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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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The Automated Control System of the Ferris Wheel Attraction

The Automated Control System of the Ferris Wheel Attraction

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2024 International Russian automation Conference, RusAutoCon 2024

作者： Boslovyak, P.V. Balashov, A.A. Bauman Moscow State Technical University Faculty of Robotics and Complex Automation Department of Vertical Transport Systems Moscow Russia

Special attention is paid to the program code, which reveals the logic of attraction control. Algorithms are analyzed, which ensure accurate synchronization of movement, effective load distribution on structural elements, as well as safety. The technical details of the program code implementation that supports the continuous and stable operation of the attraction are considered. The real loading of the Ferris Wheel attraction during its operation is considered. The issues of integrating modern technologies into safe procedures for the attraction operation to ensure maximum comfort and safety of visitors are also covered. The necessary elements that must be built into the design of the attraction to create an automated control system are considered. © 2024 IEEE.

关键词： Control systems

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Neural Network System for Monitoring the Condition of Equipment and Predicting Malfunctions

Neural Network System for Monitoring the Condition of Equipm...

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2024 Conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024

作者： Lazovskiy, Nikolay V. St. Petersburg Electrotechnical University 'LETI' Student of the Department of Robotics and Automation of Production Systems Saint Petersburg Russia

ISBN: (纸本)9798350360646

The scientific article 'Neural network system for monitoring equipment condition and fault prediction' explores an innovative approach to improving proactive maintenance of industrial equipment. The article presents a description of methods for continuous monitoring of the condition of machines and equipment. These methods allow you to collect a lot of data about the operation of equipment, and then use them to predict possible malfunctions. The neural network system contributes to more efficient maintenance management, reducing downtime and reducing repair costs. © 2024 IEEE.

关键词： condition monitoring data analysis data sensors equipment fault prediction industry maintenance neural network systems prediction accuracy production efficiency

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Method for Improvement of Biosignal Measurement Accuracy in the IoB System 27

Method for Improvement of Biosignal Measurement Accuracy in ...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Altay, Yeldos A. Uskenbayeva, Raisa K. Fedorov, Alexey V. Satbayev University Department of Robotics and Technical Means of Automation Almaty Kazakhstan Satbayev University Almaty Kazakhstan Itmo University Faculty of Control Systems and Robotics Saint-Petersburg Russia

ISBN: (纸本)9798350363708

This paper presents filtering methods for improvement of biosignal measurement accuracy - an ECG signal under the influence of electromagnetic interference, which has a significant impact on the operation of the IoB system. A single-frequency polynomial notch filter has been synthesized to filter this noise to improve the accuracy of ECG signal measurement. The results of the implementation of the synthesized notch filter demonstrated that the filter makes it possible to separate the informative component from the distorting noise and increase the accuracy of signal measurement during its implementation in comparison with wavelet filters. © 2024 IEEE.

关键词： Notch filters

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Water Indicator Measurement and Data Processing From Internet of Things Modules

Water Indicator Measurement and Data Processing From Interne...

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2024 International Conference on Electrical Engineering and Photonics, EExPolytech 2024

作者： Altay, Yeldos A. Bazarbay, Lashin Uskenbayeva, Raisa K. Fedorov, Alexey V. Kalpeyeva, Zhuldyz B. Satbayev University Department of Robotics and Technical Means of Automation Almaty Kazakhstan Satbayev University Almaty Kazakhstan Itmo University Faculty of Control Systems and Robotics Saint-Petersburg Russia Satbayev University Institute of Automation and Information Technologies Almaty Kazakhstan

ISBN: (纸本)9798350388879

This article presents the results of the implementation of a measuring system based on an IoT module for remote transmission of aggregate measurement data obtained during analytical monitoring of five water parameters via a wireless communication channel. A model of an experimental setup is presented and discussed, demonstrating the operability and consistency of the model for monitoring physical parameters during aggregate measurements. The obtained results of remote data transmission via a wireless communication channel revealed a statistically significant correlation between spring water parameters and the most influential predictor of acidity, conductivity, turbidity and oxidation-reduction potential is water temperature. The discovered relationship between the measured parameters is mainly associated with the effect of temperature on the processes occurring with an increase and decrease in the temperature of the analyzed water samples. © 2024 IEEE.

关键词： Data transfer

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Deep Learning and Ensemble Models for Accurate Heart Rate Estimation from PPG 21

Deep Learning and Ensemble Models for Accurate Heart Rate Es...

