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

Key Technologies for Environmental Perception in Fully Autom...

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Frontiers Technology of Information and computer (ICFTIC), IEEE international conference on

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

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

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.

关键词： Rails Disasters Prediction methods control systems Rail transportation Real-time systems Sensors Reliability Usability Vehicles

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Power Research on intelligent controller of Exposed Tea Fixation Machine Based on STM32 2

Power Research on Intelligent Controller of Exposed Tea Fixa...

引用

2020 2nd international conference on Energy, Power, Environment and computer Application, ICEPECA 2020

作者： Han, Qinghua Shi, Mingdeng Li, Xiao College of Information Engineering Tarim University Alar843300 China Key Laboratory of Modern Agricultural Engineering Tarim University Alar China

During the processing of the traditional fixation machine, the temperature is difficult to control, which affects the quality of tea frying. In order to solve this problem, Taking STM32 MCU as the control chip to complete the intelligent control of each functional module. All units are uniformly controlled by a microprocessor, and at the same time, intelligent communication equipment can be used to realize remote monitoring, view real-time production status, and control progress. In addition, the original tea farmer's machinery can be modified on-site to promote production, and the cost of the installation is lower, which is of great significance to the revival of the tea-horse culture on the "New Silk Road". © Published under licence by IOP Publishing Ltd.

关键词： Remote control

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Research on Fault Location of distributed Low-Voltage Distribution Network Based on Internet of Things 4

Research on Fault Location of Distributed Low-Voltage Distri...

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4th international conference on computer Network, Electronic and Automation, ICCNEA 2021

作者： Li, Songnong Zeng, Yan State Grid Chongqing Electric Power Research Institute No. 80 Middle Section of Huangshan Avenue Yubei DistrictChongqing China Chongqing Key Laboratory Of Energy Internet Advanced Measurement And Energy Big Data No. 80 Middle Section of Huangshan Avenue Yubei DistrictChongqing China

ISBN: (纸本)9781665444866

When the low-voltage distribution network breaks down, it needs to respond quickly, and the method of finding fault by manual line inspection is time-consuming and laborious. In order to solve the problem of lack of effective fault location method in low-voltage network, a fault location method based on distributed nodes is proposed. The method is based on the chaotic reflection method, set up multiple test points in the low voltage power grid, the real-time monitoring of power grids, power grid failure, contrast analysis of test results, the test point each test point is determined according to the principle of the shortest path with the shortest distance of fault point, set up by the recent test point measurements of potential fault branch collection, to determine the fault subnetwork, Then, multiple information sources in the fault subnetwork are fused to determine the specific fault branch, and the specific fault location is determined by the nearest test point. Finally, the fault detection and alarm system of the low-voltage distribution network is designed based on the power Internet of Things. Key technologies such as IED, LOLA wireless AD organization network and intelligent gateway are used to realize the collection, communication and intelligent research and judgment of global line information, and realtime monitoring and alarm reception of the mobile terminal are realized through the cloud platform. © 2021 IEEE.

关键词： Internet of things

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Research on intelligent monitoring System for Automatic Sprinkling of intelligent Power control Park

Research on Intelligent Monitoring System for Automatic Spri...

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2021 international conference on Advances in Optics and Computational Sciences, ICAOCS 2021

作者： Meng, Bin Ji, Xinyang Hu, Gaoshan Shenyang Urban Construction University Shenyang110167 China

Based on the analysis of the traditional automatic sprinkler maintenance methods in parks, the paper proposes the use of photovoltaic power supply and remote controllable automatic sprinkler methods. On the basis of using solar power as the power source, batteries are used to store electrical energy. The paper also adopted distributed unit design in the design, which solved the layout problems of various parks with complex terrain. In the design, environmental and engineering factors are fully considered to make the design simple, reliable, and easy to construct. At the same time, the unit monomer was tested and verified, and the results showed that the process is simple, the data is reliable, and the production efficiency is high. © Published under licence by IOP Publishing Ltd.

