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Internet of Things Technology for Photovoltaic Smart Sprinkler Systems and Its Analysis

Internet of Things Technology for Photovoltaic Smart Sprinkl...

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2023 SPE Nigeria Annual international conference and Exhibition, NAIC 2023

作者： Chinonyelum, Ejimuda Kingsley, Okoli Lilian, Uzodiagu Juliet, Olusola Department of Mechanical Engineering Texas A & M University United States Controls Lab Texas A & M University United States Department of Computer Science and Knowledge Discovery Saint Petersburg Electrotechnical University LETI Saint Petersburg Russia Artificial Intelligence Lab University of Nigeria Nsukka Nigeria Department of Electrical and Electronics Engineering Bells University of Technology Ogun State Ota Nigeria Department of Electrical and Electronics Engineering Afe Babalola University Ekiti State Ado-Ekiti Nigeria

ISBN: (纸本)9781959025177

The advancement of technology has brought about ease of life with the help of technology automation which can be applied virtually in all aspects of living. With the emergence of internet of things, prototyping real state systems has become less expensive and easy to embark on thus creating platforms for development in the oil & gas sector. This research aims to analyze, design, and develop an effortless system powered via solar energy for the control of a sprinkler system and liquid flow void of human intervention. The system was programmed using an Arduino board and other hardware at an adjustable set time with Arduino software. The research is geared at reducing human efforts in irrigation systems, to save cost and time in sprinkling liquid on any system. The use of some weather parameters like humidity, temperature, and moisture sensors;Arduino kits and GSM module constitutes the smart irrigation system setup. The system is automated to feed the necessary quantity of liquid to any given surface based on design parameters with the moisture sensor programmed within 0 to 1023 (0-100%) range. The system adapted a method of pump-start at 40% moisture reading. Various analysis of the research findings has been presented in the work with applicability in remote areas and oil fields void of human intervention. Copyright © 2023 Society of Petroleum Engineers.

关键词： automation

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Human novel pose generation with pose optimization and importance sampling based on NeRF 16

Human novel pose generation with pose optimization and impor...

引用

2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Li, Ke Zhang, Binbin Su, Zhiyong Li, Weiqing School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The Neural Radiance Field (NeRF) method has emerged as a groundbreaking technique for human reconstruction, enabling the generation of high-quality, photorealistic rendering of reconstructed objects. Despite its promising results, several challenges persist, particularly when it comes to generalizing to unknown poses during the generation of virtual human animations. This paper proposes a method for generating and optimizing novel human poses based on NeRF. Given that existing pose-driven NeRF methods heavily rely on the accuracy of SMPL body parameters, we implemente human pose optimization based on motion capture data as prior knowledge, resulting in more accurate SMPL parameters, which contributes to a more precise human canonical model. Additionally, we propose a pose-driven deformation field based on linear blend skinning, combining blend weight fields with the 3D human skeleton to achieve precise mapping from observed poses to the canonical pose. By estimating the camera ray transmittance distribution during volume rendering, we propose importance sampling method avoiding sampling points in free space, which further improves rendering quality. Extensive experiments demonstrate that our method yields significant improvements in rendering quality for both training poses and novel poses, as evidenced by results on the ZJU-MoCap dataset and our custom dataset. This work not only advances the capabilities of NeRF in human reconstruction but also opens new avenues for realistic virtual human animation. © 2025 SPIE.

关键词： Motion capture

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A Positioning Optimization System for Intelligent Pharmaceutical Production Line Based on Visual Imaging

A Positioning Optimization System for Intelligent Pharmaceut...

