This paper presents an innovative framework combining neuromorphic computing and stream processing analytics for energy-efficient smart greenhouse management. The proposed Adaptive Greenhouse Optimization and Resource...
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ISBN:
(数字)9798331518097
ISBN:
(纸本)9798331518103
This paper presents an innovative framework combining neuromorphic computing and stream processing analytics for energy-efficient smart greenhouse management. The proposed Adaptive Greenhouse Optimization and Resource Integration (AGORI) methodology addresses key challenges in real-time environmentalmonitoring, energy optimization, and sustainable agriculture. By leveraging Meta Spark Creator AR for visualization and implementing a novel hybrid algorithm combining Spiking Neural Networks (SNNs) with streaming data analytics, our system achieves 47% improved energy efficiency and 38% better crop yield prediction accuracy compared to traditional methods. The framework was validated across 14 international deployment sites, demonstrating robust performance under diverse climatic conditions. Results show significant improvements in resource utilization and operational sustainability, with particular emphasis on power stability and green energy integration.
The rapid integration of distributed energy resources and renewable energy systems into modern power grids has necessitated the development of intelligent operation platforms for Virtual Power Plants (VPPs). This stud...
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ISBN:
(数字)9798331511708
ISBN:
(纸本)9798331511715
The rapid integration of distributed energy resources and renewable energy systems into modern power grids has necessitated the development of intelligent operation platforms for Virtual Power Plants (VPPs). This study explores the technological optimization of a VPP smart operation platform based on a cloud-edge-endpoint collaborative architecture. The proposed architecture leverages cloud computing for centralized data analysis and optimization, edge computing for real-time decision-making and control, and endpoint devices for local monitoring and execution. Key challenges such as data transmission latency, resource allocation, and system scalability are addressed through innovative algorithms and optimization techniques. The framework ensures efficient coordination across all layers, enabling the VPP to enhance operational efficiency, reliability, and scalability. Experimental results demonstrate significant improvements in resource utilization, system responsiveness, and overall performance, providing a practical solution for the intelligent management of VPPs in dynamic energy environments.
Large-scale bridge projects are an vital part of national infrastructure, and are directly related to the safety of people's lives and properties. The identification of modal parameters of bridge structures under ...
Large-scale bridge projects are an vital part of national infrastructure, and are directly related to the safety of people's lives and properties. The identification of modal parameters of bridge structures under working conditions is an vital part of bridge damage identification. Considering the practicability of bridge detection, bridge detection should generally be based on environmental excitation. The existing means of modal parameter identification under environmental excitation are not suitable for The identification accuracy of the modal frequency is high, while the identification of the displacement mode has a large error. The existing means of modal parameter identification under environmental excitation have relatively high identification accuracy for the modal frequency, while the identification of the displacement mode has a large error. Due to the complexity and large-scale bridge structure, the importance and necessity of bridge structure health monitoring are more and more recognized by people. The means used in this paper is to improve the genetic algorithm to simulate the phenomena of reproduction, crossover and gene mutation that occur in the spontaneous selection and spontaneous heredity process. Genetic algorithm is a global optimization adaptive probabilistic search algorithm, which has the advantages of intelligent optimization and robustness.
Clinics around the globe are adopting newer, highly advanced technologies. The Internet of Things (IoT) has emerged due to advancements in information and communication technology. Integration of the IoT with accessib...
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Wireless ad-hoc networks are local area networks that are built without a centralized administration point. They are widely utilised in many applications, particularly those involving the Internet of Things (IoT), suc...
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This paper studies the event-triggered output optimal control for discrete-time multi-agent systems (MASs) with unmeasured system states. A state estimator is initially developed to monitor the agent's states, and...
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ISBN:
(数字)9798350353594
ISBN:
(纸本)9798350353600
This paper studies the event-triggered output optimal control for discrete-time multi-agent systems (MASs) with unmeasured system states. A state estimator is initially developed to monitor the agent's states, and subsequently, a new event-triggered reinforcement learning control method is devised using the state observer. To avoid the interdependence of agent control inputs, the hierarchy decomposition method is applied to assign priorities for each agent in the control design. A distributed event-triggered condition is designed for the sensor-to-controller channel of each agent, such that the data transmission among the agents can be greatly reduced. Lastly, stability analysis and numerical simulation are presented to demonstrate availability of the scheme.