关键词： Parks

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Maximizing Wheat Crop Productivity with intelligent Land Analysis and IoT

Maximizing Wheat Crop Productivity with Intelligent Land Ana...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), international conference on

作者： C. Bala Kamatchi A. Muthukumaravel Department of Computer Science Application Bharath Institute of Higher Education and Research Chennai Tamilnadu India Faculty of Arts and Science Bharath Institute of Higher Education and Research Chennai Tamilnadu India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Agricultural productivity plays an essential role in global food security, with a special focus on increasing the production of important crops such as wheat. To meet the requirement, the study proposes a novel technique that combines an intelligent land analysis framework with IoT (Internet of Things) technology. The growing global population and the limitations of traditional farming methods in meeting expanding demand create a pressing requirement for such a model. Existing methods tend to be limited by a lack of real-time data, resulting in inefficient resource usage and yield losses. The proposed method, on the other hand, merges soil sensors, drones, and satellite images to provide farmers with real-time information on soil moisture, levels of nutrients, and the health of crops. It employs data analytics and machine learning algorithms to provide personalized irrigation, fertilization, and pest control recommendations, allowing for accurate and timely interventions. Field experiments reveal a 20% increase in wheat yield, 15% reduction in water use and 18% reduction in fertilizer usage, demonstrating the effectiveness of the method compared with the existing approach in increasing productivity while saving resources and avoiding environmental impacts.

关键词： Productivity Image sensors Data analysis Machine learning algorithms Crops Food security Real-time systems Internet of Things monitoring Farming

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IoT based intelligent Information-Analytical System Architecture for Water Tank monitoring

IoT based Intelligent Information-Analytical System Architec...

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2021 international conference on Information Technology, ICIT 2021

作者： Zhukovskyy, Viktor Printz, Damon Zhukovska, Nataliia Hubach, Maksym Rajab, Hesham The National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine Meadowlark Solutions LLC AlbinWyoming United States

ISBN: (纸本)9781665428705

This paper proposes an approach for the design of the web-based information-analytical system. The system is expanding the capabilities of the existing water-monitoring business with an improved web-based information system. IoT devices may send the data through several possible channels: satellite, cellular or internet network. Device profiles contain all necessary functions to control an individual IoT device, handle incoming data and trigger different actions. The information-analytical system analyses incoming data and sends information on a daily basis via text short messages to the clients. Client at the same time may control their devices remotely via text message commands. The corresponding architecture was used in a real production environment and demonstrated its consistency. © 2021 IEEE.

关键词： Water tanks

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Vehicle Detection and Classification Using Deep Neural Networks

Vehicle Detection and Classification Using Deep Neural Netwo...

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2022 international conference on Electrical and Information Technology, IEIT 2022

作者： Chowdhury, Shuva Chowdhury, Shithi Ifty, Jeba Tahsin Khan, Riasat Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Urbanized large cities encounter significant challenges of manual traffic control due to overpopulation, traffic congestion and land shortage. These cities have initiated implementing intelligent transportation systems by employing automatic and efficient traffic monitoring and management. Due to bad conditions in traffic, many minor incidents occur on streets, e.g., avoiding traffic rules, over-taking tendency, and high-speed and intoxicated driving, overdrive limit, unsafe lane changes on the road, etc. Sometimes these incidences provoke collisions, accidents, crimes, loss of time and human fatalities. For this reason, automatic vehicle detection and classification are essential to predict traffic congestion levels and lane control and enhance road safety and security. This automated process can solve economic, social, and environmental concerns and impact everyday living issues. In this work, automatic vehicle detection and classification have been introduced using various deep neural network frameworks, i.e., VGG16, VGG19 and YOLOv5. We used transfer learning algorithms built on a conventional neural network for native vehicle identification and classification. This research used a Bangladeshi vehicle images dataset containing 9,058 images. Next, we employed the 'ImageDataGenerator' Keras data preprocessing and augmentation framework and various deep learning networks, VGG16, VGG19, and YOLOv5. Finally, the proposed VGG16 and VGG19 models' accuracies for ten classes are 75.15% and 79.57%, respectively. The YOLOv5 architecture attained the best performance with 83.02% accuracy for fifteen vehicle classes. © 2022 IEEE.