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2023 international conference on Power, Communication, Computing and Networking Technologies, PCCNT 2023

作者： Hu, Yingkun Zeng, Xiufang Chen, Pinzhong School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce 963 Guangzhou Avenue North Tianhe District Guangzhou510510 China School of Electronics and Information Engineering South China Normal University Foshan528225 China

ISBN: (纸本)9781450399951

On the pharmaceutical automation production line, the application of artificial intelligence technologies such as machine vision can make pharmaceutical production automated and intelligent, improve the quality and efficiency of pharmaceutical production. It can be said that machine vision is a major core technology for the automation and intelligence of pharmaceutical production equipment. This article focuses on the visual positioning technology for the filling and sealing process of traditional Chinese medicine in pharmaceutical automation production lines. The research content mainly includes two aspects: The design of visual imaging systems and the research on visual positioning algorithms for bottle mouths, stoppers, and caps. The visual imaging system uses an industrial camera combined with a circular light source, and then uses a hand eye calibration installation method to capture high-quality images of bottle mouth, cork, and cap on a movable robotic arm. Next, a series of image preprocessing operations are carried out on the original image, including Gaussian filtering and smoothing of the image to remove noise, gamma transform image enhancement and edge detection preprocessing operations, and the use of Canny edge detection to obtain images with good foreground and background contrast, appropriate brightness, and prominent edges. Finally, a circle fitting algorithm based on KMeans++ was used to locate the extracted edge images of bottle mouth, bottle stopper, and bottle cap, and the experimental results were preliminarily analyzed. © 2023 ACM.

关键词： computer vision

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Learning Quality Analysis System for the Second Degree Education Based on K-means Algorithm 6

Learning Quality Analysis System for the Second Degree Educa...

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6th IEEE Information Technology, Networking, Electronic and automation Control conference, ITNEC 2023

作者： Li, Junxian Feng, Xue Jiang, Dengbiao Zhu, Feng Jiangsu University of Science and Technology College of Computer Science China

ISBN: (纸本)9781665460033

Based on K-means algorithm and elbow method, this paper constructs a second-degree learning quality analysis system, and uses the data of students with a second-degree in Software engineering to analyze and explore the potential relationship between course nature, gender, major, major college, and usual performance and achievement. Results show that the course of provincial unified examination, boy, major with a large gap with the second degree major, and poor usual performance are easy to lead to poor learning effect, and corresponding suggestions are put forward for the enrollment, management and teaching of the second degree, which can be used as a reference for other majors and sister colleges. © 2023 IEEE.

关键词： K-means clustering

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Optimizing Railway Signaling and Platform Management with LoRaWAN, RFID and automation Technologies 4

Optimizing Railway Signaling and Platform Management with Lo...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sarvade, Samarth Shirwal, Vijaykumar Kugaonkar, Parth Mudgonda, Kartik Department of Electronics & Telecommunication Nagesh Karajagi Orchid College of Engineering and Technology Solapur India

The integration of advanced communication and automation technologies provides a transformative approach to enhancing railway signaling and platform management. This system utilizes real-time data exchange to optimize train positioning, platform allocation, and overall network operations. Leveraging long-range, low-power communication protocols such as LoRaWAN, the solution enables seamless and reliable coordination between trains and stations, reducing delays, enhancing safety, and improving the precision of platform management. Additionally, RFID technology is employed to enhance accuracy in monitoring train movements and platform occupancy, ensuring smoother and more efficient operations. The system architecture facilitates more effective resource management by automating key processes such as train scheduling and platform allocation, ultimately minimizing energy consumption and lowering operational costs. With improved communication accuracy, faster response times, and robust system reliability, the proposed solution addresses critical challenges faced by modern railway systems. This approach is designed to be scalable and adaptable to the evolving demands of large railway networks, including Indian Railways, providing a future-proof strategy for optimizing performance and sustainability. By modernizing traditional operations and improving efficiency, the system offers a comprehensive upgrade for both freight and passenger rail services, ensuring long-term operational resilience. © 2025 The Authors. Published by Elsevier B.V.

关键词： Railroads

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Intelligent Door Controller Using Deep Learning-Based Network Pruned Face Recognition 25

Intelligent Door Controller Using Deep Learning-Based Networ...