The authors of this article discuss the holistic method of automation of gardening operations based on IoT technology that allows real time environmental factors to be monitored, decisions to be made based on data, an...
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ISBN:
(数字)9798331527495
ISBN:
(纸本)9798331527501
The authors of this article discuss the holistic method of automation of gardening operations based on IoT technology that allows real time environmental factors to be monitored, decisions to be made based on data, and those decisions to be executed at a distance. The objective of the system is to minimize the amount of water used, to improve the state of the plants, and ultimately to allow the users of the system to control their gardens from anywhere in the world with as little effort as possible. The concept makes use of the continuously monitored network of sensors to observe important parameters like soil moisture, temperature, humidity, light intensity and rainfall among others. After being received, this information is up to date and the system will use this data to manage irrigation and lighting systems for the plants efficiently. Also, the support of this cloudbased platform is essential today as it allows the users to look up the parameters in their garden remotely and go for manual intervention when required. All the advantages of cloud technology provide remote control of the system but also all the historical data sets which are important trends in future decision making.
As urbanization and population growth accelerate, efficient waste management becomes a critical challenge for municipalities and corporations. This proposed methodology presents a comprehensive waste and garbage manag...
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ISBN:
(纸本)9798350336474
As urbanization and population growth accelerate, efficient waste management becomes a critical challenge for municipalities and corporations. This proposed methodology presents a comprehensive waste and garbage management system designed for corporations by using advanced technologies such as the Raspberry Pi controller, sensors, and IoT through the Blynk platform. The system incorporates an ultrasonic sensor to accurately measure the garbage levels within waste bins, providing real-time data to a central Raspberry Pi unit. The gas sensors detect harmful gases emitted from decaying waste, enabling proactive measures to mitigate environmental pollution and health hazards. The Raspberry Pi acts as the core processing unit, managing data acquisition, analysis, and communication. An LCD display on each waste bin provides local information on current fill levels, gas levels, and system status. To enhance the system's connectivity and communication capabilities, GPRS (General Packet Radio Service) and GPS modules are integrated. The GPRS module enables remote data transmission to a central server, facilitating efficient monitoring and management of multiple waste bins across a corporation. The GPS module adds a geospatial dimension, enabling location tracking for each waste bin, contributing to route optimization and overall logistical efficiency. Utilizing the Blynk platform enables effortless remote monitoring and control through IoT integration. Authorized users can conveniently access real-time data and receive timely alerts regarding crucial fill levels through the Blynk mobile application. Moreover, waste bin management can be efficiently handled remotely using this platform. The system thus provides a user-friendly interface for administrators to optimize waste collection routes, schedule pickups, and allocate resources effectively. The proposed Waste Garbage Management System offers a cost-effective, scalable, and environmentally friendly solution for corporation
In recent years, the development of new technologies has improved the management of resources and services at the urban level. In this sense, several cities worldwide have developed intelligent infrastructures such as...
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The urgent demand for sustainable smart city solutions, fueled by rapid urbanization and environmental concerns, has prompted the adoption of advanced technologies to enhance living standards and resource efficiency a...
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ISBN:
(数字)9798350385403
ISBN:
(纸本)9798350385410
The urgent demand for sustainable smart city solutions, fueled by rapid urbanization and environmental concerns, has prompted the adoption of advanced technologies to enhance living standards and resource efficiency across various sectors. LoRaWAN technology emerges as an effective and affordable means to connect sensors in urban areas, addressing challenges such as scalability, energy consumption, and high deployment costs, faced by many smart city initiatives. This research tackles these issues by implementing four key low-cost monitoring systems: air quality, environmental, liquid flow and level, and smart streetlights. Contributing to a more sustainable urban environment, we design and deploy LoRaWAN-based systems equipped with sensors, and strategically placed gateways for optimal coverage. The collected data is transmitted to a central network server which can be accessed remotely for real-time monitoring and analysis. The result confirms that LoRaWAN-based monitoring systems efficiently gather and transmit data with low energy use, Implementation of these systems in smart cities enhances urban planning, resource management, and sustainability, facilitating proactive pollution control and cost reduction.
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