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10th international conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2021

作者： Barbulescu, Corneliu Dragomir, Toma-Leonida Department of Automation and Applied Informatics Politehnica University Timișoara Timișoara Romania

the real capacitors' behaviour in electric circuits modelled by a single capacity deviates from the ideal one. In order to find better compromises between precision and simplicity, different C-R-L models are used. In these models, C, R, L are called equivalent parameters and take constant values. Under these assumptions, the capacitors are modelled as lumped parameter subsystems although it is well known that the real capacitors are essentially distributed parameter systems. As highlighted in this paper, the capacitors are also time-variant subsystems. To prove this, we use two types of experimental data: data measured during the capacitor’s discharge process and data obtained from frequency characteristics. the article proposes two estimation methods of equivalent values for the model parameters C and R based on their time variance highlighted by the experimental data. the estimation methods use a system of equations associated with the discharging of capacitors, respectively, with the frequency characteristics via polynomial regression. the experiments were carried out with an electrolytic polymer capacitor rated 220 μF, 25 V, 2.5 A rms, 85 ℃, designed mainly for energy storage and filtering, the results being confirmed by experiments performed on other similar capacitors. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Digital storage

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. these issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. the data is gathered from a dataset on EV charging. the data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). the proposed MSNN-AO is utilized within the MATLAB platform. the proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. the MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. this highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. the MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes

IBN-STR: A Robust Text Recognizer for Irregular Text in Natu...

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international conference on Pattern Recognition

作者： Xiaoqian Li Jie Liu Guixuan Zhang Shuwu Zhang School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences

Although text recognition methods based on deep neural networks have promising performance, there are still challenges due to the variety of text styles, perspective distortion, text with large curvature, and so on. To obtain a robust text recognizer, we have improved the performance from two aspects: data aspect and feature representation aspect. In terms of data, we transform the input images into S-shape distorted images in order to increase the diversity of training data. Besides, we explore the effects of different training data. In terms of feature representation, the combination of instance normalization and batch normalization improves the model's capacity and generalization ability. this paper proposes a robust scene text recognizer IBN-STR, which is an attention-based model. through extensive experiments, the model analysis and comparison have been carried out from the aspects of data and feature representation, and the effectiveness of IBN-STR on both regular and irregular text instances has been verified. Furthermore, IBN-STR is an end-to-end recognition system that can achieve state-of-the-art performance.

关键词： Analytical models Text recognition Neural networks Training data Transforms Distortion Data models

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Multi-scale 2D Representation Learning for weakly-supervised moment retrieval

Multi-scale 2D Representation Learning for weakly-supervised...

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international conference on Pattern Recognition

作者： Ding Li Rui Wu Yongqiang Tang Zhizhong Zhang Wensheng Zhang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Horizon Robotics China Institute of Automation Chinese Academy of Sciences

Video moment retrieval aims to search the moment most relevant to a given language query. However, most existing methods in this community often require temporal boundary annotations which are expensive and time-consuming to label. Hence weakly supervised methods have been put forward recently by only using coarse video-level label. Despite effectiveness, these methods usually process moment candidates independently, while ignoring a critical issue that the natural temporal dependencies between candidates in different temporal scales. To cope with this issue, we propose a Multi-scale 2D Representation Learning method for weakly supervised video moment retrieval. Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates. Two dimensions in this map indicate the start and end time points of these candidates. then, we select top-K candidates from each scale-varied map with a learnable convolutional neural network. With a newly designed Moments Evaluation Module, we obtain the alignment scores of the selected candidates. At last, the similarity between captions and language query is served as supervision for further training the candidates' selector. Experiments on two benchmark datasets Charades-STA and ActivityNet Captions demonstrate that our approach achieves superior performance to state-of-the-art results.

关键词： Training Annotations Benchmark testing Pattern recognition Convolutional neural networks

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An experimental analysis of different approaches to audio–visual speech recognition and lip-reading 15th

An experimental analysis of different approaches to audio–v...

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15th international conference on Electromechanics and robotics Zavalishin’s Readings, ERZR 2020

作者： Ivanko, Denis Ryumin, Dmitry Karpov, Alexey St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Russia

ISBN: (纸本)9789811555794

In this paper, we have analyzed different approaches to audio–visual speech recognition. We mainly focused on testing different modalities fusion techniques, rather than other parts of AVSR (e.g., feature extraction methods). Tree audio–visual modalities integration methods were under consideration, namely GMM-CHMM, DNN-HMM and end-to-end approaches, defined as the most promising and commonly found in scientific literature. the testing was performed on two different datasets: on GRID corpus for the English language and on HAVRUS corpus for the Russian. Obtained results once again confirms the superiority of neural network approaches compared to the others in conditions when we have enough data to effectively train NN models, which was demonstrated by our experiments on the GRID dataset. On a more compact in size HAVRUS database, the best recognition results were demonstrated by the traditional GMM-CHMM approach. this paper presents our vision on current state of audio–visual speech recognition field and possible directions for the further research. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Neural networks

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Inception-based Deep Learning Architecture for Tuberculosis Screening using Chest X-rays

Inception-based Deep Learning Architecture for Tuberculosis ...