关键词： Traffic congestion

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Prediction of environmental Parameters of Yungang Grottoes Based on BO-CNN-LSTM Artificial Neural Network 8th

Prediction of Environmental Parameters of Yungang Grottoes B...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Gao, Lunrui Yan, Hongbin Liu, Tingzhang Zhang, Shaoyou Xu, Xinrui Department of Automation College of Mechatronics Engineering and Automation Shanghai University Shanghai China Cultural Heritage Protection and Monitoring Center Yungang Research Institute Shanxi China

ISBN: (纸本)9789819602933

This paper introduces a method for predicting internal environmental parameters of the Yungang Grottoes using a hybrid approach that combines Long Short-Term Memory (LSTM) neural networks with Convolutional Neural Network (CNN) technology. To enhance the predictive accuracy, the Bayesian Optimization algorithm was employed to optimize the most relevant hyperparameters of the proposed model. The study utilized a database obtained from external weather stations and internal cave sensors as a sample set to train the model. Initially, a preprocessing algorithm was applied to improve data quality and prediction accuracy. Subsequently, the developed model was utilized to forecast the environmental parameters inside the cave under external conditions. The obtained results were compared with other model types: LSTM neural networks and CNN-LSTM hybrid neural networks. The findings indicate that the proposed model surpasses the comparative models in terms of prediction accuracy and quality across all analyzed parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Cloud and IoT based Framework for Air and Noise Pollution monitoring System via Extreme Learning Machine

Cloud and IoT based Framework for Air and Noise Pollution Mo...

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international conference on ent Computing and control Systems (ICICCS)

作者： V S Narayana Tinnaluri Latha Sahukar Ankur Chaudhari G. Malathy Subbarao Kolavennu L Leonard Department of Computer Science Koneru Lakshmaiah Education Foundation Green fields Vaddeswaram Guntur Andhra Pradesh India ECE Department Sreenidhi Institute of Science and Technology Yamnampet Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science & Engineering Paavai Engineering College Pachal Namakkal Tamilnadu India CSE Cyber Security Department Sphoorthy Engineering College Hyderabad Telangana India Department of analytics ISBR Business School Bangalore Karnataka India

Nowadays, air and noise pollution pose the greatest threat to the environment and to public health. Air and noise pollution are rising rapidly. In order to get it under control, close observation of it is strongly advised. This research addresses this problem by unveiling a method of detecting both ambient noise and the presence of potentially dangerous chemicals. Pollution is increasing at an alarming rate, and it’s starting to cause problems for living things. Pollutants like loud noises and hazardous gases have a negative impact on people’s health and want special attention. In this research, the proposed approach suggests the development of a network of sensors to monitor the environmental noise and air quality. A NodeMCU equipped with an ESP8266 WLAN adaptor is part of an Internet of Things (IoT) based Air and noise pollution monitoring system, together with a DHT11, a MQ9 gas sensor, and a Lm393 sound sensor. This monitoring system was developed with the help of neural network–based algorithms like CNN, SVM, and ELM to accurately forecast the level of pollution in a given area. Root-Mean-Square-Error (RMSE), Mean Square Error (MSE), and Mean-Absolute-Error (MAE) were used as performance indicators. Based on the performance, the Extreme Learning Machine (ELM) based training algorithm for single hidden layer feed forward neural networks (SLFNs) has revolutionized SLFN development. The results showed ELM as the most effective solution. The proposed model can now be used in high-pollution and noisy locations.

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A Review on the Use of Smart Agriculture Systems to Protect Crops

A Review on the Use of Smart Agriculture Systems to Protect ...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： Gulshan Kumar Palash Gourshettiwar Saurabh Suman Shravan Kumar Dhiraj Kumar Rahul Raushan Department of Computer Science and Design Faculty of Engineering and Technology Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture Technological innovation Space technology Crops Production Soil Communications technology Internet of Things Remote sensing Farming

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