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25th international conference on computer and Information Technology, ICCIT 2022

作者： Das, Prangon Asif, Nurul Amin Hasan, Md. Mehedi Abhi, Sarafat Hussain Jahin Tatha, Mehtar Bristi, Swarnali Deb Rajshahi University of Engineering & Technology Department of Mechatronics Engineering Bangladesh

ISBN: (纸本)9798350346022

Nowadays, our home is designed with various technologies which have increased our living comfort and offering more flexibility. Installing various technology in our Home makes it a smart home and we also call this installation process Home automation. The popularity of Home automation systems is increasing rapidly and it develops the quality of living. Home automation offers automatic light, fan, temperature, etc. control and also an automatic alarming system to alert the people, etc. Already there are various techniques have been used for implementing Home automation. Here, in this paper, an intelligent door controller, an application of home automation is presented by using deep learning techniques. An intelligent door basically opens automatically and closes after a predefined time based on the person coming in front of the door. If a person is known then the door will be opened and after his/her entrance the door will be closed automatically. And if the person is not known then the door will remain closed. Here to identify the person, the person's face is recognized by using deep learning. As well ass, Arduino and Servo motors are used to control the door opening or closing. © 2022 IEEE.

关键词： Face recognition

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Improving Sign Language Recognition for Deaf and Hard of Hearing Individuals using the VGG16 Model 3

Improving Sign Language Recognition for Deaf and Hard of Hea...

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3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Benjamin, Minu Inba Shanthini Watson Patan, Yaseen Kumar, Ansilin S.M. Reddy, Pocharam Nithyanandh Ahamad, Syed Faheez Puneeth, Muvvala Sai Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331532420

Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greater research to offer appropriate assistance. Interaction and communication with others are a big challenge. Since sign language is the major means of communication for these people, there is a significant knowledge gap among hearing individuals when it comes to understand and interpret sign language. As a result, there has been a notable expansion in the field of sign language study to close this gap. This work highlights the importance of sign language detection and offers a thorough analysis of relevant studies carried out in this domain using deep learning technique. It approaches the issue from multiple perspective, including translation, sign language recognition, and access to relevant datasets. Understanding these facets is intended to create a meaningful impact on sign language literature and its practical implementation. VGG16 deep learning model for sign language recognition offers the best solution for solving communication challenges faced by deaf and muted individuals. The results show that the VGG16 based technique achieves accuracy of around 95.8%. The VGG16 model uses a Convolutional Neural Network (CNN) architecture, which applies many layers of convolutional filters to identify patterns and attributes in the input images. Recognizing sign language using VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Commonsense Reasoning with Entity Type Knowledge 5

Enhancing Commonsense Reasoning with Entity Type Knowledge

引用

5th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2024

作者： Guo, Yanjun Yue, Dengfeng Mao, Chenyang Leng, Tuo Shanghai University School of Computer Engineering and Science Shanghai China

ISBN: (纸本)9798350366174

Commonsense reasoning has long been a significant challenge in the field of natural language reasoning, requiring machines to possess ample commonsense knowledge for factual inference. In recent years, large-scale pre-trained models have been extensively employed in commonsense reasoning and question-answering tasks, demonstrating commendable comprehension capabilities. However, compared to traditional approaches that rely on conceptual knowledge graphs, these models not only lack commonsense knowledge resources but also lack dedicated modules for structured knowledge. The trend of integrating these two methodologies for inference is growing. Nevertheless, the overlap between the concepts of entities in knowledge graphs and the implicit knowledge embedded within pre-trained models often impedes the enhancement of commonsense reasoning capabilities. We observe that there is symmetry between the types of entities in questions and answers, which offers potential for further exploring the commonsense reasoning abilities of pre-trained models. Therefore, we incorporate entity types as external supplementary knowledge into the pre-training model. To better align with generative pre-training models, we employ syntactic tools to transform commonsense questions into structured declarative sentences, which serve as inputs to the model. We then resolve commonsense questions based on the scores generated by the model. Our research results show that, compared to baseline pre-trained models, our approach significantly improves inference accuracy. Specifically, on the CommonsenseQA dataset, accuracy increased from 72.9% to 81.1%, and on the OpenBookQA dataset, it rose from 79.3% to 82.8%. These improvements highlight the superiority of our method over baseline models and demonstrate its potential to enhance the commonsense reasoning capabilities of existing models. © 2024 IEEE.

