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检索条件"任意字段=International Conference on Computer Distributed Control and Intelligent Environmental Monitoring"

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Key Technologies for environmental Perception in Fully Automated Driving of High Speed Railways 6

Key Technologies for Environmental Perception in Fully Autom...

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6th international conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Guo, Pengyue Ma, Zhen Wang, Rui Sun, Tingting Dept. China Academy of Railway Sciences Group Co. Ltd Institute of Electronic Computing Technology Beijing China

ISBN: (纸本)9798331541750

The fully automated driving of high-speed trains is an important part of intelligent railways. Currently, the technology of train autonomous driving is based on the train operation control system, which lacks the perception function of the train operating environment. This leads to safety hazards during the operation of the train. The international Electrotechnical Association stipulates that under the automation level of the train operation control system GOA3 and GOA4, the auto drive system shall replace the driver. It is very important to achieve timely and accurate monitoring of the environment ahead of the train under different routes and weather conditions. Perceiving the operating environment ahead of the train in a complex background is a necessary prerequisite for achieving automatic driving of the train. Therefore, having a system that can continuously detect the environment ahead of the train in real time is a practical requirement to ensure driving safety. The high-speed railway operates at a fast speed, and trains require longer braking distances for emergency situations and changes in the surrounding environment. Therefore, sensors are installed on the roadside to achieve reliable perception of the operating environment. This article mainly studies the key technology of ground environment perception for meteorological environment perception. We conducted an alarm on-board test to verify the usability of the system. © 2024 IEEE.

关键词： Railroads

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IoT-Driven Genomic Ecology: Exploring Genetic Adaptation in Changing Environments using CNN Model 3

IoT-Driven Genomic Ecology: Exploring Genetic Adaptation in ...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ranganathan, Chitra Sabapathy Riazulhameed, Arshadh Ariff Mohamed Abuthahir Sahoo, Santosh Kumar Sumathi, V. Sudhakar, G. Rathinam, Anantha Raman Mphasis Corporation Tamil Nadu Chennai India Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Vit University Department of Engineering and Technology Madhya Pradesh Bhopal India Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada Vaddeswaram India

ISBN: (纸本)9798350369083

The Internet of Things (IoT) technology and Convolutional Neural Networks (CNNs) catalyzes this investigation, which dives into the complex relationship between genomes and the surrounding environment. It monitors various environmental factors in real-time using a distributed network of Internet of Things sensors installed across various natural and urban locations. While doing this, this system collects genomic data from creatures that live in or interact with these habitats. This allows us to explore the intriguing dynamics of genetic adaptation in response to environmental changes. The research sheds light on the genetic factors responsible for an organism's capacity to flourish in constantly changing environments. The system can shed light on patterns of genetic mutations, disease prevalence, and variations in biodiversity by integrating environmental data collected from the Internet of Things with genomic information. These realizations contribute to our comprehension of the mechanisms that drive evolution;help create conservation plans for species at risk of extinction, inform disease epidemiology, and assist sustainable farming practices. The article "IoT-Driven Genomic Ecology"demonstrates a multidisciplinary approach to converging genomics, environmental science, and IoT technology. Its purpose is to shed light on the underlying principles that control life's adaptability to a world that is always changing. © 2024 IEEE.

关键词： environmental monitoring

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(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Quality control 19

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Qual...

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19th Annual international conference on distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Kalioras, Fotios Filios, Gabriel Karatzas, Stylianos Nikoletseas, Sotiris University of Patras Department of Computer Engineering and Informatics Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering and Informatics Greece University of Patras Department of Civil Engineering Greece

ISBN: (纸本)9798350346497

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution. © 2023 IEEE.

关键词： Crowdsourcing

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Raspberry Pi based intelligent greenhouse Internet of Things platform 5

Raspberry Pi based intelligent greenhouse Internet of Things...

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5th international conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Chen, Shanhu Li, Danchun Liu, Junneng Wei, Runkai Guangzhou Xinhua University Dongguan523133 China

ISBN: (纸本)9798400716485

With the development of smart agriculture, realizing accurate monitoring and management of greenhouse agriculture environment becomes the key to improve the efficiency and quality of agricultural production. This study aims to develop a Raspberry Pi based intelligent greenhouse IoT platform to enhance the automation and intelligence of greenhouse agriculture. This platform utilizes Raspberry Pi as the core controller, integrates multiple environmental sensors for real-time data acquisition, such as temperature and humidity, light intensity, etc., and optimizes the accuracy of the data through Kalman filtering algorithm. The Raspberry Pi also assumes the function of an IoT gateway to securely and efficiently transmit data to the cloud for remote storage and processing. In addition, this study also developed the supporting mobile APP and computer client software to provide users with an intuitive interface for data monitoring and remote control. In terms of energy management, by applying the BBPSO algorithm to optimize the angle of solar panels, this platform effectively improves the energy utilization efficiency and guarantees the sustainable operation of the system. The results of this study not only improve the management efficiency and crop production quality of greenhouse agriculture, but also provide a useful reference for the future technological development of smart agriculture. © 2023 ACM.

