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

共 1799 条记录，以下是1-10 订阅

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Proceedings - international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2011

Proceedings - International Conference on Computer Distribut...

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2011 international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2011

ISBN: (纸本)9780769543505

The proceedings contain 574 papers. The topics discussed include: a data acquisition system for methane drainage based on ARM and FPGA;a multi-sensor fusion method of agricultural remote sensing;a new composite switch for low voltage equipment control;a new measurement method for cavity diameter of porous concrete;a RS/GIS-based system for monitoring crop yield;design of integrated automation system of energy-fed supply substation;design of the node system of wireless sensor network and its application in digital agriculture;detect black germ in wheat using machine vision;detection of nonylphenol by exonuclease protection fluorescent quantitative PCR;development of a monitoring and control platform for honeycomb ceramics resistance test;distribution of lead in blood and organs of mice exposed to lead-contaminated drinking water;and distribution of pressure index of agricultural water resources and strategies in China based on GIS.

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Proceedings - 2012 international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2012

Proceedings - 2012 International Conference on Computer Dist...

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2012 international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2012

ISBN: (纸本)9780769546391

The proceedings contain 201 papers. The topics discussed include: a novel cost-effective portable electronic nose for indoor-/in-car air quality monitoring;a clustering routing algorithm based on energy and distance in WSN;a fusion method of heterogeneous information for sea-surface wireless sensor networks positioning;a hybrid strategy to construct scientific instrument ontology from relational database model;a method of buried pipeline route detection based on the geomagnetic field;a method of video quality assessment for CMMB video test sequences;a new image registration method based on frame and gray information;a new method of class centriod vectors classification based on the feedback;a new somewhat homomorphic encryption scheme over integers;a simple and effective algorithm for sinusoidal frequency estimation;and accumulation of soluble organic nitrogen under intensive vegetable production pattern in northeast China.

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Research on an intelligent Atmospheric Environment monitoring System Based on Modular Structure 24

Research on an Intelligent Atmospheric Environment Monitorin...

引用

2nd international conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Shi, Ting Wang, Peihao Qiao, Junfei Ao, Yunpeng Wang, Kai Zhang, Yuling Beijing Laboratory for Intelligent Environmental Protection Beijing University of Technology Beijing China

ISBN: (纸本)9798400710681

The quality of the atmospheric environment is closely related to human health and has received widespread attention in recent years. Atmospheric environment monitors are used in both production and daily life. However, current atmospheric environment monitoring systems suffer from poor compatibility, expandability, and intelligence. To address these issues, a modular monitoring system was designed. On the main control board, only the microcontroller and essential components for its operation are retained. At the same time, several general-purpose module interfaces are equipped for connecting various modules. The main control board can automatically identify the type of *** replacing sensor modules and data transmission modules, this system can be easily adapted to various usage scenarios. Through multi-point transmission modules, distributed monitoring can be achieved. By incorporating edge computing modules, the monitoring system gains edge computing capabilities, allowing it to run neural network algorithms for predicting and tracing the sources of atmospheric pollutants, thus achieving intelligent monitoring. The results verified the effectiveness of the proposed modular monitoring system, demonstrating its ability to achieve full-scenario, distributed, and intelligent monitoring with strong expandability and high intelligence. © 2024 Copyright held by the owner/author(s).

关键词： environmental monitoring

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Decision Tree Based Incident Detection for distributed Progressive Signal System in an Organic Traffic control System 12th

Decision Tree Based Incident Detection for Distributed Progr...

引用

2023 international conference on Smart Cities and Green ICT Systems and international conference on Vehicle Technology and intelligent Transport Systems

作者： Thomsen, Ingo Tomforde, Sven Christian Albrechts Univ Kiel Intelligent Syst D-24118 Kiel Germany

ISBN: (纸本)9783031709654;9783031709661

Today's world-wide traffic congestion significantly contributes to wastage of time and fuel in addition to environmental stress, such as air pollution due to CO2 emissions. To address these concerns, traffic management systems are continually developed and improved to become intelligent and adaptable. For instance, self-organizing strategies like the Organic Traffic control (OTC) system present benefits such as enhanced efficiency, resilience, and scalability. Beyond the de-centralised and traffic-responsive manipulation of traffic signals, synchronised adjustment of traffic lights through Progressive Signal Systems ("Green Waves") plays an important role. As previous work we presented an approach for creating decentralised PSS, taking recognised incidents into account to steer the traffic flows. In this paper we further propose a decision-tree based incident detection mechanism.

