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A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control systems

IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 584-602页

作者： Boqian Li Shiping Wen Zheng Yan Guanghui Wen Tingwen Huang Australian AI Institute Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China Science Program Texas A&M University at QatarDoha 23874Qatar IEEE

This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control *** problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)*** CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control *** objectives imply important properties including controllability,convergence,and robustness of control *** this framework,optimal control corresponds to the minimal solution to a constrained QP *** uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of *** recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this ***,we provide research directions that are significant for the advance of knowledge in this area.

关键词： Control barrier function(CBF) control Lyapunov function(CLF) nonlinear-affine control systems

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Business Process Remaining Time Prediction Based on Incremental Event Logs

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IEEE Transactions on Services Computing 2025年第3期18卷 1308-1320页

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： Predictive Models Feature Extraction Business Adaptation Models Accuracy Long Short Term Memory Transformers Process Control Computational Modeling Process Monitoring Predictive Process Monitoring Remaining Time Prediction Process Mining Incremental Event Log Business Processes Event Log Prediction Model Prediction Accuracy Dynamic Changes Effects Of Characteristics Long Short Term Memory Static Model Prediction Framework Continuous Updating Incremental Process Reconstruction Scheme Neural Network Training Set Convolutional Neural Network Change Process Hidden Layer Combination Of Features Recurrent Neural Network Flow Control Neural Units Continuous Bag Of Words Mean Absolute Error Values Concept Drift Improve Prediction Accuracy Construction Of Prediction Model Dynamic Nature Of Processes Real Life Scenarios Prediction Task Incremental Learning

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Construction Method of Resilience Killing Chain Based on Two-Layer Optimization Model 1

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10th China Conference on Command and Control, C2 2022

作者： Zhang, Lu Zhu, Xianqiang Xiao, Kaiming Zhang, Weiming Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha410073 China

ISBN: (数字)9789811960529

ISBN: (纸本)9789811960512

Killing-chain is the core mode of energy accumulation and release of the system. Under the concept of multi domain cooperative operation, the structure mode of killing-chain has been converted from one-way superposition and series chain structure to complex and multi-dimensional network structure. In order to efficiently plan the killing-chain, it is necessary to optimize the combination mode of killing chain based on two-layer optimization. Firstly, a networked description method of combat network is introduced based on heterogeneous network model. Secondly, the optimization model is established based on the two-layer optimization method, and two novel evaluation metrics: effectiveness index and robustness index, are proposed. Finally, we compare the proposed optimization model with the stochastic optimization method through a simulation case study and verify the superiority of the two-layer optimization model. © 2022, Chinese Institute of Command and Control.

关键词： Optimization

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Structure evolution andτ_(f)influence mechanism of Bi_(1-x)Ho_(x)VO_(4)microwave dielectric ceramics for LTCC applications

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Journal of Materials science & technology 2024年第30期197卷 1-8页

作者： Huaicheng Xiang Yuheng Zhang Junqi Chen Yang Zhou Ying Tang Jinwu Chen Liang Fang Key Laboratory of Low-Dimensional Structural Physics and Application Education Department of Guangxi Zhuang Autonomous RegionCollege of Physics and Electronic Information EngineeringGuilin University of TechnologyGuilin541004China Guangxi Universities Key Laboratory of Non-Ferrous Metal Oxide Electronic Functional Materials and Devices Guangxi Key Laboratory of Optical and Electronic Materials and DevicesCollege of Materials Science and EngineeringGuilin University of TechnologyGuilin541004China School of Mechanical Engineering Guilin University of Aerospace TechnologyGuilin541004China

Bi_(1-x)Ho_(x)VO_(4)(0.1≤x≤0.9)ceramics were prepared via a solid-state reaction method,and all the ceram-ics could be well densified in the 920–980℃*** ceramics with 0.1≤x<0.4 were composed of both monoclinic scheelite(M)and tetragonal zircon(T)phases,and a single M phase could be obtained in the range of x≥*** measuredε_(r) decreased from 58.9(x=0.1)to 14.7(x=0.9),so do the calcu-lated values(ε_(r)(C−M)=34.3–12.1),and the main reason forε_(r)>ε_(r)(C−M)was the rattling of Ho^(3+)in the *** points with zeroτf appeared in Bi1–x Hox VO4(0≤x≤1)ceramics,and the best mi-crowave dielectric properties withε_(r)=16.6,Q×f=18,400 GHz(f=10.69 GHz),andτf=+3.29 ppm/℃were obtained in the Bi_(0.2)Ho_(0.8)VO_(4)*** change in temperature coefficient of ionic polarizability(τ_(αm))caused by the rattling effect of cations is the physical essence that affectsτ***,the rattling effect can be used as an effective mechanism to regulateτf in low-ε_(r) ***,there was no chemical reaction between Bi_(1-x)Ho_(x)VO_(4) and Ag electrode,which indicates potential applications in low-temperature co-fired ceramic(LTCC)technology.

