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第43届中国控制会议

作者： Yibo Zhang Dewei Li Yuanqiang Zhou Furong Gao Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University College of Electronic and Information Engineering Tongji University Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Clear Water Bay KowloonHong Kong and the Guangzhou HKUST Fok Ying Tung Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Quality prediction of plastic products has always been an important research topic in the injection molding production process. However, existing quality prediction methods struggle to handle both high-frequency data and statistical data generated during the injection molding production process. This paper introduces a novel quality prediction model for injection molding production, utilizing a combination of Convolutional Neural Networks(CNN) and Parallel-Cascade Long Short-Term Memory(PCLSTM) networks. The PCLSTM network is designed to handle the complexities of high-frequency data in injection molding,overcoming the limitations of traditional LSTM methods in processing long sequence data. By segmenting data into stages and utilizing a cascade mechanism, the PCLSTM architecture efficiently captures important temporal dynamics. Additionally, the CNN network is designed to extract statistical data features. The quality prediction results are obtained by fusing two types of data features and then passing them through a fully connected network. Experimental results on real production datasets show that the CNN-PCLSTM model outperforms existing models in prediction accuracy and training convergence speed.

关键词： LSTM CNN Quality Prediction Injection Molding Process

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Domain-adaptation with knowledge accumulation through parallel stacked autoencoders: methodology and application to sulfur recovery

Domain-adaptation with knowledge accumulation through parall...

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American control Conference (ACC)

作者： Tianhao Mou Jinfeng Liu Yuanyuan Zou Shaoyuan Li Maria Gabriella Xibilia Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China Department of Chemical and Materials Engineering University of Al-berta Edmonton Alberta Canada Department of Engineering University of Messina Messina Italy

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Data-driven predictive models for end-point quality variables are important tools in industrial process modeling. However, establishing an effective predictive model with limited labeled data remains challenging. Transfer learning (TL) offers a solution by leveraging knowledge from similar but different tasks. This paper introduces a novel TL-based predictive model, domain-adaptation parallel stacked auto encoders (DA-PSAE), which can extract and accumulate knowledge from multiple similar processes. First, a parallel model structure is designed for the simultaneous extraction of static and plastic latent features. Besides, an effective TL-based training strategy is proposed, which utilizes data from multiple similar processes. The proposed model is applied to a sulfur recovery unit composed of four parallel sub-units. Experimental results verify the effectiveness of the proposed model.

关键词： Training Industries Process modeling Transfer learning Predictive models Feature extraction Data models

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Mixture Gaussian Process Model with Gaussian Mixture Distribution for Big Data

SSRN

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SSRN 2024年

作者： Guan, Yaonan Li, Dewei He, Shaoying Ren, Shuangshuang Liu, Shuren Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Dongchuan Road 800 Shanghai200240 China Northwest Branch China Petroleum Exploration and Development Research Institute Yanerwan Road 535 Gansu Lanzhou730000 China

In the era of chemical big data, the high complexity and strong interdependencies present in the datasets pose considerable challenges when constructing accurate parametric models. The Gaussian process model, owing to its non-parametric nature, demonstrates better adaptability when confronted with complex and interdependent data. However, the standard Gaussian process has two significant limitations. Firstly, the time complexity of inverting its kernel matrix during the inference process is tremendous. Secondly, all data share a common kernel function parameter, which mixes different data types and reduces the model accuracy in mixing-category data identification problems. In light of this, this paper proposes a mixture Gaussian process model that addresses these limitations. This model reduces time complexity and distinguishes data based on different data features. It incorporates a Gaussian mixture distribution for the inducing variables to approximate the original data distribution. Stochastic Variational Inference is utilized to reduce the computational time required for parameter inference. The inducing variables have distinct parameters for the kernel function based on the data category, leading to improved analytical accuracy and reduced time complexity of the Gaussian process model. Numerical experiments are conducted to analyze and compare the performance of the proposed model on different-sized datasets and various data category cases. © 2024, The Authors. All rights reserved.

