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Research on graph-based SLAM for UVC disinfection robot

Research on graph-based SLAM for UVC disinfection robot

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2021 IEEE international conference on Real-Time computing and Robotics, RCAR 2021

作者： Tan, Xuan Zhang, Hui Zhou, Xidong Zhong, Hang Liu, Li College of Electrical and Information Engineering Changsha University of Science and Technology Hunan Province410012 China Hunan University School of Robotics Visual Perception and Control Technology National Engineering Laboratory Hunan Province410082 China College of Electrical and Information Engineering Hunan University Hunan Province410082 China

ISBN: (纸本)9781665436786

With the emergence of the COVID-19 pandemic, more and more non-contact mobile disinfection robots have appeared in the medical field, which have made great contributions to the fight against the epidemic. Aiming at the problems of single disinfection method, single application scenario, low degree of intelligence and lack of autonomous mobile disinfection in existing disinfection robots, this paper proposes and designs a disinfection and epidemic prevention intelligent robot called Aimi-Robot UVC, which is based on graph-optimized slam algorithm to complete the localization and map creation functions of the robot in the unknown environment. After testing in the isolated single ward of the hospital, the realtime localization accuracy reaches 0.04m, which provides high-precision and high-reliability localization for the disinfection robot in the hospital scene and has great practical significance for the application of intelligent disinfection robots in epidemic prevention and control. © 2021 IEEE.

关键词： Graphic methods

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automatic Feature Extraction and Traffic Management Using Machine Learning and Open CV Model

Automatic Feature Extraction and Traffic Management Using Ma...

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2021 IEEE international conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2021

作者： Prakash, M. Saravanakumar, C. Lakshmi, S. Kanaga Rose, J Dafni Praba, B. Data Science and Analytics Center Karpagam College of Engineering Coimbatore India St. Joseph's Institute of Technology Department of Information Technology Chennai India Sri Muthukumaran Institute of Technology Department of Management Studies Chennai India Loyola-ICAM College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9781665435215

Artificial intelligence covers a vast area of the real time domain which supports humans for all activities. Machine learning (ML) techniques learn the data and react based on the properties of these data. The properties are identified by extracting the features from the extracted data. Image and video processing methods are essentials in real time application due the IoT (Internet of Things) devices. The data of these types of data is more complex and also high dimensional in nature. These dimensions are reduced by performing reduction techniques before performing the classification process. The proposed ML model targets the traffic management by automating the traffic light based on the flow in the road. The traffic priority is assigned based on the congestion level on the road. The traffic classification is done by considering different features and infrastructure maintained by the city. Existing system suffers the problem due to the following reasons such as traffic congestion, longer waiting time, improper maintenance of the traffic signal, and high carbon emission and so on. The objective of the proposed model is to reduce the traffic congestion by performing traffic flow conditions and make the people comfortable level during the travel. © 2021 IEEE.

关键词： Image enhancement

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Visual Positioning System of Bag Removal Robot Based on Deep Learning

Visual Positioning System of Bag Removal Robot Based on Deep...

引用

6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Bo Liu Ruiyin Tang College of electrical engineering North China University of Science and Technology Tangshan China School of electronic and control engineering North China Institute of Aerospace Engineering Langfang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In industrial production, the dumping of raw material packaging is mostly done manually, which not only affects production efficiency but also endangers the health of workers. This article proposes an improved algorithm based on YOLOV5, which uses Mobiletv3 instead of the YOLOV5 backbone network, adjusts parameters using an improved SE attention mechanism, and optimizes the model using the LeakyReLU activation function. The entire system uses an improved algorithm to detect the target bag rope, uses a depth camera to obtain the target coordinates, and finally calculates the target coordinates in the robot coordinate system. Compared with Fast RCNN and YOLOV5 algorithms, the improved YOLOV5 MobileNetV3 algorithm has a knot detection accuracy of 97.5%, a recall rate of 92.43%, and an F1 score of 94.8. The model has a small volume, fast detection speed, and a single image detection time of only 187ms, with a mapping rate of 97.5%. The error between the coordinates obtained after hand eye calibration and the actual coordinates of the robotic arm in the XYZ axis direction does not exceed 3mm, and the actual coordinates are much smaller than the target size.

关键词： YOLO Deep learning Solid modeling Accuracy Robot kinematics Robot vision systems Signal processing algorithms Production Position measurement Cameras

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Clustering by Improved PSO based Jaya Algorithm for Energy Optimization of Wireless Sensor Networks

Clustering by Improved PSO based Jaya Algorithm for Energy O...