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international conference on Pattern Recognition

作者： Dipayan Das K.C. Santosh Umapada Pal National Institute of Technology Durgapur India University of South Dakota Vermillion SD USA Computer Vision & Pattern Rec. Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781728188089;9781728188096

the motivation for this work is the primary need of screening Tuberculosis (TB) positive patients in the severely resource constrained regions of the world. Chest X-ray (CXR) is considered to be a promising indicator for the onset of TB, but the lack of skilled radiologists in such resource constrained regions degrades the situation. therefore, several computer aided diagnosis (CAD) systems have been proposed to solve the decision making problem, which includes hand-engineered feature extraction methods to deep learning or Convolutional Neural Network (CNN) based methods. Feature extraction, being a time and resource intensive process, often delays the process of mass screening. Hence, an end to end CNN architecture is proposed in this work to solve the problem. Two benchmark CXR datasets have been used in this work, collected from Shenzhen (China) and Montgomery County (USA), on which the proposed methodology achieved a maximum abnormality detection accuracy (ACC) of 91.7% (0.96 AUC) and 87.47% (0.92 AUC) respectively. Considering these datasets, to the best of our knowledge, the obtained results are superior to the state of the art deep learning based works.

关键词： Deep learning Solid modeling Analytical models Design automation Computational modeling Face recognition Feature extraction

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Detail Fusion GAN: High-Quality Translation for Unpaired Images with GAN-based Data Augmentation

Detail Fusion GAN: High-Quality Translation for Unpaired Ima...

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international conference on Pattern Recognition

作者： Ling Li Yaochen Li Chuan Wu Hang Dong Peilin Jiang Fei Wang School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

Image-to-image translation, a task to learn the mapping relation between two different domains, is a rapid-growing research field in deep learning. Although existing Generative Adversarial Network (GAN)-based methods have achieved decent results in this field, there are still some limitations in generating high-quality images for practical applications (e.g., data augmentation and image inpainting). In this work, we aim to propose a GAN-based network for data augmentation which can generate translated images with more details and less artifacts. the proposed Detail Fusion Generative Adversarial Network (DFGAN) consists of a detail branch, a transfer branch, a filter module, and a reconstruction module. the detail branch is trained by a super-resolution loss and its intermediate features can be used to introduce more details to the transfer branch by the filter module. Extensive evaluations demonstrate that our model generates more satisfactory images against the state-of-the-art approaches for data augmentation.

关键词： Deep learning Image segmentation Computer vision Superresolution Generative adversarial networks Information filters Data models

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An Identifying Campus Garbage Application Based on Optimized Convolutional Neural Networks

An Identifying Campus Garbage Application Based on Optimized...

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international conference on robotics and automation Engineering (ICRAE)

作者： Hongyu Li Chuan Xu Qilong Wu Junxing Guo Xinhua Zhu Yan Su School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665406987

this paper proposes to present an effective and practical mobile garbage identification application named GCNet (Garbage Classification Network), which is capable of classifying different types of campus garbage and estimating the results using convolutional neural networks. the GCNet consists of three major parts. First, a data enhancement based on image stitching is designed to enrich data sets and enhance network robustness. then we propose a spatial attention for reducing model structure and detection accuracy enhancement. Deformable convolution, the third component, is used to solve the problem of loss of sampling details or burr of normal convolution. Furthermore, we deployed the GCNet model to the mobile terminal and designed a mobile terminal intelligent campus garbage identification application. the experimental results show that the proposed GCNet performs well on campus garbage detection with 89.8% mean average precision (mAP), which outperforms the state-of-the-art methods. the running time on our application could achieve 0.083s per image, meeting the real-time detection. the proposed method is effective and applicable for accurate and real-time campus garbage detection.

关键词： Deformable models Technological innovation Smoothing methods Convolution Neural networks Real-time systems Robustness

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Classification of Anode Current Signals Based on 1D Convolutional Neural Networks

Classification of Anode Current Signals Based on 1D Convolut...

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international conference on robotics and automation Engineering (ICRAE)

作者： Xi Chen Shiwen Xie Yongfang Xie Xiaofang Chen School of Automation Central South University Changsha China

ISBN: (纸本)9781665406987

Intelligent and refined production becomes the development direction of the aluminium electrolysis industry. Anode current signals (ACS) can reflect the local conditions of electrolytic cells, timely and accurately classify the anode current signals, which will help to achieve regionalization and fine control of cells. Anode current signals are typical multivariable time series, so it is difficult to obtain its discriminant features based on traditional spectrum classification methods. therefore, this paper presents a method to classify the anode current signals using one-dimensional convolutional neural networks (1D-CNN). In addition to the input layer and output layer, the proposed CNN model consists of 8 layers, including 3 convolution layers, 2 max-pooling layers, and 3 fully connected layers. the model can automatically extract the features from the original data, so as to realize the three types of anode current signals classification, namely, normal, anode effect (AE) and anode change (AC). the experimental results show that the classification accuracy reaches 87.6%, which verifies the effectiveness of the method.

关键词： Training Aluminum Pattern classification Feature extraction Data models Classification algorithms Convolutional neural networks

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