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Research on forming mechanism of digitalized frozen sand mold green casting

Journal of Intelligent Manufacturing and Special Equipment 2025年第1期6卷 83-98页

作者： Haoqin Yang Zhongde Shan Dandan Yan Jianpei Shi Xiao Liang Shijie Dong Jian Huang Lei Luo Haoming Shi Runmou Wu State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China Department of Mechanical Engineering Tsinghua University Beijing China Nanjing Yangtze River Industrial Technology Research Institute Co. Ltd. Nanjing University of Aeronautics and Astronautics Nanjing China

Purpose This study aims to promote the sustainable green development of the casting industry. Design/methodology/approach This paper presents an innovative frozen sand mold green casting technology that utilizes water as a novel alternative to conventional organic or inorganic binders in the sand mold production process. The sand is solidified through cryogenic treatment and subsequently transformed into complex, high-quality structures utilizing Computer Numerical control machining or 3D printing methods. Findings In contrast to conventional sand mold manufacturing technology, this method has advantages in environmental conservation and sustainable production practices, especially its ability to minimize the emission of harmful gases and dust to nearly zero throughout the casting process. Additionally, the high thermal conductivity of the frozen sand molds significantly enhances the performance of the castings. Furthermore, by implementing an economic and efficient thermal dissipation circulation, it is possible to directly recycle more than 90% of the casting sand. This approach helps to avoid tedious procedures associated with traditional sand mold recovery methods that rely on physical and chemical techniques. Social implications This technology not only facilitates the green transformation of the contemporary casting industry but also establishes a new technology standard for more sustainable and environmentally friendly production processes. Originality/value This paper presents an innovative frozen sand mold green casting technology that utilizes water as a novel alternative to conventional organic or inorganic binders in the sand mold production process.

关键词： Frozen sand mold CNC machining 3D printing High-performance castings Green casting

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A Study on Decentralized Autonomous Organizations Based Intelligent Transportation System enabled by Blockchain and Smart Contract

A Study on Decentralized Autonomous Organizations Based Inte...

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2021 China Automation Congress, CAC 2021

作者： Hou, JiaChen Ding, WenWen Liang, Xiaolong Zhu, FengHua Yuan, Yong Wang, FeiYue Institute of Systems Engineering Macau University of Science and Technology China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Mathematics Renmin University of China Beijing China

ISBN: (纸本)9781665426473

With the rapid development of the transportation industry, Intelligent Transportation System (ITS) tries to adapt to industry changes through constructing new organizational forms, management methods, and incentive mechanisms. However, many new governance issues have arisen due to the transportation system's complexity, diversity, and uncertainty. DAO(Decentralized autonomous organization) enabled by blockchain and smart contracts are regarded as effective methods to solve the organizational governance problems for complex systems. Therefore, this article analyzes the main issues currently faced by ITS. Then, this paper proposed a 6-layer DAO structure framework for ITS and discussed its key research issues. Finally, a case study for the Macau transportation system is carried out, and further research directions are proposed. The DAO-based transportation system will represent the future trend of ITS development. This paper aims to motivate more discussion for this new direction and provide helpful guidance and reference for future research work. © 2021 IEEE

关键词： Smart contract

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

引用

Security and Communication Networks 2023年第1期2023卷

作者： Li, Chao Fu, Yuhan Zhang, Rui Liang, Hai Wang, Chonghua Li, Junjian Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China COMAC Shanghai Aircraft Manufacturing Co. Ltd Beijing100005 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. © 2023 Chao Li et al.

关键词： Internet of things

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Vibration control of a parallel microgripper driven by linear ultrasonic motors

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Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 2022年第1期42卷 11-15页

作者： Geng, Ranran Yao, Zhiyuan Xu, Hao Zhang, Yangyang Industrial Center/School of Innovation and Entrepreneurship Nanjing Institute of Technology Nanjing Nanjing211167 China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing Nanjing210016 China

This paper proposes an active control mothed based on the state feedback to suppress the vibration of the parallel microgripper finger driven by linear ultrasonic motors, to improve the stability of the microgripper. First, the dynamic model of the disk and the moving finger in microgripper system is established by finite eleH ment method, and the state equation is derived. Then, the controllability and stability of the vibration system are analyzed. The state feedback matrix is obtained by pole assignment method, and the vibration feedback conH trol law is designed. The feedback system is simulated and analyzed by Simulink to verify the vibration suppresH sion effect of the active control on the finger. Finally, the experiments of clamping steel ball and camellia pollen cell in micron scale are carried out by using the designed control system. The results indicate that the clamping performance and stability of the microgripper under the active control strategy can meet the requirements of micro-nano positioning accuracy. © 2022, Editorial Department of JVMD. All right reserved.