关键词： Internet of things

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Research Progress of Aquaculture environmental Factor Prediction Model Based on Machine Learning 9

Research Progress of Aquaculture Environmental Factor Predic...

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9th international conference on control Science and Systems Engineering, ICCSSE 2023

作者： Qin, Haijing Tian, Yunchen Wang, Hongpo Li, Qingfei Cong, Xueqi Sui, Jinzhu College of Computer and Information Engineering Tianjin Agricultural University Tianjin China

ISBN: (纸本)9798350339055

The accurate aquaculture environmental factors prediction model is established using big data technology and machine learning algorithm. It has become a research hotspot because it can accurately predict water quality, strengthen water quality management and reduce aquaculture risks. This paper introduces the characteristics and innovations of aquaculture water quality prediction models based on shallow machine learning and deep learning. It expounds on the primary process and standard methods of aquaculture environment factor prediction models based on machine learning. Analyzing the model performance index, the integrity of data collection, scientific processing, and the rationality of the model-building method is the basis for establishing a high-quality model. At the same time, it also points out the existing problems of empirical determination model structure and the need to accelerate the collaborative development of water environment monitoring technology and water environment prediction technology in the future to establish an intelligent water environment regulation system and promote the development of intelligent aquaculture. © 2023 IEEE.

关键词： Water quality

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Design of intelligent light control system based on NB-IoT

Design of intelligent light control system based on NB-IoT

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2022 international conference on Wearables, Sports and Lifestyle Management, WSLM 2022

作者： Zeng, Xiaoling Zhang, Jianping Chongqing Electronic Information College Chongqing China

ISBN: (纸本)9781665408196

At this stage, the level of urbanization in China is constantly improving, and the construction of 'smart city' is gradually included in the ranks of national urban planning. As an important part of smart cities, intelligent street light are welcoming unprecedented development opportunities. The traditional street light control system generally adopts the methods of centralized line control, human inspection, and computer-side web monitoring. Aim at the lag of traditional street lamp management, and lack of real-time monitoring and remote control, this thesis designs a set of intelligent street light monitoring system based on NB-IoT (Narrow Band-Internet of Things). The system realizes the real-time control of street lighting system, environmental monitoring, spray cooling and dust suppression, etc. Cloud technology is used to calibrate the geographic location information and working status information of each street lamp. The test results show that the system is stable in operation and reliable in data transmission, reducing the cost and energy consumption of the traditional street lamp control nodes, and realizing the remote monitoring and intelligent management of the status of urban street lamps by the terminal equipment. © 2022 IEEE.

关键词： Internet of things

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Smart Sewage System with Advanced Remote monitoring for Efficient Urban Infrastructure Management 2

Smart Sewage System with Advanced Remote Monitoring for Effi...

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2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Madhavi, S. Ashmita, R.S. Dakshatha, E.S. Dhinakar, T. K.S.Rangasamy College of Technology Tiruchengode Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

In urban areas across the state, inadequate Sewage systems have posed significant challenges, especially in densely populated regions. This work presents the development of a Smart Sewage Skeletal System aimed at addressing these challenges. The system implements remote monitoring capabilities to transform traditional sewage infrastructure into an intelligent and responsive network. The work aims in minimizing human interaction in sewage management, harnessing the potential of IoT, cloud computing, and big data, and improving public health, sanitation, and environmental protection. The system employs various sensors, including overflow and level sensors using ultrasonic waves, flow sensors, and gas sensors, to detect sewage issues such as blockages, overflows, and gas anomalies with high accuracy. The Arduino microcontroller processes the sensor data and controls valves and water pump motors to take prompt corrective actions. Furthermore, the system offers remote monitoring and control through a user-friendly graphical interface, accessible by municipalities and municipal corporations, enabling real-time surveillance and management of the sewage network. This infrastructure setup reduces the risk of urban flooding, prevents sewage spillage onto streets, protects the environment, optimizes resource management, and enables swift emergency response. This scheme provides solution that enhances the efficiency and maintenance of urban sewage infrastructure, ultimately contributing to cleaner, healthier, and more resilient cities. © 2024 IEEE.

