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Towards On-device Deep Neural Network Inference and Model Update for Real-time Gesture Classification

Towards On-device Deep Neural Network Inference and Model Up...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Mustapha Deji Dere Jo Ji-Hun Boreom Lee Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology Gwangju Korea

Deep learning resurgence ushered in the application of pattern recognition algorithms in high-impact research fields with impressive accuracy. In addition, deep neural networks (DNN) have recently been used to classify gestures for rehabilitation device control utilizing raw electromyography data. However, the computational resources required by a convolution neural network (CNN) are a constraint that often limits deployment to embedded devices for real-time inference. An optimized edge adaptive convolutional neural network using a short-time Fourier transform (STFT) spectrogram input was proposed in this study. The model’s classification accuracy was evaluated offline and on-device for inter-subject accuracy. Furthermore, an adaptive weight update approach was implemented to improve inference model accuracy due to degradation. The proposed model and optimization technique achieved offline accuracy of 92.19 % and 94.29 % for the raw and STFT input, respectively. However, the on-device accuracy for raw and STFT input to the model was 82.26 % and 85.19 %, respectively. On the other hand, the adaptive model update increased the respective accuracy by an average of 7% on-device. Finally, our study demonstrates the deployment of DNN on-device for real-time gesture classification inference.

关键词： Deep learning Degradation Adaptation models Time-frequency analysis Convolution Neural networks Real-time systems

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Building a Land Use and Land Cover (LULC) Classifier Using Decadal Maps 1

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2nd international conference on intelligent Computing, Information and Control Systems, ICICCS 2020

作者： Bharathi, D. Karthi, R. Geetha, P. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811584435

ISBN: (纸本)9789811584428

Global satellite program for remote sensing and earth observation has yielded huge volume of images with rich information. Geoportals such as Bhuvan and USGS host large number of satellite images and tools for analysis. The objective of this paper is to investigate the potential of using freely available satellite images to build a Land Use Land Cover classifier model (LULC) using machine learning approaches. The historically available LULC map is proposed to be used for identifying ground truth labels during classifier construction. Multispectral Landsat-8 images are used as input for classification by decision tree (DT), random forest (RF) and support vector machines (SVM). The data was mapped to six classes according to the international Geosphere-Biosphere Program (IGBP) classification scheme. The performance metrics used for evaluating the classifiers are accuracy, kappa coefficient, user, and producer accuracies. The study infers that random forest is able to classify LULC data with higher accuracy. The study provides a mean of building LULC maps from available data for multiple terrains. These classifiers can be used as an automated tool for generation of LULC maps. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Landsat

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intelligent Control of a Permanent Magnet DC Motor

Intelligent Control of a Permanent Magnet DC Motor

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international Carpathian Control conference (ICCC)

作者： Gheorghe Bujgoi Dorin Sendrescu University of Craiova Craiova Romania

This paper presents the use of artificial intelligence for the control of a permanent magnet DC motor. The system can be found especially in robotic systems that perform repetitive operations. The control law is generated by an intelligent Reinforcement learning algorithm. From the numerous variants of this type of algorithm, the Policy Iteration type algorithm was chosen. The algorithm was experimentally implemented using a data acquisition system and Matlab/Simulink software. The control system was tested for several variants of the load (by changing its inertia moment). The simulation and experimental results show that the intelligent control method based on reinforcement learning has better trajectory tracking and vibrations suppression.

关键词： Vibrations Trajectory tracking Software algorithms Reinforcement learning DC motors Permanent magnets Stability analysis

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AI-Enhanced Predictive Maintenance in Manufacturing Processes

AI-Enhanced Predictive Maintenance in Manufacturing Processe...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Ponrudee Netisopakul Nawarat Phumee Shool of Information Technology Knowledge Management and Knowledge Engineering Laboratory King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand School of Information Technology Data Technology and Innovation King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

