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Pager Explosion:Cybersecurity Insights and Afterthoughts

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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Digital CEOs in Digital Enterprises:Automating,Augmenting,and Parallel in Metaverse/CPSS/TAOs

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 820-823页

作者： Juanjuan Li Rui Qin Sangtian Guan Xiao Xue Peng Zhu Fei-Yue Wang with State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Economics and Management Nanjing University of Science and TechnologyNanjing 210094China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan... 详细信息

关键词： exceptional Auto advancing

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Chlorine-induced mixed valence of CuO_(x)/C to promote the electroreduction of carbon dioxide to ethylene

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Nano Research 2023年第7期16卷 8827-8835页

作者： Xin Wang Ming Miao Bowen Tang Haotian Duan Fulong Zhu Huigang Zhang Xian Zhang Wen-jin Yin Yongzhu Fu College of Chemistry Zhengzhou UniversityZhengzhou 450001China School of Physics and Electronic Science Hunan University of Science and TechnologyXiangtan 411201China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Qian Xuesen Laboratory of Space Technology China Academy of Space TechnologyBeijing 100094China

Electrochemical conversion of CO_(2)(CO_(2)RR)into high-value fuel is identified as one of the promising approaches to achieve carbon *** synthesis of high-efficiency CO_(2)reduction electrocatalysts with high C_(2):C_(1) selectivity remains a field of intense *** studies have shown that the presence of Cu(I)is beneficial for the reduction of CO_(2)into C_(2)***,the stable presence of Cu(I)remains controversial,especially in the negative potential *** we report a simple and easily scalable catalyst precursor Cu_(2)(OH)_(3)Cl/C,which automatically forms in-situ chlorine-doped Cu/Cu_(2)O heterointerface during *** catalyst not only exhibits a Faradaic efficiency of 33.03%but also provides a long-term stability of Cu^(+),gaining a stable electrolysis of 11 h,with an ethylene/methane ratio over *** experimental results and mechanistic studies confirm that the presence of Cl^(-)inhibits the reduction of Cu^(+),inducing the formation of Cu^(0)/Cu^(+),and reduces the reaction energy of the intermediate ^(*)CO dimerization,thereby facilitating the formation of C_(2)*** work provides a feasible way to synthesize copper ions with long-term and stable positive charge in CO_(2)RR and expands a new way to synthesize ethylene industrial products in the future.

关键词： basic copper chloride chlorine dopant mixed-phase catalysts CO_(2)reduction reaction ethylene

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Cluster Analysis Using Explainable AI for Confused Learners

Cluster Analysis Using Explainable AI for Confused Learners

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2023 IEEE Asia-Pacific Conference on Computer science and Data Engineering, CSDE 2023

作者： Samani, Chaitali Musial, Katarzyna School of Computer Science University of Technology Sydney Sydney Australia School of Computer Science University of Technology Sydney Complex Adaptive Systems Lab Sydney Australia

ISBN: (纸本)9798350341072

The process of learning is multifaceted and intricate, with confusion playing a pivotal role in engagement and motivation. Confusion arises from a disequilibrium that a learner may have due to some knowledge misconceptions when performing cognitive tasks. While some form of confusion can lead to better achievements in such tasks, prolonged confusion causes frustration and disengagement. So, to maintain a balance between less confused to highly confused states is highly crucial. In this study we propose explainable clustering technique for confused learners. In this study we first dig deeper into the set of confused learners and form clusters using K-Means clustering. K-Means clustering provides the clusters like a black-box technique is since the purpose of this clustering was to better understand the confused learners, we extended the clustering with tree-based approach that explains why a particular learner was placed in a particular cluster. This explainability provides an opportunity to design better intervention strategies to control confusion in learners © 2023 IEEE.

