检索结果-内蒙古大学图书馆

LCA-based Multi-scenario Study on Steel or Aluminum Wheel Hub for Passenger Vehicles

Procedia CIRP 2023年 116卷 191-196页

作者： Lu Gao Zhihui Wang Yanan Wang Tao Peng Weipeng Liu Renzhong Tang State Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou 310058 China Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province Zhejiang University Hangzhou 310058 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou 310058 China School of Engineering Zhejiang University City College Hangzhou 310015 China

Lightweighting is one of the enabling techniques for energy saving and carbon reduction in the automotive industry. However, the life cycle performance of lightweight components remains unclear. Wheel hub is an important component, and its trend of replacing steel with aluminum is happening. Based on life cycle assessment and using ReCiPe model, this paper reports a cradle-to-grave study on the environmental impact of steel and aluminum wheel hub in passenger vehicles. Considering multi-scenario in the Chinese market, it is found that when the service life of passenger vehicle is less than 5.6 years, steel wheel hub is more environmental-friendly, and when the service life is less than 8.6 years, steel wheel hub also has more economic advantages. Combining different lightweight methods and different types of vehicles, it is found that structural optimization reduces the environmental impact of per unit weight mostly, process optimization reduces the life cycle environmental impact of wheel hub, and using lightweight steel wheel hub in electric vehicles has more potential in environmental benefits. some suggestions are, 1) to choose wheel hub that matches your actual usage to enjoy more environmental-economic advantages; 2) to prioritize structural-process integrated optimization for lightweight wheel hub; and 3) to use more lightweight components in electric vehicles.

关键词： Life cycle assessment Steel wheel hub Aluminum wheel hub Lightweight methods Scenario study

来源：评论

学校读者我要写书评

暂无评论

A multi-notary cross-chain system with supervision and identity tracking

引用

Peer-to-Peer Networking and Applications 2025年第4期18卷 1-18页

作者： Liang, Hai Lu, Xiaoye Peng, Xinyong Wang, Yujue Yang, Changsong Wang, Lun Li, Jun Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China DBAPP Security Co Institute of Digital Talents Innovation Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China

The interoperability of blockchain cross-chain systems still poses a risk of data and user identity leakage. Concurrently, regulators encounter challenges in effectively verifying user identities, and the discrepancy between cross-chain regulation and the protection of user identity and privacy remains unaddressed. To address the aforementioned issues, this paper proposes an Identity-Verified Multi-Notary Cross-Chain Traceability and Regulation (IMCTR) system. The IMCTR system achieves cross-chain activity supervision through a supervision chain, mitigates centralization risks by utilizing a notary chain, and designs a multi-receiver encryption scheme to enhance the efficiency of signcryption and de-signcryption in cross-chain interactions while reducing computational overhead for users and safeguarding identity privacy. Additionally, regulators can effectively verify the receiver’s identity, swiftly identify unauthorized recipients, and prevent information leakage. Finally, a consortium blockchain cross-chain system that facilitates collaboration among multiple business chains has been established. The theoretical analysis and experimental results demonstrate that IMCTR significantly reduces the computational costs associated with encryption/decryption and signcryption/de-signcryption processes when compared to existing multi-receiver encryption schemes. Furthermore, IMCTR exhibits improved efficiency in cross-chain operations, as well as in identifying and tracking illicit users. This validates the effectiveness and high efficiency of IMCTR.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

A Novel Approach Based on Modified and Hybrid Flower Pollination Algorithm to Solve Multi-objective Optimal Power Flow

引用

IAENG International Journal of Applied Mathematics 2021年第4期51卷 1-18页

作者： Chen, Gonggui Qin, Qilin Zhou, Ping Kang, Peng Zeng, Xianjun Long, Hongyu Zou, Mi Professor of Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Master degree candidate of Chongqing University of Posts and Telecommunications Chongqing400065 China Senior engineer of Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China Senior engineer of State Grid Chongqing Electric Power Company Chongqing400015 China Professor level senior engineer of Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Assistant professor of Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

In this paper, a modified and hybrid flower pollination algorithms (MHFPA) is proposed for dealing with the multi-objective optimal power flow (MOOPF) problem with conflictive objectives. The algorithm combines the mutation and crossover process in the differential evolution (DE) algorithm, introduces the sinusoidal nonlinear dynamic switching probability (SNDSP) and the elite strategy of elder generation (ESEG), which can improve the shortcomings of the original pollen algorithm that it is easy to fall into the local optimum and the diversity is insufficient. A screening approach with Pareto-dominant rule (SAPR) is proposed to ensure that the state variable can satisfy the inequality constraints of the power system. A uniformly distributed Pareto optimal set (POS) is obtained by the non-dominant sorting with elite strategy (NSES) based on Rank and Density estimation, and the best trade-off solution (BTS) is determined from the POS obtained by the fuzzy affiliation theory. For practicality, the total fuel cost, active power loss, emissions and voltage deviation are selected as objective functions. Due to the limitations of the actual power system, the valve point effect is also considered. The IEEE30-, 57- and IEEE118-bus test systems are used to verify the performance of the proposed MHFPA. In addition, two © 2021. All Rights Reserved.

关键词： Electric load flow

来源：评论

学校读者我要写书评

暂无评论

Intention-Aware Planner for Robust and Safe Aerial Tracking

Intention-Aware Planner for Robust and Safe Aerial Tracking

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Qiuyu Ren Huan Yu Jiajun Dai Zhi Zheng Jun Meng Li Xu Chao Xu Fei Gao Yanjun Cao The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Robotics Institute of Zhejiang University Yuyao China Huzhou Institute Zhejiang University Huzhou China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to speci cally evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.