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international conference on Electronics, Communication and computing Technologies (ICECCT)

作者： P. Malarvizhi G. Kavithaa Computer Science and Engineering Knowledge Institute of Technology Salem India Electronics and Communication Engineering Government College of Engineering Salem India

In recent years, industries have automated processes which mean the amount of human participation has decreased, resulting in the Fourth Industrial Revolution. A highly distributed self-organizing system known as a Wireless Sensor Networks is employed in so many control systems such as monitoring the surroundings, automate the reporting, and detecting the event. High bandwidth needs, high power consumption, security and quality of service delivery are some of the obstacles that wireless sensor networks must overcome. Each sensor node in a wireless sensor networks has a different power consumption rate based on the non-uniformity of event detection and the interspace between the sink node and sensor node. This shortens the lifespan of the network and causes an energy difference between the sensor nodes. Particle Swarm Optimization based Jaya algorithm (PSO-J) has been experimented to lower the power consumption of the sensor node by improving the selection of cluster head. The proposed algorithm provides better results than existing clustering algorithms.

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The Role of Medical Informatics in Facilitating Evidence-Based Healthcare Practices

The Role of Medical Informatics in Facilitating Evidence-Bas...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Gupta, Manish Saeed, Ali Srinija, K. Kumar, Amit Jayanthi, Muthuswamy Lalitha, Yaragudipati Sri Lovely Professional University Phagwara India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

Medical knowledge, technology, and data have changed healthcare. A new method, OptimizedClinicalDecide (OCD), is transforming evidence-based health care and achieving this critical goal. To treat OCD, it is critical to use medical data as well as cutting-edge algorithms and learning systems that evolve over time. This strategy effectively addresses the long-standing issue of incompatible healthcare data sets, making it perfect for data integration. OCD's dynamic learning engine is continually adding new patient data to the knowledge base, guaranteeing that health care practitioners have the most up-to-date patient health information. Modern healthcare relies on decision-making aid, and flexible decision optimization techniques enhance it by providing patient-specific guidance. OCD's ability to use cutting-edge medical knowledge and promote lifelong learning are its assets. Advanced analytics and personalized patient outcome prediction methodologies increase forecasting and prognosis. The proposed strategy makes use of mobile health resources and is appropriate for the present era of patient-centered treatment. MEPCC promotes patients to work together on health-care decisions. Traditional approaches suffer with adaptability, data integration, and decision support. OptimizedClinicalDecide addresses these challenges and increases patient involvement. According to evidence, OCD has the ability to transform health care. This enhances our mission of providing educated, specialized, and collaborative patient care. © 2025 IEEE.

关键词： Personalized medicine

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SAGC circuit design and implementation for FSK modem through radio channel 18

SAGC circuit design and implementation for FSK modem through...

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18th international conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Sutam, Apichat Magkeethum, Apiwat Wardkein, Paramote Koseeyaporn, Jeerasuda Sae-Chia, Sukkharak King Mongkut's Institute of Technology Faculty of Engineering Telecommunications Department Ladkrabang Thailand Phranakhon Si Ayutthaya Rajabhat University Faculty of Science and Technology Thailand

ISBN: (纸本)9780738111278

In this research, the sinusoidal automatic gain control circuit (SAGC) has been fabricated by using differentiator circuits, multiplier circuits, a summation circuit, a square root circuit and a division circuit. By using an Operational amplifier (Op amp) and 2 capacitors, the differentiator circuit has been constructed An Op amp and IC AD633 used as a multiplier are combined for fabricating the square root circuit [1]. Apart from evaluating the SAGC characteristics, the FSK recovery process for transmitting the (Global Positioning System) GPS coordinates through radio channel has been improved via the proposed SAGC circuit. The results show that the proposed circuits and its implementation operate perfectly and agree well with the conventional theory. © 2021 IEEE.

关键词： Timing circuits

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Aluminium-Metal Oxide (Al2O3) Performance Evaluation;Utilising Nanofluid Cooling Technology and Artificial Intelligence for Maximum Power Point Tracking of Perovskite Solar Cells

Aluminium-Metal Oxide (Al2O3) Performance Evaluation;Utilisi...

引用

Sustainable computing and Integrated Communication in Changing Landscape of AI (ICSCAI), international conference on

作者： Arnav Kotiyal Layth Hussein Krishna Kant Dixit Shrikant Upadhyay Nimmalaharathi J. Relin Francis Raj Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of EIE (School of Engineering) Sree Vidyanikethan Engineering College AP Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Improving the efficiency of PV systems linked to the grid is the primary goal of this research. One factor that might affect productivity is the maximum power point tracker's accuracy. Incremental conductance (INC) and perturb-and-observe are two of the few dependable methods for maximum power point tracking (MPPT). Make the MPPT control system more efficient with the aid of AI. This study enhances the design of an ANN for an MPPT system, which in turn increases the power output of a grid-connected PV system. Prior research on ANN-based MPPT systems mostly used temperature and solar irradiance as their only parameters; however, this study offers new parameters that significantly enhance the performance of the ANN. Secondly, neuron activation function type, learning technique, and the number of hidden layer neurons can all be optimized using a genetic algorithm (GA). Simulation and comparison results demonstrate that the revised ANN architecture enhances MPPT functionality.