关键词： Sewage

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Research on Artificial Intelligence (AI) Recognition Automated Soft-Shell Crab Breeding System under 'Internet +' Technology 3

Research on Artificial Intelligence (AI) Recognition Automat...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Zheng, Lei Gong, Mingmin Li, Zhenyang Yang, Mingming School of Business Shenzhen Institute of Technology Shenzhen China School of Information Engineering Wuhan College Wuhan China School of Information and Communication Shenzhen Institute of Technology Shenzhen China

ISBN: (纸本)9798350380989

This project aims to use artificial intelligence technology to raise crabs in land equipment, so as to easily obtain soft-shell crabs, to increase the supply of new aquatic products in the market, to meet the needs of market consumption upgrading, and finally to achieve the goal of economic benefits. This project is based on artificial intelligence to identify automatic soft-shell crab cultivation technology, combined with scientific breeding management methods, the general crab to soft-shell crab cultivation. The equipment uses the Internet of Things, big data, cloud computing, computer vision and other technologies to design five systems, including soft shell crab mounting identification system, water quality monitoring system, three-dimensional breeding system, automatic feeding system, integrated control system, to realize the local and remote all-round intelligent monitoring of soft-shell crab breeding base. In this paper, STC15F2K60S2 as the core of the design of MCU based hardware platform is presented. RS485 communication mode is used to collect data of each sensor in real time, and multiple environmental parameters can be monitored in real time. Using video surveillance technology, the pond can be secured, and the pond can be photographed on the surface and underwater. The feeding device is controlled by PLC and the feeding machine and aerator are controlled intelligential. The network uses ESP8266 wireless network to realize network connection to AP. Through the establishment of the 'server' management software, we enable users to remote query through the computer browser, mobile APP and other ways in the case of network. © 2024 IEEE.

关键词： Automatic recognition monitoring system sensor soft shell crab breeding system water quality monitoring

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An intelligent Solid Waste Classification and monitoring Alert System using Deep Learning

An Intelligent Solid Waste Classification and Monitoring Ale...

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2024 international conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Selvi, S. Elamathy, G.O. Kirana, B. Swetha, S. Government College of Engineering Department of Computer Science and Engineering Bargur India

ISBN: (纸本)9798331541170

Efficient waste control is critical for India's smart cities. This study uses Convolutional Neural network (CNN) technology to classify and reveal waste, integrating sensors, Internet of Things (IoT) gadgets, and system studying to promote sustainability. Also targets to deal with inefficient waste management by using CNN technology for effective garbage classification and tracking. The device integrates sensors, IoT devices, and machine learning algorithms to sort and track waste, promoting sustainable practices and environmental conservation. The proposed system uses an ArduinoMKR1000 microcontroller, ultrasonic sensors, and jumper wires to monitor garbage bin levels and classify waste into biodegradable and non-biodegradable classes the usage of CNN algorithms. The system employs the DenseNet121 model for image type and makes use of ThingSpeak for real-time tracking and notifications. The experimental consequences display an accuracy of 95% for the DenseNet121 version, outperforming traditional CNN and ResNet-50 architectures. The proposed device's overall performance surpasses that of different models because of its dense connectivity and efficient feature map creation. Real-time monitoring and data analytics provide precious insights for waste reduction and sustainability tasks, contributing to cleaner, greener, and more sustainable environments for future generations. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-Time Defect Detection monitoring System For Extrusion-based 3D Food Printing Using Internet of Things 3

Real-Time Defect Detection Monitoring System For Extrusion-b...

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3rd international conference on Smart Cities, Automation and intelligent Computing Systems, ICON-SONICS 2023

作者： Mawardi, Cholid Buono, Agus Priandana, Karlisa Herianto Institut Pertanian Bogor Department of Computer Science Bogor Indonesia Universitas Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia

ISBN: (纸本)9781509062805

Quality control remains a significant challenge in 3D food printing, in which defects during the printing process will lead to resource and time wastage. This work proposes a novel approach integrating cameras, image processing, and supervised machine learning to autonomously assess the quality of 3D food printed items. At crucial stages of the printing process, images of partially manufactured parts are captured and analyzed based on its geometry. Utilizing smartphones and the Internet of Things for efficient monitoring, the designed system aims to minimize material wastage, a prevalent concern in the creative food industry. The monitoring tool employs a 3D printer embedded with Raspberry Pi and a primary camera, with image processing carried out by the Obico plugin on Raspberry Pi to issue commands for real-time performance monitoring. The implementation of this technology not only addresses early defect detection but also enhancing food design and minimizing environmental impact, presenting promising prospects for the future of the food industry. © 2023 IEEE.

关键词： Internet of things

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