关键词： Big data

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Recent advances on formal methods for safety and security of cyber-physical systems

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control Theory and Technology 2020年第4期18卷 459-461页

作者： Xiang Yin Shaoyuan Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

Cyber-physical systems(CPSs)are engineering systems with both computational and physical components[1].Typical CPSs include energy systems,transporation systems,autonomous vchicles,*** are usual hybrid involving complex interactions of continuous dynamics with discrete *** development of cotroller design and verification algorithms for such complex systems are crucial and challenging *** demands for safety and security of CPSs put stringent constraints on their analysis and design,and ncessitat the use of formal model-based *** recent years,we have witnessed a substantial increase in the use of formal techniques for the verifcation and design of safetyritical and security senstve CPSs[2].

关键词： formal CPS autonomous

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Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning

Autonomous Exploration and Mapping for Mobile Robots via Cum...

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

作者： Zhi Li Jinghao Xin Ning Li Department of Automation Shanghai Jiao Tong University Shanghai P.R. China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engi-neering Research Center of Intelligent Control and Management Shanghai China

Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with DRL, and first propose a Cumulative Curriculum Reinforcement Learning (CCRL) training framework to alleviate the issue of catastrophic forgetting faced by general CL. Besides, we present a novel state representation, which considers a local egocentric map and a global exploration map resized to the fixed dimension, so as to flexibly adapt to environments with various sizes and shapes. Additionally, for facilitating the fast training of DRL models, we develop a lightweight grid-based simulator, which can substantially accelerate simulation compared to popular robot simulation platforms such as Gazebo. Based on the customized simulator, comprehensive experiments have been conducted, and the results show that the CCRL framework not only mitigates the catastrophic forgetting problem, but also improves the sample efficiency and generalization of DRL models, compared to general CL as well as without a curriculum. Our code is available at https://***/BeamanLi/CCRL_Exploration.

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Building Energy Optimization Based on Biased ReLU Neural Network

Building Energy Optimization Based on Biased ReLU Neural Net...

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第40届中国控制会议

作者： Hongyi Li Xinglong Liang Jun Xu Harbin Institute of Technology Key Laboratory of System Control and Information Processing Ministry of Education

This paper proposes a building energy optimization strategy based on artifical intelligence technology modeling ***,the data set generated by Energy Plus energy consumption simulation software is used as the training set and test set of the Biased ReLU neural network（BRNN）.Secondly,the building energy consumption prediction model and indoor temperature prediction model are built based on the Biased ReLU neural ***,model predictive control（MPC） is uesd to achieve energy saving by controlling the set temperature of the building’s Heating,Ventilation and Air Conditioning（HVAC） ***,the joint simulation of MATLAB and EnergyPlus is realized by introducing the building control virtual test bed（BCVTB）.The results show that our method can effectively reduce building energy consumption.

关键词： Biased ReLU neural network MPC building energy consumption HVAC system

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Robust sliding model predictive control with disturbance compensation in application to AUV pitch-axis motion

Robust sliding model predictive control with disturbance com...

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Chinese control and Decision Conference, CCDC

作者： Jiuwu Hui Jingqi Yuan State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Shenzhen Department of Automation and the Key Laboratory of System Control and Information Processing/Ministry of Education Shanghai Jiao Tong University Shanghai

ISBN: (纸本)9781665478977

Constraints on the control input and system states as well as uncertain disturbances are the practical challenges in many motion control systems. In this paper, a disturbance observer (DO) with two-step disturbance compensation algorithm is proposed to deal with disturbances and enhance the robustness of the controller, while a discrete sliding mode predictive control (DSMPC) scheme is designed to handle saturation constraints on the control input and system states with the application to an autonomous underwater vehicle (AUV). The DSMPC combines the model predictive control (MPC) with the discrete sliding mode control (DSMC) using linear matrix inequalities approach. The stability of the proposed DSMPC are proved via Lyapunov stability theorem. Finally, simulation results are presented to illustrate the effectiveness of the whole control scheme.

