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Optimal control for spatial-temporal distributed nonlinear autoregressive with exogenous inputs correlation model of steel rolling reheating furnace: a coordinated time-sharing control approach

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Control Theory and Technology 2023年第2期21卷 190-211页

作者： Qingfeng Bao Sen Zhang Jin Guo Zhiqiang Li Zhenquan Zhang School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China Hesteel Group Company Limited Shijiazhuang 050031HebeiChina

Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distributed-nonlinear autoregressive with exogenous inputs correlation model(STD-NARXCM)to spatial temporal distributed-autoregressive with exogenous inputs correlation model(STD-ARXCM)in working point is ***,a new coordinated time-sharing control architecture in different time periods is proposed,which is along the length of the SRRF to improve the control ***,a hybrid control algorithm of expert-fuzzy is proposed to improve the dynamic of the temperature and the heating rate during time period 0 to t_(1).A hybrid control algorithm of expert-fuzzy-PID is proposed to enhance the control accuracy and the heating rate during time period t_(1) to t_(2).A hybrid control algorithm of expert-active disturbance rejection control(ADRC)is proposed to boost the anti-interference and the heating rate during time period t_(2) to t_(3).Finally,the experimental results show that the coordinated time-sharing algorithm can meet the process requirements,the maximum deviation of temperature value is 8-13.5℃.

关键词： Steel rolling reheating furnace Spatial-temporal distribution Coordinated time-sharing control Controller structure in different periods Hybrid control algorithm

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dTEE: A Declarative Approach to Secure IoT Applications Using TrustZone 23

dTEE: A Declarative Approach to Secure IoT Applications Usin...

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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Sun, Tong Li, Borui Teng, Yixiao Gao, Yi Dong, Wei Zhejiang University The State Key Laboratory of Blockchain and Data Security College of Computer Science China Southeast University School of Computer Science and Engineering China

ISBN: (纸本)9798350362015

Internet of Things (IoT) applications have recently been widely used in safety-critical scenarios. To prevent sensitive information leaks, IoT device vendors provide hardware-assisted protections, called Trusted Execution Environments (TEEs), like ARM Trust-Zone. Programming a TEE-based application requires separate code for two components, significantly slowing down the development process. Existing solutions tackle this issue by automatic code partition while not successfully applying it in two complicated scenarios: adding trusted logic and interactions with secure *** propose dTEE, a declarative approach to secure IoT applications based on TrustZone. dTEE proposes a rapid approach that enables developers to declare tiered-sensitive variables and functions of existing applications. Besides, dTEE automatically transforms device drivers into trusted ones. We evaluate dTEE on four real-world IoT applications and seven micro-benchmarks. Results show that dTEE achieves high expressiveness for supporting 50% more applications than existing approaches and reduces 90% of the lines of code against handcrafted development. © 2024 IEEE.

关键词： Internet of things

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All-fiber and self-starting Yb-doped Mamyshev oscillator based on nonlinear polarization evolution

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Chinese Optics Letters 2024年第12期22卷 74-79页

作者： Zecong Li Kai Wang Zinan Huang Qianqian Huang YanlüLin Weixi Li Zhichao Luo Chengbo Mou Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data ScienceJoint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai UniversityShanghai 200444China Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Laboratory of Nanophotonic Functional Materials and Devices School of Information and Optoelectronic Science and EngineeringSouth China Normal UniversityGuangzhou 510006China

We experimentally demonstrate a Yb-doped all-fiber mode-locked laser based on the Mamyshev *** entire experimental setup operates only by injecting pump powers and adjusting polarization controllers(PCs),which realizes *** types of pulse patterns are observed at different pump powers and polarization states,including single pulses and up to eight-pulse bound-state *** operating wavelength of single-pulse mode-locking switching between 1072.3and 1043.1 nm can be realized by increasing the pump power while keeping the PCs in a fixed *** design can provide an attractive experimental model for all-fiber and self-starting Mamyshev oscillators.

