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Augmented Intelligence and Sustainable systems (ICAISS), International conference on

作者： S M Ramesh S Rajeshkannan Sumit Pundir Navdeep Dhaliwal Sanjay Mishra B Shanthi Saravana Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering St.Joseph’s College of Engineering OMR Chennai Tamil Nadu India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Division of Research and Development Lovely Professional University Phagwara Punjab India Division of Research and Innovation Uttaranchal University Dehradun Uttarakhand India Department of Electrical and Electronics Engineering Adhiparasakthi College of Engineering Kalavai Tamil Nadu India

The advanced innovation in the power electronics system paved a numerous ways in the development and monitoring of power consumption. This helps to maintain the demand side management to adopt several measures in limiting the consumption of power. This is implemented through embedded system with artificial intelligence techniques. These systems are highly reliable and stable in performing operations. The important objective of the proposed system includes the power monitoring and controlling through embedded controllers. These systems are integrated with wireless sensor network for enabling data storage in the network with higher security concerns. The stored data are used for optimization process. This includes image processing techniques with feature extraction. The power monitoring system is implemented by automatically evaluating the environmental conditions through sensors and functions based upon the priority. They are implemented with remote monitoring and control systems with providing real time information to the user’s mobile phone. The communication network are enhanced through the internet of things. This is enhanced through priority scheduling techniques through random forest algorithm. Thus, the overall system helps in enabling optimum power consumption through machine learning.

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2022 IEEE International conference on Real-Time Computing and Robotics, RCAR 2022

2022 IEEE International Conference on Real-Time Computing an...

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2022 IEEE International conference on Real-Time Computing and Robotics, RCAR 2022

ISBN: (纸本)9781665469838

The proceedings contain 128 papers. The topics discussed include: design and validation of a master-slave continuum robot for maxillary sinus surgery;a magnetically steerable and automatically propulsion guidewire robot system for vascular interventional surgery;design of lifting mechanism of transfer robot for the elderly;design and analysis of an upper limb exoskeleton robot for stroke rehabilitation;design of knee exoskeleton robot based on human physiology;design of a continuum robot system with object detection for the diagnosis of vocal fold lesions;design of a miniaturized magnetic actuation system for motion control of micro/nano swimming robots;closed-loop electromagnetic actuation system for magnetic capsule robot in a large scale;an autonomous fire-fighting robot with ackermann steering mechanism;design of an autonomous robot system for oil sampling in ultra-high voltage substation;a soft stretch-flexible pressure sensor for tactile sensing on nonplanar surfaces;teleoperation of dexterous micro-nano hand with haptic devices;and measurement and analysis of low-voltage power carrier characteristics.

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Improving Drone Agility and Stability with advanced Flight control systems Using MPU-6050 IMU and High-Fidelity Simulation

Improving Drone Agility and Stability with Advanced Flight C...

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Cybernation and Computation (CYBERCOM), International conference on

作者： Sandeep Sing Kang Nongmeikapam Thoiba Singh Richa Grover Department of Computer Science And Engineering Chandigarh University Punjab India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The study intends to improve drone capabilities for recreational use by creating a strong flight control system. The effort aims to educate drone enthusiasts and specialists on a novel approach to assessing and testing UA V flight control systems using high-fidelity simulation and MATLAB SimulinklFPGA Hardware-in-the-Loop (HIL). The MPU 6050 IMU and other sophisticated sensors and control algorithms enable the system to achieve exceptional flight stability, accuracy, and agility. When compared to conventional drone designs, extensive testing shows enhanced trajectory tracking, agility, durability, and reliability. The accomplishments of the research highlight the possibility of additional developments in autonomous drone systems, such as the incorporation of sophisticated algorithms such as computer vision and swarm intelligence. With planned improvements ranging from intelligent navigation to energy efficiency optimizations for extended flight endurance, the study's open-sourcing approach fosters cooperation and creativity in the development of future drone technology.