关键词： Simulation Prediction algorithms Stability analysis Robustness Linear matrix inequalities Motion measurement Motion control

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AoI-Reliability Analysis with Block Length over Erasure Channels Using Rateless Codes

AoI-Reliability Analysis with Block Length over Erasure Chan...

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International Conference on Communication Technology (ICCT)

作者： Haobo Huang Yusun Fu Yue Qiao Junpeng Yin Weiwu Yan Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

As a metric to measure the information freshness, Age of information (AoI) is widely used to evaluate low-latency high-reliability communication. Since the communication in Industrial Internet of Things (IIoT) often has the special requirements, the relationship among AoI, reliability and block length in time-critical scenarios with bounded latency and non-time-critical scenarios without bounded latency is explored respectively in this paper, based on erasure channels using rateless codes. We build a model using LT codes over erasure channels and consider two metrics: average AoI and bounded AoI. Theoretical analysis and simulation results show that AoI has a minimum value with different block lengths. Furthermore, we draw a conclusion that the minimum value of AoI under different reliability is monotonically decreasing functioned with reliability. Finally, the impact of overhead on reliability and AoI of LT codes in time-critical scenarios is studied.

关键词： Measurement Codes Simulation Reliability theory information age Communications technology Time factors Low latency communication Industrial Internet of Things

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Enhancing the McEliece Scheme Based on Concatenation of Polar Codes and Blocked QC-LDPC Codes

Enhancing the McEliece Scheme Based on Concatenation of Pola...

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IEEE Conference on Communications and Network Security (CNS)

作者： Xin Lin Yusun Fu Zihao Wang Junpeng Yin Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350375961

ISBN: (纸本)9798350375978

This paper proposes an enhanced McEliece cryptosystem, leveraging a concatenation scheme of Polar codes and blocked QC-LDPC codes. During the key generation phase, the generator matrices for both Polar and blocked QC-LDPC codes are employed as private keys. These matrices are subsequently multiplied by reversible and random permutation matrices respectively. The products of these multiplications are then combined to form the public key, effectively concealing the structural properties of the generator matrices. In the encryption phase, the plaintext is encrypted sequentially through the Polar codes and the blocked QC-LDPC codes. This process introduces two layers of random error vectors, culminating in the generation of the encrypted ciphertext. The proposed scheme reduces the public key length by 54% and doubles the security level compared to the original McEliece scheme with similar parameters. It also significantly reduces the total key length compared to improved schemes based on Polar codes and QC-LDPC/MDPC codes at an 80-bit security level, and shortens the private key length compared to schemes based on Polar-LDPC concatenated codes. Notably, the scheme is secure against known structural and decoding attacks while increases resistance to information set decoding (ISD) attacks. Through the randomization and hashing of the plaintext, the proposed scheme can achieve IND-CCA2 security.

关键词： Resistance Codes Public key Network security Generators Vectors Decoding Encryption Concatenated codes Polar codes

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Boltzmann Robust Soliton Distribution for Rateless Codes

Boltzmann Robust Soliton Distribution for Rateless Codes

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International Conference on Communication Technology (ICCT)

作者： Jinhui Tang Yusun Fu Yue Qiao Junpeng Yin Haobo Huang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The decoding success rate may be worse when classical degree distributions are used in short-packet transmission of rateless codes, especially in industrial communication scenarios. A novel Boltzmann degree distribution is proposed for Luby Transform (LT) codes. The Boltzmann degree distribution has a high decoding success rate for short-packet transmission but a lower decoding success rate in long-packet transmission. To balance the performance of rateless codes for short and long packet transmission, the Boltzmann degree distribution and the robust soliton distribution (RSD) are combined with a new joint degree distribution, called Boltzmann Robust Soliton Distribution (BRSD). Simulation results show that compared with RSD and Poisson robust soliton distribution (PRSD), the proposed joint distribution can achieve a higher decoding success rate and reduce the average degree, especially in short-packet transmission of industrial communication.

关键词： Codes Simulation Industrial communication Symbols Solitons Transforms Robustness Decoding Complexity theory Physics

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