关键词： state feedback

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Research and Application of Real-time Computing Framework Based on Smart Power Plant

Research and Application of Real-time Computing Framework Ba...

引用

Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Junwei Fu Qing Guo Bao Gu Xinnan Ye Rongdong Yu Zhenwei Zhang Yuwei Meng Zheneng Digital Technology Co. Ltd Zhejiang Energy Technology Research Institute Co. Ltd State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Zheneng Digital Technology Co. Ltd Hangzhou China

At present, intelligent sensors and the industrial Internet have enabled remote monitoring and optimal control of smart power plants, reducing the workload of manual labor. However, the requirements of real-time and accuracy in big data analysis can not be satisfied with limited computing hardware resources, complex computing processes and weak algorithm robustness. To address real-time issue, a real-time computing framework based on early warning models is proposed. And it is reconstructed by the algorithm process with distribution computation, which is composed of five services: model master, model node, database, supervisor, and data preprocessing. Finally, a large number of models are deployed in the framework to realize real-time, reliable and accurate state awareness of equipment. After experimental verification, this framework has achieved the design requirements with constrained computing resources in the actual deployment of the coastal power plant and outperformed the traditional framework.

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System Identification with Variance Minimization via Input Design

arXiv

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arXiv 2022年

作者： Mao, Xiangyu He, Jianping Zhao, Chengcheng The Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China

The subspace method is one of the mainstream system identification method of linear systems, and its basic idea is to estimate the system parameter matrices by projecting them into a subspace related to input and output. However, most of the existing subspace methods cannot have the statistic performance guaranteed since the lack of closed-form expression of the estimation. Meanwhile, traditional subspace methods cannot deal with the uncertainty of the noise, and thus stable identification results cannot be obtained. In this paper, we propose a novel improved subspace method from the perspective of input design, which guarantees the consistent and stable identification results with the minimum variance. Specifically, we first obtain a closed-form estimation of the system matrix, then analyze the statistic performance by deriving the maximum identification deviation. This identification deviation maximization problem is non-convex, and is solved by splitting it into two sub-problems with the optimal solution guaranteed. Next, an input design method is proposed to deal with the uncertainty and obtain stable identification results by minimizing the variance. This problem is formulated as a constrained min-max optimization problem. The optimal solution is obtained from transforming the cost function into a convex function while ensuring the safety constraints through the method of predictive control. We prove the consistency and the convergence of the proposed method. Simulation demonstrates the effectiveness of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimal systems

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Robust Optimization for Quantum Reinforcement Learning control using Partial Observations

arXiv

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi 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 Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 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 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 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 quantum approximate optimization algorithm. 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 QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

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基于自解耦三明治结构的横向运动栅场效应晶体管MEMS微力传感器

引用

Engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

arXiv

引用

arXiv 2021年

作者： Wang, Qianhao He, Botao Xun, Zhiren Xu, Chao 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 School of Automation Nanjing Institute of Technology Nanjing211112 China

Gaze is an intuitive and direct way to represent the intentions of an individual. However, when it comes to assistive aerial teleoperation which aims to perform operators' intention, rare attention has been paid to gaze. Existing methods obtain intention directly from the remote controller (RC) input, which is unfriendly to non-professional operators, as the experimental results show in Sec. VI-C. Further, most teleoperation works do not consider environment perception which is vital to guarantee safety. In this paper, we present GPA-Teleoperation, a gaze enhanced perception-aware assistive teleoperation framework, which addresses the above issues systematically. We capture the intention utilizing gaze information, and generate a topological path matching it. Then we refine the path into a safe and feasible trajectory which simultaneously enhances the perception awareness to the environment which the operator is interested in. Additionally, the proposed method is integrated into a customized quadrotor system. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons verify that the proposed system is reliable, robust and applicable to even unskilled users. We will release the source code of our system to benefit related researches. © 2021, CC BY-NC-ND.

关键词： Remote control

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