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21st IEEE India Council International Conference, INDICON 2024

作者： Dhatri Shankari, T.M. Ugarakhod, Rashmi Pes University Centre for Robotics Automation and Intelligent Systems (cRAIS) Department of Ece Bangalore India

ISBN: (纸本)9798350391282

Photoplethysmography (PPG) is a non-invasive, optical technique for measuring blood volume changes in peripheral tissues. In this paper, deep learning and ensemble methods are explored for an accurate estimation of heart rate directly from the PPG signals. Different models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Hybrid CNN-LSTM, Siamese network, Extreme Gradient Boosting Trees (XGBoost), and hybrid models of XGBoost-CNN-LSTM, have been evaluated for their performance using the PPG-DaLiA dataset. The performance of these models were evaluated using mean absolute error (MAE), standard deviation (SD) and the Percentage of Data within an Error range (PDE). The results show that the proposed CNN-LSTM architecture is effective for practical heart rate prediction. The paper also provides valuable insights about the strengths and weaknesses of these models and their possible applications in wearable health monitoring. © 2024 IEEE.

关键词： Convolution neural networks deep learning ensemble model heart rate estimation Long Short-Term networks photoplethysmography Siamese Network XGBoost

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Cascade LTI System for Acoustic Emission Digital Signal Processing: Result Visualization Via Scalogram For Defect Pattern Representation 27

Cascade LTI System for Acoustic Emission Digital Signal Proc...

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27th International Conference on Digital Signal Processing and its Applications, DSPA 2025

作者： Altay, Yeldos A. Fedorov, Alexey V. Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (纸本)9798331518752

Modern acoustic emission (AE) diagnostic systems are sensitive instruments for detecting developing defects. A significant limitation of the AE method is the difficulty of isolating signals against the background of interference, which significantly reduces the signal-to-interference ratio. Therefore, in this article a cascade filtering scheme for visualizing acoustic signatures through a scalogram is proposed to isolate AE signals that characterize the process of defect formation. Acoustic portraits of the isolated signals characterizing the process of defect formation are given. It is shown that the amplitude of the AE signal from a defective structure during crack formation is characterized by the highest values of the signal amplitude and has a rich frequency-time structure, the signal shape of which is determined by discrete AE. © 2025 IEEE.

关键词： acoustic emission cascade filtering crack detection defects LTI systems pore time-frequency analysis

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Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

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Tsinghua Science and Technology 2024年第1期29卷 244-256页

作者： Wenjie Geng Zhiqiang Cao Peiyu Guan Fengshui Jing Min Tan Junzhi Yu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Grasp detection plays a critical role for robot *** pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and ***,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are *** is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains ***,the channel information in high-level feature is also not well ***,the performance of grasp detection is *** solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle *** low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature *** a hierarchical fusion guarantees the quality of grasp ***,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective *** such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the ***,an information enhancement module is added before the encoder to reinforce input *** proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

关键词： grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual

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Signal Processing for Acoustic Emission Signature Analysis and Defect Detection 26

Signal Processing for Acoustic Emission Signature Analysis a...

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26th International Conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Altay, Yeldos A. Kuzivanov, Dmitry O. Altay, Dinara A. Fedorov, Alexey V. Satbayev University Department of Robotics and Technical Means of Automation Almaty Kazakhstan ITMO University Faculty of Control Systems and Robotics Saint-Petersburg Russia Military Engineering Institute of Radio Electronics and Communications Department of Natural Sciences Almaty Kazakhstan

ISBN: (纸本)9798350361728

This article presents the results of implementing a digital signal processing method for analyzing the acoustic emission (AE) signature and detecting defects such as cracks and pores. The signal processing method is based on a cascade connection of digital high-pass filters and ensures the selection of informative signals and increases the signal-to-interference ratio. In order to control the formation of defects in the process of selective laser melting, the paper presents the results of experimental testing of the cascade filtration method for detecting defects in the internal structure such as cracks and pores. The signal components recorded during the development of defects during the growth of additive manufacturing products were extracted. AE signatures were analyzed and statistical relationships were assessed. The relationship between the parameters of the AE signal and the values of laser radiation power has been identified, which characterizes the process of defect formation. © 2024 IEEE.

关键词： High pass filters

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A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

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Science China(Technological Sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

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