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International Conference on Communications, Information System and Computer engineering (CISCE)

作者： Mengyuan Li Beijing Key Laboratory of Knowledge Automation for Industrial Process (University of Science and Technology Beijing) Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In response to the existing offline anomaly detection algorithms performing well on static data, but struggle to meet the requirements of accuracy and real-time processing when the data distribution changes. This paper proposes an online anomaly detection algorithm with concept drift adaptation to adapt to continuously changing data distributions. Firstly, the data is subjected to offline training using a Long Short-Term Memory autoencoder to obtain anomaly scores. And unsupervised anomaly detection is performed based on dynamic thresholds. Then, an adaptive sliding window is employed for concept drift detection, with the model being updated and adjusted through concept drift adaptation. Finally, the improved Particle Swarm Optimization algorithm is utilized to optimize the hyperparameters that contribute more to the accuracy. Results indicate that the proposed algorithm effectively adapts to evolving data distributions, meeting the real-time processing and accuracy requirements essential for anomaly detection.

关键词： Training Adaptation models Accuracy Heuristic algorithms Concept drift Prediction algorithms Real-time systems

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Metalenses phase characterization by multidistance phase retrieval

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Light(Science & Applications) 2024年第9期13卷 1898-1907页

作者： Bowen Liu Jialuo Cheng Maoxiong Zhao Jin Yao Xiaoyuan Liu Shaohu Chen Lei Shi Din Ping Tsai Zihan Geng Mu Ku Chen State Key Laboratory of Surface Physics Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education)and Department of PhysicsFudan University200433 ShanghaiChina Department of Electrical Engineering City University of Hong KongKowloonHong Kong SARChina State Key Laboratory of Terahertz and Millimeter Waves City University of Hong KongKowloonHong Kong SARChina Institute for Nanoelectronic Devices and Quantum Computing Fudan University200438 ShanghaiChina Collaborative Innovation Center of Advanced Microstructures Nanjing University210093 NanjingJiangsuChina Shanghai Research Center for Quantum Sciences 201315 ShanghaiChina Centre for Biosystems Neuroscienceand NanotechnologyCity University of Hong KongKowloonHong Kong SARChina Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua University518071 ShenzhenGuangdongChina

Metalens,characterized by their unique functions and distinctive physical properties,have gained significant attention for their potential *** further optimize the performance of metalens,it is necessary to characterize the phase modulation of the *** this study,we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and *** findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently *** system enables the analysis of focal length and aberration by utilizing the computed phase *** extend our investigation to measure the phase distribution of the metalens operating in the near-infrared(NIR)spectrum and identify the impact of defects in the sample on the ***,we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working *** system provides a straightforward method for the phase characterization of metalens,aiding in optimizing the metalens design and functionality.

关键词： phase system distribution

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Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression

arXiv

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arXiv 2024年

作者： Hu, Jiao Cui, Jiaxu Yang, Bo College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Discovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the mysterious patterns and mechanisms of the formation and evolution of complex phenomena in various fields and assist in decision-making. In this work, we develop a universal computational tool that can automatically, efficiently, and accurately learn the symbolic changing patterns of complex system states by combining the excellent fitting ability from deep learning and the equation inference ability from pre-trained symbolic regression. We conduct intensive experimental verifications on more than ten representative scenarios from physics, biochemistry, ecology, epidemiology, etc. Results demonstrate the outstanding effectiveness and efficiency of our tool by comparing with the state-of-the-art symbolic regression techniques for network dynamics. The application to real-world systems including global epidemic transmission and pedestrian movements has verified its practical applicability. We believe that our tool can serve as a universal solution to dispel the fog of hidden mechanisms of changes in complex phenomena, advance toward interpretability, and inspire more scientific discoveries. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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data-Driven Design of Fault Detection Approach for Dynamic Systems Based on Subspace Identification Method

Data-Driven Design of Fault Detection Approach for Dynamic S...

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data Driven Control and Learning Systems (DDCLS)

作者： Lyu Yating Gao Jingjing Yang Xu Huang Jian Cui Jiarui Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the context of continuous advancements in modern industry and the escalating demands for the safety and reliability of control systems, timely fault diagnosis of control systems becomes paramount. Driven by this imperative, this article devotes to data-driven design of fault detection systems based on subspace identification method. For this purpose, a fault detection approach based on Subspace State Space System Identification and model-based full-dimensional observer design is firstly designed. It is followed by a data-driven fault detection approach based on one-step identification of stable kernel representation and direct design of the residual generator. Finally, the effectiveness and performance of the two proposed approaches have been verified through a case study on the laboratory threetank system.

关键词： Fault diagnosis Analytical models Fault detection Observers Aerospace electronics Control systems Generators

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Production quality early warning method for firearms components based on cloud edge collaboration 4

Production quality early warning method for firearms compone...

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Sun, Jiawei Hao, Jia Zhang, Xiaoning School of Mechanical Engineering Beijing Institute of Technology Beijing10081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge and Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing10008 China Tianjin Key Laboratory of Space Environment Simulation Technology Tianjing300450 China

ISBN: (纸本)9798400709449

Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. The production process of firearms components is highly challenging and quality problems in the production process may directly affect the safety of people's lives and property. Thus, this paper proposes a production-quality early warning method for firearms components based on cloud-edge collaboration. Real-time early warning and rapid processing for abnormal conditions in the production process are realized based on cloud data analysis and edge intelligent prediction, thereby improving production efficiency and quality. The cloud edge collaboration framework is developed for cloud training of the long short-term memory models and real-time sample edge acquisition, enhancing the adaptability of quality early warning algorithms under specific conditions and the real-time quality of early warning for firearms components. © 2023 ACM.