关键词： Bi_(1–x)Ho_(x)VO_(4) Near-zeroτ_(f) Bond valence Rattling effect LTCC

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On the Performance of NOMA Over Coded Caching in Cache-Aided Wireless Network 8

On the Performance of NOMA Over Coded Caching in Cache-Aided...

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8th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2023

作者： Ma, Yuanyuan Zhi, Ruxin Xu, Zhan Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Beijing100101 China Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing100101 China

ISBN: (纸本)9798350317923

This paper centers around the examination of a downlink system comprising of a base station (BS) located at the center of a circular cell. Within this cell, we find cache-equipped users uniformly distributed. Our aim is to explore an innovative approach to enhance spectral efficiency by dividing the users into two groups, effectively utilizing the principles of nonorthogonal multiple access (NOMA) and coded caching. Introducing the NOMA over coded caching scheme, we propose a novel method to elevate the service rate. We derive closedform expressions to accurately quantify the performance of NOMA over coded caching in terms of the service rate. Simulation experiments are conducted to validate the superiority of our proposed algorithm. The results demonstrate the remarkable efficacy of the NOMA over coded caching schemes in significantly improving the service rate, surpassing the conventional coded caching and non-encoding caching schemes. © 2023 IEEE.

关键词： Spectrum efficiency

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Developing deep learning-based large-scale organic reaction classification model via sigma-profiles

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Green Chemical engineering 2025年第2期6卷 181-192页

作者： Wenlong Wang Chenyang Xu Jian Du Lei Zhang State Key Laboratory of Fine Chemical Frontiers Science Center for Smart Materials Oriented Chemical EngineeringInstitute of Chemical Process Systems EngineeringSchool of Chemical EngineeringDalian University of TechnologyDalian116024China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore117585Singapore

Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions,especially in the field of organic *** hundreds of thousands of reactions available for reference,one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics,which makes target-guided navigation in the vast chemical space *** previous attempts that apply deep learning to reaction classification tasks have made substantial progress,developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open *** this work,a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and ***,the model is trained under the open-source dataset USPTO_TPL which contains recorded reactions of up to 1000 different *** multi-classification accuracy of the model on the testing dataset is 99.382%,showing its great potential for practical ***,a reaction similarity map is presented to correlate the reactions in the USPTO_TPL dataset based on their sigmaprofile-based statistical ***,representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.

关键词： Reaction classification Reaction fingerprint Sigma-profiles Deep learning

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Deep Learning-Based Modulation Recognition for Imbalanced Classification 4

Deep Learning-Based Modulation Recognition for Imbalanced Cl...

引用

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

作者： Luo, Runqing Sun, Jian Guo, Yanan Beijing Information Science and Technology University 100101 China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9798400709449

Automatic modulation recognition (AMR) is a crucial task in intelligent receiver systems, with significant applications in both civilian and military scenarios. Deep learning-based modulation recognition algorithms have achieved high recognition accuracy and good robustness. However, the performance of DL-AMR heavily relies on a large amount of training data. When the training dataset has a limited number of samples or an imbalanced distribution across different classes, the accuracy of automatic modulation recognition for minority class samples may significantly decrease. Therefore, a CNN model with a focal loss as the output layer's loss function is proposed to address the issue of decreased recognition accuracy for minority classes in the deep learning model when faced with imbalanced training data. Simulation results demonstrate that this model improves the recognition accuracy of classes with a limited number of samples. © 2023 ACM.