关键词： Mamyshev oscillator Yb-doped fiber laser pulse dynamic pattern nonlinear polarization evolution

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TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing 38

TreeVI: Reparameterizable Tree-structured Variational Infere...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xiao, Junxi Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Mean-field variational inference (VI) is computationally scalable, but its highly-demanding independence requirement hinders it from being applied to wider scenarios. Although many VI methods that take correlation into account have been proposed, these methods generally are not scalable enough to capture the correlation among data instances, which often arises in applications involving graphs or explicit constraints among instances. In this paper, we developed the Tree-structured Variational Inference (TreeVI)2, which uses a tree to capture the correlation among latent variables in the posterior. We show that samples from the tree-structured posterior can be reparameterized efficiently and parallelly, making its training cost just 2 or 3 times that of VI under the mean-field assumption. To capture correlation with more complicated structure, the TreeVI is further extended to the multiple-tree case. Furthermore, we show that the underlying tree structure can be automatically learned from training data. With experiments on synthetic datasets, constrained clustering, user matching and link prediction, we demonstrate the efficacy of TreeVI in capturing instance-level correlation in posteriors and enhancing the performance of downstream applications. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-modal Fine-grained Retrieval with Local and Global Cross-Attention 14

Multi-modal Fine-grained Retrieval with Local and Global Cro...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Chen, Qiaosong Zhang, Ye Liu, Junzhuo Wang, Zhixiang Deng, Xin Wang, Jin Chongqing University of Posts and Telecommunications Key Laboratory of Data Engineering and Visual Computing Chongqing400065 China Maastricht Netherlands

ISBN: (纸本)9798350335385

The goal of cross-modal retrieval is that the user gives any sample as a query sample, and the system retrieves and feeds back various modal samples related to the query sample. At present, the cross-modal retrieval method mainly focuses on coarse-grained, which is far from being satisfied in practical application. However, there are many difficulties in fine-grained retrieval, such as the heterogeneous gap and semantic gap between multi-modal data, the difficulty of similarity measurement, and the small difference in fine-grained sample features. To overcome these limitations, we propose a novel multi-modal fine-grained retrieval method with the LAGC-Attention module, which can fully extract and fuse feature information from different modalities and represent them in a common space. Specifically, we use local and global cross self-attention to extract the neighboring and global context information for each single modal data, which greatly enhances the feature representation capability of each modality (image, text, audio, video), and especially reduce the gap between different feature distributions. Finally, Extensive experiments and ablation studies demonstrate that our method achieves state-of-the-art on the public dataset PKU FG-XMedia. © 2023 IEEE.

关键词： Semantics

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Optimization of YOLOv8 model based on pruning 4

Optimization of YOLOv8 model based on pruning

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Zhang, Dengji Hong, Sunyan Gao, Weiwei Yu, Jing Chen, Zhenting Shen, Xutian Wu, Jianxiong School of Information Engineering Kunming University Kunming650214 China Key Laboratory of Data Governance and Intelligent Decision in Universities of Yunnan Kunming650214 China

ISBN: (纸本)9781510686106

YOLOv8 is one of the most commonly used object detection algorithms. However, its network model has a large number of parameters, resulting in slow performance on embedded devices. One of the challenges in industrial applications of this algorithm is reducing the parameter size of the YOLOv8 model without significantly compromising its detection accuracy, thus enabling it to run efficiently on embedded devices. To address this, a structured pruning strategy based on Torch-Pruning was designed specifically for medium-sized YOLOv8 models like *** this study, the model was trained on the COCO dataset, resulting in a computational workload of 39.6G and a parameter count of 25.9M. Thirteen pruning iterations were conducted with different pruning rates to systematically reduce the model's parameter count and identify the optimal pruned model. Comparative analysis with the unpruned model showed promising results: the computational workload decreased from 39.6G to 33.7G, a reduction of 14.9%;the parameter count decreased from 25.9M to 22.0M, a reduction of 15%;the average precision improved from 0.6 before pruning to 0.7 after fine-tuning the pruned model parameters;and the inference time per image decreased from 10.2ms before pruning to 9.5ms afterward. © 2024 SPIE.