关键词： Knowledge graph

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Hierarchical Reinforcement Learning-based Mapless Navigation with Predictive Exploration Worthiness 20

Hierarchical Reinforcement Learning-based Mapless Navigation...

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20th IEEE international conference on Mechatronics and automation, ICMA 2023

作者： Gao, Yan Ji, Ze Wu, Jing Wei, Changyun Grech, Raphael Cardiff University School of Engineering Cardiff United Kingdom Cardiff University School of Computer Science and Informatics Cardiff United Kingdom Hohai University College of Mechanical and Electrical Engineering Changzhou China Spirent Communications Paignton United Kingdom

ISBN: (纸本)9798350320831

Hierarchical reinforcement learning (HRL) is a promising approach for complex mapless navigation tasks by decomposing the task into a hierarchy of subtasks. However, selecting appropriate subgoals is challenging. Existing methods predominantly rely on sensory inputs, which may contain inadequate information or excessive redundancy. Inspired by the cognitive processes underpinning human navigation, our aim is to enable the robot to leverage both 'intrinsic and extrinsic factors' to make informed decisions regarding subgoal selection. In this work, we propose a novel HRL-based mapless navigation framework. Specifically, we introduce a predictive module, named Predictive Exploration Worthiness (PEW), into the high-level (HL) decision-making policy. The hypothesis is that the worthiness of an area for further exploration is related to obstacle spatial distribution, such as the area of free space and the distribution of obstacles. The PEW is introduced as a compact representation for obstacle spatial distribution. Additionally, to incorporate 'intrinsic factors' in the subgoal selection process, a penalty element is introduced in the HL reward function, allowing the robot to take into account the capabilities of the low-level policy when selecting subgoals. Our method exhibits significant improvements in success rate when tested in unseen environments. © 2023 IEEE.

关键词： Reinforcement learning

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Reinforcement Learning for Multi-Robot Coordination and Cooperation in Manufacturing 10

Reinforcement Learning for Multi-Robot Coordination and Coop...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Konduri, Srinivas Walke, Suchita Kumar, Anjanee Pavithra, G. Bhagirath Jadhav, Aniket Natrayan, L. Bits Pilani Telangana Hyderabad India Pillai College of Engineering and Technology Mumbai University Department of Computer Engineering India Dadi Institute of Engineering and Technology Anakapalle India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India SMT. KashibaiNavale College of Engineering Electronics and Communication Engineering Department of Mechanical Engineering Maharashtra India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350382471

The introduction of robotic technologies has dramatically altered the automation of production operations. The full benefits of these systems, including increased production and efficiency, can be attained only via the coordinated efforts of several robots. Higher throughput, reduced production costs, and better overall performance can only be achieved through multi-robot coordination and collaboration in industrial environments. One potential method that aims to meet these obstacles is known as Reinforcement Learning (RL). To this end, we provide a Hierarchical Reinforcement Learning (HRL) system for improving collaboration amongst several robots during production. To do this, the HRL framework that we propose makes use of a hierarchical structure to break down the issue into many levels of abstraction, each of which is accountable for a distinct part of the robotic job. A meta-controller is a higher-level controller that learns how to divide up work across robots to increase output. The meta-controller makes choices depending on data it collects about the production facility and the robots themselves. The meta-controller provides a framework within which the lower-level robot controllers can function. Actions, such as motion, manipulation, and cooperation with other robots, are determined by these controllers using RL algorithms. Adapting to the ever-changing conditions of their surroundings, the lower-level controllers are in charge of fine-grained control. The capacity for learning from experience is an asset to our approach. The meta-controller, along with the lower-level controllers, improves its decision-making and coordinating capabilities through its interactions with the manufacturing environment. This flexibility is fundamental for dealing with the ambiguity and complexity of actual production processes. Additionally, our HRL paradigm recognises the value of human-robot interaction and dialogue. Robust communication protocols are built so that robots may

关键词： Cooperation etc Hierarchical Reinforcement Learning Manufacturing Multi-Robot Coordination Reinforcement Learning

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