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arXiv 2024年

作者： Zhang, Mengqi Ye, Xiaotian Liu, Qiang Ren, Pengjie Wu, Shu Chen, Zhumin School of Computer Science and Technology Shandong University China School of Computer Science Beijing University of Posts and Telecommunications China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China

Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge. Model editing emerges as a promising solution to address these challenges. However, existing editing methods struggle to track and incorporate changes in knowledge associated with edits, which limits the generalization ability of post-edit LLMs in processing edited knowledge. To tackle these problems, we propose a novel model editing method that leverages knowledge graphs for enhancing LLM editing, namely GLAME. Specifically, we first utilize a knowledge graph augmentation module to uncover associated knowledge that has changed due to editing, obtaining its internal representations within LLMs. This approach allows knowledge alterations within LLMs to be reflected through an external graph structure. Subsequently, we design a graph-based knowledge edit module to integrate structured knowledge into the model editing. This ensures that the updated parameters reflect not only the modifications of the edited knowledge but also the changes in other associated knowledge resulting from the editing process. Comprehensive experiments conducted on GPT-J and GPT-2 XL demonstrate that GLAME significantly improves the generalization capabilities of post-edit LLMs in employing edited knowledge. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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System Dynamics-Based Analysis on Factors Influencing Comprehensive Competence of Primary and Secondary School Students

System Dynamics-Based Analysis on Factors Influencing Compre...

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Chinese Automation Congress (CAC)

作者： Sifeng Jing Xiwei Liu Xiaoyan Gong Hongxia Zhao The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China

With rapid progress of artificial intelligence (AI) , it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures; 2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents.

关键词： Analytical models Text analysis System dynamics Education Decision making Predictive models Market research

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Exponential Synchronization of Chaotic Lur'e systems using Sampled-Data PI Control

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IFAC-PapersOnLine 2023年第2期56卷 9787-9792页

作者： Hong-Zhang Wang Xing-Chen Shangguan Du Xiong Chuan-Ke Zhang School of Automation China University of Geosciences Wuhan 430074 P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 P. R. China

In this paper, an exponential synchronization design method for chaotic Lur'e systems is developed. Firstly, a sampled-data based proportional-integral (PI) type controller is used in the slave system to achieve the synchronization. Next, the synchronization problem of the chaotic Lur'e system is transformed into the stabilization problem of the synchronization error system. Then, by introducing virtual vectors for reconstruction, a new augmented error system is obtained. As a result, the synchronization design based on sampled-data PI type controller can be achieved using the existing sampling system method. And based on this, a criterion for exponential synchronization design is obtained using the Lyapunov functional method. Finally, the effectiveness of the developed method is demonstrated by numerical example.

关键词： Chaotic Lur'e systems sampled-data PI control exponential synchronization system augmentation method

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Adaptive Backstepping Sliding Mode Control for Underwater Glider Trajectory Tracking Based on Nonlinear Disturbance Observer

Adaptive Backstepping Sliding Mode Control for Underwater Gl...

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Chinese Automation Congress (CAC)

作者： Zhenjia Li Weihua Cao Chao Gan Mingming Wen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Guangzhou Marine Geological Survey Guangzhou China

An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observer is devised to observe ocean current. Then, the backstepping sliding mode control is used to devise motion attitude controller of UG to ensure that UG can track the target trajectory quickly. Additionally, the stability of UG's control system is analyzed. In the end, the proposed control strategy is compared with other approaches.

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Obstacle Detection of Unmanned Surface Vessel based on Faster RCNN

Obstacle Detection of Unmanned Surface Vessel based on Faste...

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Industrial Cyber-Physical systems (ICPS)

作者： Jiahe Cai Sheng Du Chengda Lu Bo Xiao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Guangzhou Marine Geological Survey Guangzhou China

With the development of unmanned surface vessel in the world today, obstacle avoidance using environmental information is the basis to ensure its high maneuvering performance and safety. However, directly using standard algorithms will lead to missing and wrong identification severely for characteristics of marine obstacles. This paper adds a multi-scale feature extraction layer of dilation convolution and group convolution to Faster Region based Convolutional Neural Network (Faster-RCNN), the baseline model, and changes the classification algorithm to improve its robustness and accuracy. Soft-Non Maximum Suppression (Soft-NMS) is used to enhance the prediction effects further. After improvements, the mean average precision value increases by 3.35%, and the final loss value decreases by 0.20. Given the phenomenon of missing and misidentification in the prediction by the baseline model, the results of our new model show outstanding performance.

关键词： Convolution Predictive models Feature extraction Prediction algorithms Robustness Classification algorithms Safety

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Deep Reinforcement Learning for Dynamic Error Compensation in 3D Printing

Deep Reinforcement Learning for Dynamic Error Compensation i...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Dong Wang Zhen Shen Xiangyang Dong Qihang Fang Weixing Wang Xisong Dong Gang Xiong School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

In the 3D printing process, various error factors can affect the accuracy of the final printing quality. However, current 3D printing error compensation methods have limited effects and usually cannot work in real-time. The 3D printing error compensation process is modeled as a Markov decision process (MDP) in this paper, and Deep Reinforcement Learning (DRL) is applied for dynamic error compensation. This method learns autonomously through trial and error by interacting with the printing environment, which makes it adaptable to various types of 3D printers without specific training. Then, we simulate the digital light processing (DLP) 3D printing. Due to the huge state and action space of sliced images, applying the DRL algorithm to DLP is challenging. We propose an error compensation method based on morphological image operation and use Autoencoder to extract error features to reduce the state space. We then implement our method using a Twin Delayed Deep Deterministic policy gradient algorithm (TD3). The results demonstrate the effects of our method in compensating 3D printing errors.