关键词： Traffic management Organic traffic control Progressive signal systems Green waves Incident detection Supervised learning

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IoT-based environmental monitoring System 3

IoT-based Environmental Monitoring System

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3rd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhiram, D. Dhanush Reddy, K. Koushik Reddy, P. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This project meets the current important demand for the constant monitoring of environmental conditions because climate change and human activities affect ecosystems. Temperature and humidity are measured using the DHT11/DHT22 sensor connected to the ESP8266 microcontroller and the GPS module is used to obtaining the location information. In contrast to current approaches targeting the communication data or energy consumption only, the contaminants of this system are unified, location-based, and central. It provides immediate data transfer to the cloud through a comfortable low power interface for usage in farming, transportation, and more for environmental monitoring. The findings show that all the environmental conditions and location change can be monitored by the IoT-based system which use ESP32/Arduino microcontroller for sensing data by a Bluetooth or Wi-Fi through cloud platform or server and it gives alert message to web interface or mobile application when temperature increases or decreases more than threshold values. This practical system is especially valuable for those areas of interest that require a constant check, such as environmental control or the logistics and health safety of the transported goods or passengers. © 2025 IEEE.

关键词： environmental monitoring

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Next-Gen Energy Management: IoT Gateway for Smart monitoring and Appliance control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

This research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. The system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. These data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. The machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. The system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. The solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. This integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Soil Moisture Prediction Analysis for intelligent and Efficient control 6th

Soil Moisture Prediction Analysis for Intelligent and Effici...

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6th international conference on intelligent Communication, control and Devices, ICICCD 2024

作者： Mane, Renuka Khandekar, Prasad D. MIT World Peace University Pune India DES Pune University Pune India

ISBN: (纸本)9789819783281

This work employs novel techniques to measure soil water content, an important aspect of agricultural development and water resource management. We used a variety of machine learning models, to examine a large amount of soil moisture data that was gathered over time. In the beginning, we used tools like pandas, matplotlib, and scikit-learn to analyze and display the data extensively. We also used spectral intensity analysis to examine the relationship between soil moisture and several environmental conditions. This research checks how well different computer models predict soil moisture. We also try to make the soil moisture predictions better by using smart techniques and computer algorithms. We look into using distributed control systems to keep an eye on soil moisture in different farming areas. This study is part of a bigger idea called precision agriculture, where we use smart systems to analyze soil moisture data accurately. The results can help farmers make better choices, manage water well, and take care of the environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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3rd international conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616978

The proceedings contain 27 papers. The special focus in this conference is on Machine Learning, Cloud Computing and intelligent Mining. The topics include: Decentralized Optimal Tracking control of MRMs Under Health Indicator-Based Event-Triggered Mechanism;iterative Learning control for Nonlinear Switched Continuous-Time Systems with Time-Varying Delay;Model Migration and Joint Communication and Computing Resource Allocation in Native AI Wireless Networks;FTB-RRP control for 2-D Continuous-Discrete Systems in Roesser Model;Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf Optimization Algorithm;A Bat-BP Optimization Algorithm Based System of Dry Ice Decontamination for UAV;Optimal Resource Allocation Algorithm for Multi-user AI Tasks in Native AI Networks;study on Non-intrusive Load monitoring Method Based on K-means Clustering and Dual Convolutional Neural Networks;research and Application of computer Vision and Predictive Maintenance in Health Management of Conveying Equipment;attitude Stabilization control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection control;state Estimation and control of Switched Neural Networks with Mode-Dependent Hybrid Dwell Time;distributed Consensus control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential;lifetime Prediction of Fuel Cell Composite Operating Conditions Based on Genetic Algorithm Optimized Elman Neural Network;a Tiered Native Digital Twin for Telco Networks;defender: The Possibility of Repairing Jailbreak Defects;research on Special Vision Recognition and Fault Alarm System for Vertical Roller;malicious Traffic Classification Algorithm Based on Multimodal Fusion;Enhanced Affine Formation control in Multi-agent Systems with Disturbance Rejection Using RISE controller;model Predictive control for Contour control in Biaxial System.

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3rd international conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616930