Edge intelligentcontroller (EIC) is widely used in Industrial Internet of Things (IIoT) as the core component of Industrial Edge computing (IEC). intelligent IIoT typically involves massive computationally intensive ...
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ISBN:
(纸本)9798350350227;9798350350210
Edge intelligentcontroller (EIC) is widely used in Industrial Internet of Things (IIoT) as the core component of Industrial Edge computing (IEC). intelligent IIoT typically involves massive computationally intensive tasks, multi-subtask of resource-constrained EICs are offloaded to the edge server (ES) for execution, thereby obtaining lower computing latency. Therefore, this paper investigates the multi-subtask offloading (MSTO) problem in IEC system, where there exist diverse offloading scenarios with varying parameters, such as the number of tasks, transmission rate, and edge computation ability. Accordingly, multiple tasks with dependencies are modelled as directed acyclic graphs (DAGs). Then, we develop a sequence to sequence (Seq2Seq) model and Asynchronous Advantage Actor-Critic (A3C) collaborative algorithm for MSTO (S-A3C) to obtain the best offloading policy. The performance analysis and comparisons demonstrate that our method has better decision-making, outperforming several other baseline algorithms. In conclusion, this paper provides a new perspective on reducing the overall computation latency of multi-subtask IEC system in IIoT.
Estimating camera motion and continuously reconstructing dense scenes in deformable environments presents a complex and open challenge. Many existing approaches tend to rely on assumptions about the scene's topolo...
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ISBN:
(纸本)9798350377712;9798350377705
Estimating camera motion and continuously reconstructing dense scenes in deformable environments presents a complex and open challenge. Many existing approaches tend to rely on assumptions about the scene's topology or the nature of deformable motion. However, these assumptions do not hold true in medical endoscopy applications. To address these challenges, we introduce DDS-SLAM, a novel dense deformable semantic neural SLAM that achieves accurate camera tracking, continuous dense scene reconstruction, and high-quality image rendering in deformable scenes. First, we propose a novel hybrid neural scene representation method capable of capturing both natural and artificial deformations. Additionally, by leveraging the 2D semantic information of the scene, we introduce a semantic loss function based on semantic distance fields. This approach guides network optimization at a higher level, thereby enhancing system performance. Furthermore, we validate our method through a series of experiments conducted on several representative medical datasets, demonstrating its superiority over other state-of-the-art approaches. The code is available at: https://***/IRMVLab/DDS-SLAM.
This research study proposes a novel approach for behavioral tracking and anomaly detection in digital systems by using AI-driven models, particularly for applications in signal processing and digital computer environ...
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We present an aerial vehicle composed of a custom quadrotor with tilted rotors and a helium balloon, called SBlimp. We propose a novel control strategy that takes advantage of the natural stable attitude of the blimp ...
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ISBN:
(纸本)9781665491907
We present an aerial vehicle composed of a custom quadrotor with tilted rotors and a helium balloon, called SBlimp. We propose a novel control strategy that takes advantage of the natural stable attitude of the blimp to control translational motion. Different from cascade controllers in the literature that controls attitude to achieve desired translational motion, our approach directly controls the linear velocity regardless of the heading orientation of the vehicle. As a result, the vehicle swings during the translational motion. We provide a planar analysis of the dynamic model, demonstrating stability for our controller. Our design is evaluated in numerical simulations with different physical factors and validated with experiments using a real-world prototype, showing that the SBlimp is able to achieve stable translation regardless of its orientation.
The current seasonal electricity peak, imbalance between supply and demand, and poor load regulation ability in the operation of the power grid are becoming increasingly prominent. In order to achieve orderly electric...
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In the context of contemporary digitalization, the remote oversight and management of substation apparatus via programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems has tra...
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The advancement of communication and information technology has facilitated the creation of the Internet of Things (IoT). Today, IoT plays a crucial role in tracking, recording, storing, displaying, and communicating ...
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The advancement of communication and information technology has facilitated the creation of the Internet of Things (IoT). Today, IoT plays a crucial role in tracking, recording, storing, displaying, and communicating data across various fields such as healthcare, smart cities, engineering, and more. This paper introduces a smart system designed for nursing staff in hospital wards, utilizing smart devices (STM32) with wireless capabilities and an ESP8266 WiFi communication module. These devices register and log in to the public cloud via a broadband network. The system integrates various sensors to detect light and humidity levels in the ward and includes a flame alarm function. It also controls small lamps and fans through infrared remote control and mobile phone. Additionally, the system monitors patients' heart rates and simulates blood pressure. Using the ESP8266's MQTT protocol, it uploads various data to the Alibaba Cloud platform for display. Users can observe this data through their mobile phones and send control instructions. The system also pushes notifications through DingTalk. The design and implementation of this system will enhance the efficiency and security of nursing work in hospital wards.
Big data boosts agricultural production through intelligent transformation;data will become the emerging element of modern agricultural production. Therefore, applying big data technology to agriculture is a new trend...
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ISBN:
(纸本)9798350350227;9798350350210
Big data boosts agricultural production through intelligent transformation;data will become the emerging element of modern agricultural production. Therefore, applying big data technology to agriculture is a new trend in the development of modern agriculture. It can promote the development and progress of agricultural information service technology to a large extent. It can also promote the overall development process of the agricultural field to a large extent. Developing smart agriculture can improve agricultural modernization and promote agricultural transformation and upgrading. Smart agriculture can improve the yield and quality of agricultural products, reduce the waste of natural resources, and reduce the pollution of the environment. There is an increasing number of research results related to smart agriculture. However, there is a lack of research on using big data analytics tools to sort out smart agriculture fully. Therefore, based on knowledge mapping, this paper analyzes intelligent agriculture's research hotspots and development trends. This study concludes that precision agriculture, the Internet of Things, big data, artificial intelligence, and cloud computing are the current research hotspots in intelligent agriculture. Furthermore, it is moving toward intelligence and sustainability.
Real-time vehicle classification is one of the most important services of traffic management, road safety, and security in the age of intelligent transportation systems and smart and developed cities. In this research...
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With the increasing installed capacity of photovoltaic (PV) in China, there is a risk of subsynchronous oscillations (SSOs) in grid-connected PV systems. As for the grid-connected PV systems, the high complexity of th...
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ISBN:
(纸本)9798350318562;9798350318555
With the increasing installed capacity of photovoltaic (PV) in China, there is a risk of subsynchronous oscillations (SSOs) in grid-connected PV systems. As for the grid-connected PV systems, the high complexity of the dynamic behavior and the difficulty of obtaining mathematical models have brought new challenges to the stability control of power systems. To enhance the stability of the modern power systems, this paper proposes a data-driven SSO suppression strategy for grid-connected PV systems. This strategy leverages data-driven predictive control to mitigate SSOs by constructing suitable objective functions and constraints. Furthermore, it employs the rolling optimization and feedback correction in the predictive control to determine the optimal input sequences for grid-connected PV systems, ensuring that the system's state converges to the desired steady state. The data-driven strategy proposed in this paper can be used as a bridge between the measurement data and the dynamic characteristics of the complex power system, which greatly enhances the stability and robustness of the SSO suppression. Finally, the effectiveness and robustness of the suppression strategy are verified in various conditions through MATLAB/Simulink simulation.
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