关键词： Trajectory tracking Reliability engineering Stability analysis sensor systems sensors Particle swarm optimization Optimization Drones Aerospace control Testing

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Navigation and obstacle avoidance technology for warehouse tracking AGVs based on multi-sensor information fusion

Navigation and obstacle avoidance technology for warehouse t...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE International conference on

作者： Naiwen Zhang College of Advanced Engineers University of Science and Technology Beijing Beijing China

The Automated Guided Vehicle (AGV) is a device that uses various sensor technologies, automation, and multiple control systems. AGVs have many navigation methods, of which the magnetic stripe navigation system is well developed. The magnetic stripe navigation AGV positioning is carried out precisely and at a lower cost level. However, the magnetic stripe is prone to breakage, and the AGV can only travel according to the magnetic stripe and cannot be intelligently avoided or the task changed in real-time by the control system. This paper draws on magnetic stripe navigation tracking principles, using grey-scale sensors for stable path tracking; distance sensors are combined for timely obstacle avoidance; Bluetooth is used to obtain approximate indoor locations for path planning. The navigation system simultaneously combines multi-sensor information for navigation, ultimately enabling the AGV to be quickly positioned accurately along the prescribed path and return to the route in time to avoid obstacles.

关键词： Electrical engineering Remotely guided vehicles Navigation control systems Real-time systems Trajectory Collision avoidance

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Design of a Deep Learning-Based Autonomous Vehicle Environmental Perception and Decision-Making system

Design of a Deep Learning-Based Autonomous Vehicle Environme...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： Hulin Jin Zhiran Jin Yong-Guk Kim School of Computer Science and Technology Anhui University Hefei China Foothill Preparatory School Temple City California USA Department of Computer Engineering Sejong University seoul South Korea

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Advances in computing power and sensor technology have propelled substantial advancements in autonomous vehicles, particularly in high-precision positioning, environmental perception, and intelligent decision-making. This study introduces a novel deep learning-based environmental perception and decision-making system for autonomous vehicles aimed at mitigating the challenges encountered in current studies. The proposed system employs advanced machine learning algorithms and deep neural network technology to rapidly and accurately adjust to a variety of intricate driving scenarios. The system integrates a range of sensors, encompassing cameras, radar, and Lidar, to thoroughly detect objects in the vehicle's environment and enhance driving safety and efficiency. Detailed methodologies, encompassing sensor fusion techniques and model construction, are delineated. Simulation results attest to the system's efficacy in precisely perceiving the environment and making astute driving decisions. Through the comprehensive analysis, the proposed system demonstrates superior adaptability, generalization capabilities, and performance relative to traditional rule-based control methods, thereby offering significant potential for enhancing the safety and efficiency of autonomous driving.

关键词： Adaptation models Machine learning algorithms Laser radar Simulation Decision making Artificial neural networks sensor fusion Cameras Safety Autonomous vehicles

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Special Issue on Computational Intelligence for IoT-based Human Activity Recognition

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第4期6卷 725-727页

作者： LI, X. I. A. O. L. I. CHEN, H. U. A. N. H. U. A. N. PLOETZ, T. H. O. M. A. S. WU, M. I. N. CHEN, Z. H. E. N. G. H. U. A. ASTAR Agcy Sci Technol & Res Inst Infocomm Res 1 Fusionopolis Way Singapore 138632 Singapore Univ Sci & Technol China Sch Comp Sci Hefei 230026 Peoples R China Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

The papers in this special section focus on computational intelligence for Internet-of-Things-based human activity recognition (HAR). HAR benefits numerous real-world applications. For instance, it can be adopted in a healthcare service system to monitor the rehabilitation processes of patients. Another important application of HAR is in security and surveillance which require to analyze human behaviors and detect anomalies in specific areas. Finally, in human computer interface, recognizing human activities can be used to control robots and play virtual reality games. Many Internet of Things (IoT) sensors have been utilized for human activity recognition, such as wearable sensors, smartphones, radio frequency (RF) sensors. Owing to the rapid development of wireless sensor networks in IoT, a large amount of data have been collected for the recognition of human activities with different types of sensors. Conventional computational intelligence algorithms, such as shallow neural networks, require to manually extract some representative features from large and noisy sensory data, which may hinder their performance in real-world applications. Alternatively, the more advanced computational intelligence algorithms of deep neural networks have achieved great successes in many challenging research areas, such as image recognition and natural language processing. The key merit of the deep neural networks is the ability to automatically learn more accurate representative features from massive amount of data, without going through the manual and time-consuming feature extraction process.

关键词： Special issues and sections Activity recognition Human factors Feature extraction Wireless sensor networks Neural networks Internet of Things

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advanced Paraglider Simulator control system 22

Advanced Paraglider Simulator Control System

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22nd International conference Computational Problems of Electrical Engineering (CPEE)

作者： Sivera, Richard Stasek, Petr Vorlicek, Jakub Calta, Lukas Kropik, Petr Karban, Pavel Univ West Bohemia Dept Elect & Computat Engn Plzen Czech Republic

ISBN: (纸本)9781728184302

control systems for every simulator have to be fast and precise. These are exact properties that are needed when designing a paraglider simulator. With it's complex set of controls and complicated aerodynamics physics, paraglider simulator requires a very precise system to mimic it's real world counterpart behaviour. From construction to software solution, a team of people with various sets of skills was put together to develop the control system, that is worthy of simulator going into production. This paper will go over how the control system was developed, what parts we're chosen for it and what can we expect from such system.