关键词： Target tracking Semantics Prediction methods Turning Prediction algorithms Robustness Safety Planning Trajectory optimization Quadrotors

来源：评论

学校读者我要写书评

暂无评论

Automatic Depth Optimization for Quantum Approximate Optimization Algorithm

arXiv

引用

arXiv 2022年

作者： Pan, Yu Tong, Yifan Yang, Yi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Mathematics and Statistics McGill University MontrealQCH3A 0B9 Canada

Quantum Approximate Optimization Algorithm (QAOA) is a hybrid algorithm whose control parameters are classically optimized. In addition to the variational parameters, the right choice of hyperparameter is crucial for improving the performance of any optimization model. control depth, or the number of variational parameters, is considered as the most important hyperparameter for QAOA. In this paper we investigate the control depth selection with an automatic algorithm based on proximal gradient descent. The performances of the automatic algorithm are demonstrated on 7-node and 10-node Max-Cut problems, which show that the control depth can be significantly reduced during the iteration while achieving an sufficient level of optimization accuracy. With theoretical convergence guarantee, the proposed algorithm can be used as an efficient tool for choosing the appropriate control depth as a replacement of random search or empirical rules. Moreover, the reduction of control depth will induce a significant reduction in the number of quantum gates in circuit, which improves the applicability of QAOA on Noisy Intermediate-scale Quantum (NISQ) devices. © 2022, CC BY.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Capacity Model and Optimal Scheduling Strategy of Multi-Microgrid Based on Shared Energy Storage

SSRN

引用

SSRN 2024年

作者： Dai, Bin Wang, Honglei Li, Bin Li, Chengjiang Tan, Zhukui Electrical Engineering College Guizhou University Guiyang550025 China Key Laboratory of "Internet+" Collaborative Intelligent Manufacturing in Guizhou Provence Guizhou Guiyang550025 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China School of Engineering University of Tasmania HobartTAS7005 Australia Electric Power Research Institute Guizhou Power Grid Co. Ltd. Guiyang550002 China

The large-scale use of renewable energy (RE) requires proportional investment in energy storage to solve the uncertainty of both the supply and demand sides of the power grid, which brings about the problems of high investment costs and long payback periods. This paper proposes a multi-microgrid energy storage sharing (SES) model. The SES model shapes the virtual energy storage capacity during power system operation, and the demand for energy storage capacity is reduced. A benefit distribution mechanism is developed to ensure fair income distribution among participants based on their investment proportion, promoting direct benefit interaction. A bi-level optimization method is designed to optimize the energy storage capacity and scheduling strategy simultaneously to ensure that they match each other. A non-dominated sorting equilibrium optimizer algorithm is proposed to avoid the Pareto solution set falling into local optimal and ensure the reasonable realization of the proposed benefit distribution mechanism. The results show that compared with distributed energy storage, the SES model reduces the required storage capacity of the system by 43.27% and reduces the daily investment and operation and maintenance cost by 25.98%. With the same operational performance, the SES model requires less storage capacity and 97.30% self-consumption of RE. © 2024, The Authors. All rights reserved.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

A Reinforcement Learning-Based Detection Method for False Data Injection Attack in Distributed Smart Grid

A Reinforcement Learning-Based Detection Method for False Da...

引用

International Conference on Informative and cybernetics for Computational Social systems (ICCSS)

作者： Kuo Zhang Zheng-Guang Wu Institute of Cyber-Systems and Control Zhejiang University Hangzhou China The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665402460

False data injection attack(FDIA) is a traditional attack for the smart grid. There are many methods for the detection of the FDIA, but few of them can send the attack alarm successfully without an attack model. In this paper, we propose a reinforcement learning-based FDIA detection method for the distributed smart grid. The detection problem is formulated as a partially observable Markov decision process(POMDP) problem, and the observation of the POMDP can be obtained from the estimation of state and attack which come from the Kalman filter. By using the Sarsa algorithm, we can get a Q-table through online training. Finally, we use the IEEE-118 bus power system to evaluate the performance of our detector, and numerical results show the accurate response for the FDIA.

关键词： Training Learning systems Estimation Distributed databases Detectors Markov processes Smart grids

来源：评论

学校读者我要写书评

暂无评论

Star-Convex Constrained Optimization for Visibility Planning with Application to Aerial Inspection

arXiv

引用

arXiv 2022年

作者： Liu, Tianyu Wang, Qianhao Zhong, Xingguang Wang, Zhepei Xu, Chao Zhang, Fu Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Mechanical Engineering The University of Hong Kong Hong Kong

The visible capability is critical in many robot applications, such as inspection and surveillance, etc. Without the assurance of the visibility to targets, some tasks end up not being complete or even failing. In this paper, we propose a visibility guaranteed planner by star-convex constrained optimization. The visible space is modeled as star convex polytope (SCP) by nature and is generated by finding the visible points directly on point cloud. By exploiting the properties of the SCP, the visibility constraint is formulated for trajectory optimization. The trajectory is confined in the safe and visible flight corridor which consists of convex polytopes and SCPs. We further make a relaxation to the visibility constraints and transform the constrained trajectory optimization problem into an unconstrained one that can be reliably and efficiently solved. To validate the capability of the proposed planner, we present the practical application in site inspection. The experimental results show that the method is efficient, scalable, and visibility guaranteed, presenting the prospect of application to various other applications in the future. Copyright © 2022, The Authors. All rights reserved.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Robust optimization for quantum reinforcement learning control using partial observations

引用

Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

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：