This research aims to apply artificial intelligence technology to a manufacturing industry, specifically, to forecast temperature and insulation values of motors from the CNC machine. dataset from motor sensors are collected and forecasting models are trained using four deep learning models, namely, multilayer perceptron (MLP), long-short term memory (LSTM), LSTM autoencoder, and bidirectional LSTM (Bi-LSTM). Models are evaluated by measuring the deviation of forecasting values from the real values. Two measures, root mean square error (RMSE) and mean absolute error (MAE), are used to assess model’s performance. Experiments are conducted and found that the Bi-LSTM yielded the lowest RMSE and MAE numbers, hence, the best model to be selected. Further development has been implemented by integrating Bi-LSTM and genetic algorithm (GA) in order to optimize the model performance. Instead of searching the huge hyperparameter space of the neural network, the integrate GA-LSTM model using RMSE as a fitness function to reduce the search space and obtain the optimal or near optimal hyperparameters. The empirically best model is found which yields a lower RMSE value of 0.041 comparing to 0.18 when not optimized.

关键词： Deep learning Temperature measurement Temperature sensors Manufacturing processes Sociology Predictive models Forecasting

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Improved Single Haze Removal Algorithm Based on Color Attenuation Prior 2

Improved Single Haze Removal Algorithm Based on Color Attenu...

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2nd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2021

作者： Liu, Lijin Cheng, Guojian Zhu, Jiadong School of Intelligent Science and Information Engineering Xi'an Peihua University Shannxi Xi'an China School of Computer Science Xi'an Shiyou University Shaanxi Xi'an China

ISBN: (纸本)9781665428767

With continuous development and application of computer vision, images with high clarity and quality are increasingly important in training different kinds of models, especially in the field of automated driving and road safety monitoring. In this paper, through learning on the benchmarking dataset, we propose a novel quadratic model of color attenuation prior to calculate the scene depth map and then obtain the transmission matrix and restore the hazy-free image. Numerous experimental results show that the proposed approach is more accurate on sculpt the correlation between scene brightness, saturation and depth, therefore it outperforms the linear model in terms of both dehazing effect and clarity. Meanwhile, the objective and subjective comparison with the other state-of-the-art algorithms on the hazy images demonstrate the efficiency and effectiveness of the proposed method. © 2021 IEEE.

关键词： Color

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Machine learning-based BGP Traffic Prediction

Machine Learning-based BGP Traffic Prediction

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Talaya Farasat Muhammad Ahmad Rathore Akmal Khan JongWon Kim Joachim Posegga University of Passau Germany Northwestern University USA IUB Pakistan GIST South Korea

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Accurate Internet traffic predictions can provide support to network operators for applications such as traffic engineering, bandwidth allocation, anomaly detection, etc. We apply and compare different forecasting techniques (traditional and machine learning-based techniques) on real BGP data that is collected from two well-known Internet exchange points (IXPs) to derive BGP future volume-based predictions. Our experimental evaluation shows that multivariate Bayesian Ridge outperforms all other forecasting techniques we consider. Through univariate LSTM, we are able to predict new BGP volume-based features. Furthermore, to study the impact of dataset size on BGP forecasting, we perform experiments on three BGP dataset sizes, i.e., Short (one-month), Medium (three-months), and Long (five-months) Periods. Our results show that the Short-Period BGP dataset seems to be sufficient for getting accurate predictions. We also present a use case study (forecast Google Leak anomaly) that supports our experimental evaluations. We provide our collected BGP datasets publically which will be helpful to perform further research experiments and analysis regarding BGP traffic predictions.

关键词： Privacy Channel allocation Internet Bayes methods Security Forecasting Long short term memory

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Active learning - Competency Development Strategy 2nd

Active Learning - Competency Development Strategy

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Khatsrinova, Olga Yurievna Seliverstova, Olga Khatsrinova, Julia Tarasova, Ekaterina Barabanova, Svetlana Kazan Natl Res Technol Univ Kazan Russia Fed Ctr Educ Legislat Moscow Russia

ISBN: (纸本)9783030402747;9783030402730

The study is devoted to the development of legal competence in the study of the discipline "Jurisprudence". To enhance the learning activities were used interactive forms of practical exercises. The use of business and role-playing games, work with cases, the construction of Synquain (the word "synquain" comes from the French word "five" and means "a poem consisting of five lines"), the analysis of situations occurring in real professional activity leads not only to quality of the educational process, but also forms the legal consciousness of students. They increase their professional identity and actively participate in legal events.