关键词： Deep learning

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Distributed Spatially Non-Stationary Channel Estimation for Extremely-Large Antenna systems

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent systems 2024年第3期11卷 1-8页

作者： Xu, Yanqing Wang, Shuai Jiang, Ruihong Wang, Zhou School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Integrated Circuits Tsinghua University Beijing100084 China

The purpose of this paper is to develop a distributed channel estimation (CE) algorithm for spatially non-stationary (SNS) channels in extremely large aperture array systems, addressing the issues of high communication cost and computational complexity associated with traditional centralized algorithms. However, SNS channels differ from conventional spatially stationary channels, presenting new challenges such as varying sparsity patterns for different antennas. To overcome these challenges, we propose a novel distributed CE algorithm accompanied by a simple-yet-effective hard thresholding scheme. The proposed algorithm is not only suitable for uniform antenna arrays but also for irregularly deployed antennas. Simulation results demonstrate the advantages of the the proposed algorithm in terms of estimation accuracy, communication cost and computational complexity. © (2024) Xu et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Channel estimation

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Partial label learning via label-specific features enhancement and semi-disambiguation

Partial label learning via label-specific features enhanceme...

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2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Hu, Jinhua Chai, Jing Sun, Xianran School of information Science and Engineering Yunnan University Kunming650504 China Yunnan Key Laboratory of Intelligent Systems and Computing China

ISBN: (纸本)9781510682597

Partial Label Learning (PLL) represents a common multi-class weakly supervised learning challenge, where each instance is linked with a set of possible labels, only one of which is the ground-truth label, and it remains unknown during the training. A common strategy for solving this multi-class problems is to decompose it into a series of binary classification problems based on certain coding rules (e.g., OvO, ECOC), and then process each binary classification problem separately using the original features. To address the deficiency of discriminative information in the original features, a feature enhancement mechanism is proposed to introduce label-specific features to improve the discriminative performance of features. Firstly, label-specific features are generated for each decomposed binary classification problem, and then fused with the original features. The fused features are used to design corresponding binary classifiers. Additionally, due to the presence of a large number of false positive labels in the training examples, a semi-disambiguation strategy can be employed to remove some false positive labels, thereby "slimming down" the candidate label set as a preprocessing step to reduce its ambiguity. The proposed partial label learning algorithm based on features enhancement and semi-disambiguation proves its effectiveness on synthetic and real-world data sets through extensive experiments. © 2024 SPIE.

关键词： Contrastive Learning

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Progress on the antiferromagnetic topological insulator MnBi2Te4

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National science Review 2024年第2期11卷 33-49页

作者： Shuai Li Tianyu Liu Chang Liu Yayu Wang Hai-Zhou Lu X.C.Xie Department of Physics Harbin Institute of Technology Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology(SUSTech) Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area(Guangdong) Shenzhen Key Laboratory of Quantum Science and Engineering International Quantum Academy Beijing Academy of Quantum Information Sciences Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of Physics Renmin University of China State Key Laboratory of Low Dimensional Quantum Physics Department of PhysicsTsinghua University Frontier Science Center for Quantum Information Hefei National Laboratory International Center for Quantum Materials School of Physics Peking University Institute for Nanoelectronic Devices and Quantum Computing Fudan University

Topological materials, which feature robust surface and/or edge states, have now been a research focus in condensed maTher physics. They represent a new class of materials exhibiting nontrivial topological phases,and provide a platform for exploring exotic transport phenomena, such as the quantum anomalous Hall effect and the quantum spin Hall effect. Recently, magnetic topological materials have attracted considerable interests due to the possibility to study the interplay between topological and magnetic *** particular, the quantum anomalous Hall and axion insulator phases can be realized in topological insulators with magnetic order. MnBi2Te4, as the first intrinsic antiferromagnetic topological insulator discovered, allows the examination of existing theoretical predictions; it has been extensively studied, and many new discoveries have been made. Here we review the progress made on MnBi2Te4from both experimental and theoretical aspects. The bulk crystal and magnetic structures are surveyed first, followed by a review of theoretical calculations and experimental probes on the band structure and surface states, and a discussion of various exotic phases that can be realized in MnBi2Te4. The properties of MnBi2Te4thin films and the corresponding transport studies are then reviewed, with an emphasis on the edge state transport. Possible future research directions in this field are also discussed.

关键词： MnBi2Te4 magnetic topological insulator antiferromagnetic quantum anomalous Hall effect axion insulator

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