关键词： Optimal detection

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Phase spectrum based automatic ship detection in synthetic aperture radar images

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Journal of Ocean engineering and Science 2021年第2期6卷 185-195页

作者： Miaohui Zhang Baojun Qiao Ming Xin Bo Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina Institute of Data and Knowledge Engineering Henan UniversityKaifengChina School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

关键词： Ship detection saliency detection phase spectrum sea-land segmentation

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Machine learning empowered UAV-based beamforming design in ISAC systems

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Science China(Information Sciences) 2025年第5期68卷 285-286页

作者： Xiangyang DUAN Xiaoqi ZHANG Shuqiang XIA Zhongbin WANG Yihua MA Weijie YUAN State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless and Computing Research InstituteZTE Corporation School of Electrical and Data Engineering University of Technology Sydney School of Automation and Intelligent Manufacturing Southern University of Science and Technology

Integrated sensing and communication (ISAC) has been proposed as an enabling technology for the realization of the next-generation wireless system,which focuses on performing wireless communication and sensing *** the various potential ISAC-based applications,unmanned aerial vehicle (UAV)-based ISAC plays a significant part in unlocking the potential of future next-generation wireless communication,facilitating low-latency data transmission in high-mobility *** by recent advancements,a variety of effective techniques have been investigated to optimize beamforming design in ISAC *** instance,the authors in [1] introduced an extended Kalman filtering (EKF)-based method tailored for millimeter wave (mmWave) ISAC ***,Ref.[2]proposed an extended interacting multiple model (IMM)-EKF framework designed for vehicular networks with intricate roadway *** these advancements,the aforementioned methods typically employ a separate scheme for channel prediction and beam alignment,which introduces additional signaling overhead in real-world ***,there is a demand for an end-to-end beamforming design approach specifically for UAV-based ISAC systems.

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Evaluating the Impact of dataset Size on Univariate Prediction Techniques for Moroccan Agriculture

Evaluating the Impact of Dataset Size on Univariate Predicti...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Ed-daoudi, Rachid Alaoui, Altaf Zerouaoui, Jad Ettaki, Badia Zerouaoui, Jamal Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and context of prediction. This means that what makes a dataset to be considered as being big or small is controversial. In this paper, the ability of the predictive model to adapt to a particular size of data in training is examined. The study experiments on three different sizes of Moroccan agricultural data using a variety of statistical and Machine Learning techniques, to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The output of each model is measured using the Mean Absolute Error (MAE) and r-squared, and comparisons are made. The results of training the models through the three partitioned dataset show that, the models trained with the smallest and largest size of training data appear to be less accurate, while the models trained with a medium sized dataset delivers a much better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Generative Adversarial Nets Model Based on Spatio-Temporal Graph Attention Network for Multi-Step Prediction

Generative Adversarial Nets Model Based on Spatio-Temporal G...

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2023 China Automation Congress, CAC 2023

作者： Wang, Ling Wang, Rongxian Jia, Gaofeng School of Automation and Electrical Engineering Shunde Innovation School University of Science and Technology Beijing Key Laboratory of Knowledge Automation of Industrial Process of Ministry of Education Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation of Industrial Process of Ministry of Education Beijing China

ISBN: (纸本)9798350303759

The precise forecasting of traffic data holds significant importance for numerous practical applications in the real world. The challenge lies in the nonlinear spatio-temporal dependencies of the data, as well as rapid error accumulations for multi-step predictions. Therefore, we propose a Generative Adversarial Nets model based on a Spatio-Temporal Graph Attention Network (STGAT-GAN). Utilizing spatio-temporal graph attention proves to be an effective method for capturing the spatio-temporal dependencies, while GANs can learn a rich distribution over spatio-temporal data implicitly to produce realistic data adversarially. Experiments are carried out on two public traffic datasets. Comparative experimental analysis reveals that the proposed STGAT-GAN has better performance. © 2023 IEEE.

关键词： Generative adversarial networks

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