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Pre-Chirp-Domain Index Modulation for Affine Frequency Division Multiplexing

Pre-Chirp-Domain Index Modulation for Affine Frequency Divis...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Guangyao Liu Tianqi Mao Ruiqi Liu Zhenyu Xiao Electronic and Information Engineering Beihang University Beijing China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing China Yangtze Delta Region Academy Beijing Institute of Technology (Jiaxing) Jiaxing China ZTE Corporation Wireless and Computing Research Institute Beijing China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Affine frequency division multiplexing (AFDM), tailored as a novel multicarrier technique utilizing chirp signals for high-mobility communications, exhibits marked advantages compared to traditional orthogonal frequency division multiplexing (OFDM). AFDM is based on the discrete affine Fourier transform (DAFT) with two modifiable parameters of the chirp signals, termed as the pre-chirp parameter and post-chirp parameter, respectively. These parameters can be fine-tuned to avoid overlapping channel paths with different delays or Doppler shifts, leading to performance enhancement especially for doubly dispersive channel. In this paper, we propose a novel AFDM structure with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can embed additional information bits into the prechirp parameter design for both spectral and energy efficiency enhancement. Specifically, we first demonstrate that the application of distinct pre-chirp parameters to various subcarriers in the AFDM modulation process maintains the orthogonality among these subcarriers. Then, different prechirp parameters are flexibly assigned to each AFDM subcarrier according to the incoming bits. By such arrangement, aside from classical phase/amplitude modulation, extra binary bits can be implicitly conveyed by the indices of selected prechirping parameters realizations without additional energy consumption. At the receiver, both a maximum likelihood (ML) detector and a reduced-complexity ML-minimum mean square error (ML-MMSE) detector are employed to recover the information bits. It has been shown via simulations that the proposed AFDM-PIM exhibits superior bit error rate (BER) performance compared to classical AFDM, OFDM and IM aided OFDM algorithms.

关键词： Wireless communication Doppler shift Fourier transforms OFDM Bit error rate Modulation Detectors

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Research on Anti-Swing Strategies for Bridge Cranes

Research on Anti-Swing Strategies for Bridge Cranes

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Chinese Automation Congress (CAC)

作者： Shiqi Wang Enguang Hou Haoyuan Zheng Yuyi Jiang Zhen Shen Gang Xiong The College of Rail Transit of Shandong Jiaotong University Jinan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China State Key Laboratory of Multimodal Artifcial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

A study is conducted on the LH5T-13.5M electric crane, focusing on the swing characteristics of the load during steel coil lifting operations and the displacement issue of the crane. Initially, the system model is formulated using the Lagrange dynamic equations, translating the practical challenges into a solvable mathematical framework and deriving the transfer function of interest. Subsequently, the development of three control strategies is elucidated, formulated on the basis of the established model: a conventional PID control system, a fuzzy PID control system, and an adaptive proportional factor fuzzy neural PID controller. These systems are simulated and validated using Simulink. A comparative experimental setup is employed to evaluate performance. The results show that when the step input signal is 1, the maximum absolute load swing Angle of the traditional PID, fuzzy PID and adaptive scale factor fuzzy neural PID controller are 0.185°, 0.054° and 0.049° respectively for the working condition 1 experiment. The percentage overshoot corresponding to the wheelbarrow is 37.1%, 40.0%, and 30.5%, respectively. The findings indicate that the introduced fuzzy neural PID controller, equipped with an adaptive scaling factor, exhibits superior stability and effectiveness in maintaining the crane's operation within the optimal range of parameters.

关键词： Bridges Fuzzy control Adaptation models Cranes PI control Adaptive systems Fuzzy neural networks Mathematical models PD control Load modeling

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Intermittent control for nonlinear hybrid stochastic delayed systems with measurement error via dynamic event-triggered mechanism

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ISA Transactions 2025年

作者： Zhao, Hairui Chen, Dongyan Hu, Jun Department of Applied Mathematics Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin150080 China School of Automation Harbin University of Science and Technology Harbin150080 China

This paper deals with the problem of intermittent control based on dynamic event-triggered mechanism (DETM) for nonlinear hybrid stochastic delayed systems (NHSDSs) with measurement error. The DETM and intermittent control strategy are employed to alleviate the network burden in feedback and forward channels, respectively. After giving the existence and uniqueness theorem for the solution to NHSDSs, the sufficient condition to guarantee p-th moment exponential stability and almost surely exponential stability of NHSDSs is established by comparing with the constructed auxiliary system. On this basis, the intermittent control based on self-triggered mechanism is developed. In addition, it is also proved that neither the DETM nor the self-triggered mechanism can produce the Zeno phenomenon. Finally, a simulation example is given to verify the validity of the theoretical results. © 2025 Elsevier Ltd

关键词： Stabilization

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