检索结果-内蒙古大学图书馆

A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

引用

IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations

arXiv

引用

arXiv 2021年

作者： Chen, Yiwei Pan, Yu Dong, Daoyi The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

states of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modelled. In machine learning, complex multiple multilinear correlations may also exist within input features. In this paper, we present a quantum-inspired multilinear model, named Residual Tensor Train (ResTT), to capture the multiple multilinear correlations of features, from low to high orders, within a single model. ResTT is able to build a robust decision boundary in a high-dimensional space for solving fitting and classification tasks. In particular, we prove that the fully-connected layer and the Volterra series can be taken as special cases of ResTT. Furthermore, we derive the rule for weight initialization that stabilizes the training of ResTT based on a mean-field analysis. We prove that such a rule is much more relaxed than that of TT, which means ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models. Numerical experiments demonstrate that ResTT outperforms the state-of-the-art tensor network and benchmark deep learning models on MNIST and Fashion-MNIST datasets. Moreover, ResTT achieves better performance than other statistical methods on two practical examples with limited data which are known to have complex feature interactions. Impact statement—Optimal learning may require considering high-order multilinear feature interactions. Recently, quantum-inspired tensor networks have been proposed to exploit high-order correlations. However, the vanilla tensor networks are specialized in capturing only one type of multilinear feature interaction. This paper proposes a novel network design which is able to describe generic feature interactions of different orders, within one model. Due to the residual connections, the training stability has also been promoted to an unprecedented level when compared to previous tensor network methods. Our methodology offers an alte

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in industrial control Through Trusted Smart Community and Lock Mechanism 23

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

引用

23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Huang, Weiguo Ding, Yong Li, Jun Wang, Yujue Liang, Hai Yang, Changsong Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin China Hkct Institute for Higher Education Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology 999077 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users' accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI. © 2024 IEEE.

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

An Optimal control Based Trajectory Planning Method for Cooperative Merging Maneuvers

An Optimal Control Based Trajectory Planning Method for Coop...

引用

Intelligent Robotics and systems (ISoIRS), International Symposium on

作者： Qi Wang Hao Ge Xuting Duan Jianshan Zhou State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control State Key Laboratory of Air Traffic Management System and Technology School of Transportation Science and Engineering Beihang University Beijing China State Key Laboratory of Air Traffic Management System and Technology Nanjing Research Institute of Electronic Engineering Nanjing China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China

Due to the prediction errors and diverse driving styles of surrounding vehicles, traffic congestion and even accidents can occur in the traffic bottleneck area where vehicles merge into the main road. To ensure that connected and automated vehicles (CAVs) can quickly and efficiently merge into the main road, this paper proposes a cooperative trajectory planning method based on optimal control for vehicle cut-in scenarios. A optimal control model is constructed, and a quintic polynomial is used to generate the initial solution of the optimal control problem to accelerate the solution process and improve the success rate. Finally, The obtained merging trajectory is then synchronized with the movement of vehicles on the main road to optimize their actions and ensure a safe merging process. Joint simulation with Robot Operating System (ROS) and RVIZ is utilized, and the results show that the proposed algorithm can quickly obtain cooperative results, the locally successful merging trajectory meets the requirements of smoothness and safety, and if the solution does not allow merging, the merging vehicle is instructed to terminate the execution.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in industrial control Through Trusted Smart Community and Lock Mechanism

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weiguo Huang Yong Ding Jun Li Yujue Wang Hai Liang Changsong Yang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

关键词： Crowdsourcing Software testing Smart cities industrial control Smart contracts Decentralized applications Software Blockchains Software reliability Security

来源：评论

学校读者我要写书评

暂无评论

Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

引用

WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments

EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Sw...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Xin Zhou Jiangchao Zhu Hongyu Zhou Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China National Engineering Research Center for Industrial Automation (Ningbo Institute) Ningbo China Huzhou Institute Zhejiang University Huzhou China

This paper presents a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources. The planning system is formulated under gradient-based local planning framework, where collision avoidance is achieved by formulating the collision risk as a penalty of a nonlinear optimization problem. In order to improve robustness and escape local minima, we incorporate a lightweight topological trajectory generation method. Then agents generate safe, smooth, and dynamically feasible trajectories in only several milliseconds using an unreliable trajectory sharing network. Relative localization drift among agents is corrected by using agent detection in depth images. Our method is demonstrated in both simulation and real-world experiments. The source code is released for the reference of the community.

关键词： Location awareness Systematics Codes Navigation Conferences Robustness Trajectory

来源：评论

学校读者我要写书评

暂无评论

VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation

VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Acc...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Ziming Ding Tiankai Yang Kunyi Zhang Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China National Engineering Research Center for Industrial Automation Ningbo Institute Ningbo China

Recently, quadrotors are gaining significant attention in aerial transportation and delivery. In these scenarios, an accurate estimation of the external force is as essential as the six degree-of-freedom (DoF) pose since it is of vital importance for planning and control of the vehicle. To this end, we propose a tightly-coupled Visual-Inertial-Dynamics (VID) system that simultaneously estimates the external force applied to the quadrotor along with the six DoF pose. Our method builds on the state-of-the-art optimization-based Visual-Inertial system [1], with a novel deduction of the dynamics and external force factor extended from VIMO [2]. Utilizing the proposed dynamics and external force factor, our estimator robustly and accurately estimates the external force even when it varies widely. Moreover, since we explicitly consider the influence of the external force, when compared with VIMO [2] and VINS-Mono [1], our method shows comparable and superior pose accuracy, even when the external force ranges from neglectable to significant. The robustness and effectiveness of the proposed method are validated by extensive real-world experiments and application scenario simulation. We will release an open-source package of this method along with datasets with ground truth force measurements for the reference of the community.

关键词： Force measurement Torque Force Dynamics Pose estimation Estimation Transportation

来源：评论

学校读者我要写书评

暂无评论

EVA-Planner: Environmental Adaptive Quadrotor Planning

EVA-Planner: Environmental Adaptive Quadrotor Planning

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Lun Quan Zhiwei Zhang Xingguang Zhong Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China National Engineering Research Center for Industrial Automation (Ningbo Institute) Ningbo China

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages¹ .

关键词： Adaptation models Trajectory planning Heuristic algorithms Predictive models Prediction algorithms Robustness Planning

来源：评论

学校读者我要写书评

暂无评论

Data-driven Self-triggered control via Trajectory Prediction

arXiv

引用

arXiv 2022年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples. © 2022, CC BY.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：