关键词： Big data

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Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Xiaoting Wang, Juan Bian, Haodong Guo, Maozu Inner Mongolia University Ecological Big Data Engineering Research Center of the Ministry of Education School of Computer Science Hohhot China Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

ISBN: (纸本)9781665468190

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins. © 2022 IEEE.

关键词： Support vector machines

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A neural kernel framework for relation extraction

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Expert Systems with Applications 2025年 286卷

作者： Wang, Kai Chen, Yanping Yang, Weizhe Huang, Ruizhang Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University China State Key Laboratory of Public Big Data Guizhou University China

Relation extraction (RE) poses significant challenges due to the complexity of identifying semantic relationships between overlapping entity pairs within sentences. Traditional kernel-based methods effectively leverage instance-level similarities but heavily depend on manually designed kernel functions, requiring substantial domain expertise and limiting their flexibility. On the other hand, neural network approaches excel at automatically learning abstract feature representations but typically neglect critical instance-specific information, potentially missing valuable relational details during inference. These limitations motivate the exploration of a hybrid approach that effectively integrates the strengths of traditional kernels with the representational power of neural networks. In this paper, we propose a neural kernel framework designed to bridge this critical gap. The proposed framework employs neural kernels, whose parameters are initialized based on task-specific objectives and optimized through neural training procedures, enabling adaptive learning of similarity measures without relying on handcrafted kernel functions. By maintaining instance-level details through annotated training examples, neural kernels create discriminative, flexible decision boundaries tailored specifically to the relation extraction task. We further develop three complementary neural kernel components, instance kernel, description kernel, and cluster kernel, to show the advantages of kernel substitution. Extensive experiments on the ACE 2005, SemEval 2010, and CoNLL 2004 datasets demonstrate that our neural kernel framework significantly outperforms existing state-of-the-art methods, achieving F1-scores of 88.11 %, 91.08 %, and 98.63 %, respectively. © 2025 Elsevier Ltd

关键词： Natural language processing systems

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From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal data Reconstruction

arXiv

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arXiv 2024年

作者： Sun, Ziyu Su, Haoyang Wang, En Yang, Funing Yang, Yongjian Liu, Wenbin The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

With the rapid development of various sensing devices, spatiotemporal data is becoming increasingly important nowadays. However, due to sensing costs and privacy concerns, the collected data is often incomplete and coarse-grained, limiting its application to specific tasks. To address this, we propose a new task called spatiotemporal data reconstruction, which aims to infer complete and fine-grained data from sparse and coarse-grained observations. To achieve this, we introduce a two-stage data inference framework, DiffRecon, grounded in the Denoising Diffusion Probabilistic Model (DDPM). In the first stage, we present Diffusion-C, a diffusion model augmented by ST-PointFormer, a powerful encoder designed to leverage the spatial correlations between sparse data points. Following this, the second stage introduces Diffusion-F, which incorporates the proposed T-PatternNet to capture the temporal pattern within sequential data. Together, these two stages form an end-to-end framework capable of inferring complete, fine-grained data from incomplete and coarse-grained observations. We conducted experiments on multiple real-world datasets to demonstrate the superiority of our method. © 2024, CC BY.

关键词： Spatio-temporal data

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An Improved VAE-Based Quality Prediction Method with Application to a Hot Strip Rolling Mill Process

An Improved VAE-Based Quality Prediction Method with Applica...

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data Driven Control and Learning Systems (DDCLS)

作者： Yundan Liu Kai Zhang Xiaowen Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing PR China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing PR China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In a hot strip rolling mill (HSRM) process, the control of the crown quality of a strip steel is of paramount importance. In this paper, a distributed crown prediction model based on an improved Variational Autoencoder (VAE) framework is proposed. The model integrates a multi -layer VAE architecture to better accommodate the complex distribution of actual production data. Utilizing real production data from Ansteel's HSRM process, the model was subjected to extensive training and validation to optimize its predictive accuracy. Then in comparison with centralized VAE and VAE models with specialized prediction layers, the distributed model demonstrated exceptional predictive performance, with evaluation metrics including mean absolute error, root mean squared error and mean absolute percentage error. It can be seen from the application result that: the proposed method shows a better prediction result compared with the traditional methods.

关键词： Training Strips Technological innovation Accuracy Process control Production Predictive models

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Autonomous Human-Robot Collaborative Assembly Method Driven by the Fusion of Large Language Model and Digital Twin

Autonomous Human-Robot Collaborative Assembly Method Driven ...

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2024 International Conference on Intelligent Systems and Robotics, CISR 2024

作者： Chen, Jianpeng Luo, Haiwei Huang, Sihan Zhang, Meidi Wang, Guoxin Yan, Yan Jing, Shikai School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Capital Aerospace Machinery Corporation Limited Beijing100076 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China China North Artificial Intelligence & Innovation Research Institute Beijing100071 China

Human-robot collaboration (HRC) plays an important role in human-centric manufacturing, which requires cooperative robots to have the ability of collaborate with human autonomously. It is very complex to understand the intention of human during the assembly process, therefore, we proposed a method of autonomous HRC assembly driven by the fusion of large language model (LLM) and digital twin in this paper. The assembly state is recognized from two perspectives, including the perception of key parts based on transfer learning and YOLO, and perceive operator actions based on LSTM and attention mechanism. In order to improve the autonomy of HRC, a collaborative task decision method driven by fine-tuning LLM based on assembly domain knowledge is proposed. A case study of reducer assembly is presented to verify the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative robots

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