关键词： Maximum power point trackers Solar irradiance Productivity Photovoltaic systems Simulation Neurons Artificial neural networks Computer architecture Artificial intelligence Genetic algorithms

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Maximizing Grid Intelligence: Harnessing Substation Load Data via Machine Learning

Maximizing Grid Intelligence: Harnessing Substation Load Dat...

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IEEE international conference on Smart Grid Communications (SmartGridComm)

作者： Elnaz Azizi Weiqi Hua David Wallom Malcolm McCulloch Department of Engineering Science Univerity of Oxford UK Electronic and Electrical Engineering Department University of Birmingham UK

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

Distribution grids are evolving due to rising electricity demand and renewable energy integration, requiring efficient operation and effective planning. To achieve this, one essential step is translating the available load data into actionable insights. Machine learning (ML) approaches have emerged as promising solutions, leveraging increasing availability of data and computational capabilities. While research papers exist on applications of ML in power grids, a review in low-voltage substation-level is missing, an aspect that will be explored in this paper. The significance of emphasis at this level is twofold: ensuring privacy protection while gaining insights into consumption behavior, and eliminating the need for installing new meters or adjusting communication infrastructure. The paper covers three main ML algorithms, supervised, unsupervised, and reinforcement learning, their applications, while providing a critical discussion of their strengths and limitations. Furthermore, the paper provides recommendations for future studies.

关键词： Low voltage Substations Scalability Training data Reinforcement learning Stability analysis Planning Smart grids Reliability Stakeholders

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Implementation of Peak Demand Reduction on a Distribution Feeder using Python-OpenDSS co-simulation 7

Implementation of Peak Demand Reduction on a Distribution Fe...

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7th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2021

作者： Subramanya, Shilpa Aralasurali Parathodiyil, Meena Nagashree, A.N. B.M.S. College of Engineering Dept. of Electrical Engineering Bangalore India

ISBN: (纸本)9781665428491

In India, the power distribution is the weakest and the most crucial segment in the power system. The major problem faced by the power distribution sector is the management of peak load. Peak load condition leads to electric power interruption which brings down the consumer satisfaction. The proposed work focuses on prevention of peak load on urban distribution feeder. This situation may occur for small duration at any time in a day. The work includes monitoring for peak load condition on the distribution feeder. On occurrence of peak load, an automatic direct load control (DLC) algorithm will be executed which automates the curtailment process for the available flexible loads. In the present work, OpenDSS-Python co-simulation has been implemented with the aim to achieve the overload relief by changing the load curve of the distribution feeder. Implementation of DLC-Demand side management (DSM) technique reduces the supply-demand mismatch, decreases the line loss and aids in congestion management during peak load on the distribution feeder. © 2021 IEEE.

关键词： Python

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AI-Enabled Safe and Scalable Pet Care: An Intelligent System for Real Time Health Monitoring and Care

AI-Enabled Safe and Scalable Pet Care: An Intelligent System...

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international conference on ent computing and control Systems (ICICCS)

作者： Aravind G Jeeva S Selvaganesh D S.P. Sasirekha Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Modern pet health management requires advanced solutions because the world demonstrates rising demand for companion animals. The current pet care systems operate without real-time data monitoring because they do not adapt to shifting diagnostic criteria. An AI-powered pet management system based on Internet of Things (IoT) links a secure cloud-edge computing system to offer these capabilities. A proposed system depends on strong machine learning techniques to deliver persistent health tracking while simultaneously analyzing behavioral patterns and carrying out automatic care operations. Through its IoT-connected smart sensors the system obtains real-time data which enables it to identify anomalies as soon as possible while providing tailored medical solutions. A modular distributed architecture design offers better system adaptability thus making possible seamless operations between multiple operational domains. Security is of utmost importance in the system so a combination of encryption layers works together with authentication protocols and monitored data channels serve to secure both privacy and data integrity. The experimental findings reveal that predictions about behavioral outcomes together with optimized resource usage and system protection function at superior rates than traditional methods do. A thorough analysis of both ethical practices and data security measures exists for automated pet supervision to facilitate responsible program execution. This recommended system gives remote accessibility together with multi-functional pet care operations to provide safe services which benefit all pet owners along with veterinarians and additional stakeholders. The future research agenda includes developing hybrid federated learning systems to deliver individualised diagnoses as well as using diversified health data from various sources to conduct more thorough examinations while striving to enable this monitoring method in various animal care settings. Such resear

关键词： Ethics Animals Data security Scalability Computer architecture control systems Real-time systems Internet of Things Stakeholders Monitoring

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