关键词： Computer programming

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Development of Automatic Obscene Images Filtering Using Deep learning 1

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2nd international conference on Innovative Technology, engineering and Sciences, iCITES 2020

作者： Awad, Abdelrahman Mohamed Gunawan, Teddy Surya Habaebi, Mohamed Hadi Ismail, Nanang Electrical and Computer Engineering Department International Islamic University Malaysia Gombak Malaysia School of Electrical Engineering and Telecommunications UNSW Sydney Australia Electrical Engineering Department UIN Sunan Gunung Djati Bandung Indonesia

ISBN: (数字)9783030709174

ISBN: (纸本)9783030709167

Because of Internet availability in most societies, access to pornography has become a severe issue. On the other side, the pornography industry has grown steadily, and its websites are becoming increasingly popular by offering potential users free passes. Filtering obscene images and video frames is essential in the big data era, where all kinds of information are available for everyone. This paper proposes a fully automated method to filter any storage device from obscene videos and images using deep learning algorithms. The whole recognition process can be divided into two stages, including fine detection and focus detection. The fine detection includes skin color detection with YCbCr and HSV color spaces and accurate face detection using the Adaboost algorithm with Haar-like features. Moreover, focus detection uses AlexNet transfer learning to identify the obscene images which passed stage one. Results showed the effectiveness of our proposed algorithm in filtering obscene images or videos. The testing accuracy achieved is 95.26% when tested with 3969 testing images. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Realization of Competence-Based Approach in the System of engineering-Pedagogical Staff Development 2nd

Realization of Competence-Based Approach in the System of En...

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作者： Zhetesova, Gulnara Ibatov, Marat Smirnova, Galina Jantassova, Damira Shebalina, Olga Karaganda State Tech Univ Strateg Dev Karaganda Kazakhstan Karaganda State Tech Univ Karaganda Kazakhstan Karaganda State Tech Univ Ctr Engn Pedag Karaganda Kazakhstan Karaganda State Tech Univ Dept Foreign Languages Karaganda Kazakhstan

ISBN: (纸本)9783030402716;9783030402709

The study examines the projection of competence-based approach into the system of engineering-pedagogical staff development;and presented the design of the career enhancement programs of a new generation. It justified the transfer from the traditional structuring of subject-thematic content of pedagogical staff development to projecting a modular-competence content in the framework of the professional development model.

关键词： Competence-based approach Modular learning Pedagogical staff development

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Knowledge-Guided Fraud Detection Using Semi-supervised Graph Neural Network 22nd

Knowledge-Guided Fraud Detection Using Semi-supervised Graph...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Rao, Yizhuo Ren, Xiaoguang Duan, Chengyuan Mi, Xianya Cheng, Jiajun Chen, Yu You, Hongliang Gao, Qiang Zeng, Zhixian Wei, Xiao Acad Mil Sci Mil Sci Informat Res Ctr Beijing Peoples R China Natl Innovat Inst Def Technol Artificial Intelligence Res Ctr Beijing Peoples R China Natl Univ Def Technol Res Inst 63 Nanjing Peoples R China

ISBN: (纸本)9783030908881;9783030908874

Fraud detection is about finding unusual behaviors in the data, and it is essential for companies to detect fraudulent users to prevent unpredictable risks. The graph-based approaches model relationships into graphs for capturing the intricate characteristics of complex scenarios to detect fraudsters. However, it still faces the problem of data skew where labeled fraudsters are far fewer than unlabeled examples. Knowledge may help identify these unlabeled data, thus this paper combines domain knowledge with GNN and proposes a Knowledge-Guided Semi-supervised Graph Neural Network, namely KS-GNN, to address the problem of data skew. We utilize domain experts to design small amount of rules to roughly label unlabeled data as noisy and use a semi supervised method to train fraud detectors. By utilizing only 13 GFD rules conducted by domain experts, the performance of our method yields about 15% improvement over the state-of-the-art fraud detection methods CARE-GNN on banking transaction funds supervision datasets (BTFSD). Moreover, with some modification of the GFD rules on BTFSD, the performance of KS-GNN on other domain datasets such as IEEE-CIS Fraud Detection (https://***/c/ieee-frauddetection/data) and Yelp-Chi is also improved by about 5% on average compared with the state-of-the-art methods.

关键词： Fraud detection Semi supervised learning Graph neural network learning with noisy labels

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