关键词： paraglider simulator control servomotor microcontroller sensor

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IoT interface Device for Sensing Arsenic in Contaminated Water

IoT Interface Device for Sensing Arsenic in Contaminated Wat...

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Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), International conference on

作者： Shivani Pandey Satanand Mishra Ravi Kant Jain Department CARS & GM CSIR-Advanced Materials and Processes Research Institute (AMPRI) AcSIR Bhopal MP India Department of CARS & GM CSIR-Advanced Materials and Processes Research Institute (AMPRI) AcSIR Bhopal MP India Information Technology Group CSIR-CMERI AcSIR Durgapur India

Water pollution is a serious problem in different parts of the world. In addition, water quality must be monitored to ensure that the water is provided safely for drinking and other purposes. Too high a concentration of Arsenic ions in drinking water is the cause of many health problems, including heart problems, neurological problems, etc. Water sampling and laboratory analysis are required for traditional water quality monitoring. In this paper, we discussed an IoT-based interfacing sensor device for sensing arsenic contaminants in water where IoT cloud computing networks enable the integration of a variety range of mechanical and electronic devices. A Node MCU device is used for data transmission which emphasizes on Wi-Fi-controlled interface devices and IoT-enabled communication protocol for the detection of water contaminants. This system is connected to an IoT cloud platform to store the data for analyzing purposes where Red-Green-Blue (RGB) color detection occurs by identifying the wavelength of contaminants. The system makes use of IoT to display the output in real-time for on-site and off-site monitoring via mobile phone. The system makes use of IoT to display the output in real-time for on-site and off-site monitoring via mobile phone, The major advantage of IoT technology is that it easily connects devices and stores the generated data in the cloud. With the help of command control systems, data can be used for appropriate applications to make human life easier and safer while considering Industry's impact. The acceptable limits set by WHO and the Bureau of Indian Standards for Arsenic are 0.05 mg/litres and 0.01 mg/l respectively. Therefore, a smart and intelligent device that can be used for measuring Arsenic content which is very necessary today to ensure the health of human life in society.

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2022 2nd International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022

2022 2nd International Conference on Advances in Electrical,...

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2nd International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022

ISBN: (纸本)9781665411202

The proceedings contain 223 papers. The topics discussed include: a method for waste segregation using convolutional neural networks;ach reference model- a model of architecture to handle advanced cyberattacks;proficient evaluation of visual cryptography using transposition cipher and bit reversal techniques;video captioning based on image captioning as subsidiary content;an enhanced energy efficient protocol for wireless body area network;self-risk assessment model embedded with conversational user interface for selection of health insurance product;a comparison between different kernels of support vector machine to predict cardiovascular diseases using phonocardiogram signal;traffic prediction for intelligent transportation system using machine learning;investigation of new protocol: cyclic shift in aggressive packet combining scheme to combat the error in same bit location;comparison of performance of machine learning algorithms for cervical cancer classification;blackhole attack detection based on trust calculation mechanism in wireless sensor networks;and symmetry-protected topological phase classification using hybrid quantum convolutional neural network with three quantum filters.

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Design of Real-time Centroid Extraction system for Shack–Hartmann sensor Based on FPGA

Design of Real-time Centroid Extraction System for Shack–Ha...

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Electronic Technology, Communication and Information (ICETCI), IEEE International conference on

作者： Shen Xiaocui Huang Zhangqin Gao Han Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

Hartmann wavefront sensor is applied widely in adaptive optics systems. Considering the real-time performance requirements and processing amount of Hartmann sensors in computing the centroids of points, this article proposes an FPGA-based real-time image processing and centroid extraction system. The system adopts FPGA as the core processor and uses the connected domain labeling algorithm and the gray centroid method to complete the centroid calculation. In addition, the calculation results are output simultaneously through multiple DAs to drive the device under test to achieve image wavefront restoration. At the same time, the image data is uploaded to the host computer in real-time through the Ethernet interface. The system has excellent performance in real-time image processing, image output quality, and system control stability. Experiments show that it is an efficient technical means of providing real-time wavefront processing measurement for the Hartmann sensor in various application scenarios, such as adaptive optical correction.

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