关键词： K-means clustering

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Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

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Frontiers of Mechanical Engineering 2023年第3期18卷 123-143页

作者： Yiming YAN Shuting WANG Yuanlong XIE Hao WU Shiqi ZHENG Hu LI School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China

To achieve the collision-free trajectory tracking of the four-wheeled mobile robot(FMR),existing methods resolve the tracking control and obstacle avoidance *** the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is *** address this challenge,this paper proposes an obstacle-circumventing adaptive control(OCAC)***,a novel anti-disturbance terminal slide mode control with adaptive gains is formulated,incorporating specified control laws for different *** formulation guarantees rapid convergence and simultaneous chattering *** introducing sub-target points,a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one,thereby reducing the burden of local path searching while ensuring system stability during obstacle *** experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.

关键词： four-wheeled mobile robot obstacle-circumventing adaptive control adaptive anti-disturbance terminal sliding mode control sub-target dynamic tracking regression obstacle avoidance

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The Metaverse of Mind:Perspectives on DeSci for DeEco and DeSoc

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2043-2046页

作者： Fei-Yue Wang IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

First of all, I would like to take this opportunity to express my sincere and deep thanks to our Editor-in-Chief, Professor Meng Chu Zhou, who took over my position after I was drafted for rejuvenating IEEE Transactions on Computational Social systems in 2017. During the past five years, Meng Chu’s professional leadership and dedication has transformed IEEE/CAA Journal of Automatica Sinica(JAS) from its infancy to a young and high-impact publication in the world that is full of vitality and actively engaged by a group of talented and charged associate Ei Cs and editors, which is clearly demonstrated in Meng Chu’s farewell editorial [1]. I am very glad that Professor Qing-Long Han, an influential and leading scientist of the world-class in AI, control, automation, and intelligent science and technology from Australia, as well as a staunch supporter and great leader of this journal from its beginning, will take over the Ei C torch from Meng Chu next year, since I am extremely confident that our journal will reach a new high for its service and quality under his new leadership.

关键词： journal leadership Australia

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Online Machine Learning for Real-Time Cell Culture Process Monitoring 37th

Online Machine Learning for Real-Time Cell Culture Process ...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Khuat, Thanh Tung Bassett, Robert Otte, Ellen Gabrys, Bogdan Complex Adaptive Systems Laboratory The Data Science Institute University of Technology Sydney UltimoNSW2007 Australia CSL Innovation MelbourneVIC3000 Australia

ISBN: (纸本)9789819603503

In the biopharmaceutical industry, the production of monoclonal antibodies (mAbs) relies heavily on the optimization and real-time monitoring of cell culture processes. This study assesses the effectiveness of online machine learning (OML) models for the real-time monitoring of nutrient and metabolite concentrations, such as glucose, lactate, and ammonium, during the cell cultivation process using Raman spectroscopy as input data. The research addresses the challenge of limited data inherent in cell culture processes, particularly when transferring learned information to new bioreactor runs with different cell lines and media compositions. Traditional pretrained machine learning models often fail to generalize their learned information in such context. Our research demonstrates that OML models can dynamically adapt to the evolving conditions of cell culture processes, providing accurate and timely updates on critical nutrient and metabolite concentrations. We conducted a series of experiments comparing the performance of OML models against pretrained models in monitoring cell culture processes. The findings indicate that OML models exhibit superior performance in capturing the dynamic changes of nutrient and metabolite concentrations during cell cultivation. In contrast, pretrained models exhibit substantial performance degradation when applied to a completely new bioreactor run, highlighting the limitations of static learning approaches in handling the complex, dynamic nature of cell cultures. This study emphasizes the potential of OML in enhancing the robustness and efficiency of real-time process monitoring in mAb production, paving the way for more adaptive and resilient biomanufacturing processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Metabolites

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Tipping Point Detection Using Reservoir Computing

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Research 2024年第1期2023卷 779-790页

作者： Xin Li Qunxi Zhu Chengli Zhao Xuzhe Qian Xue Zhang Xiaojun Duan Wei Lin College of Science National University of Defense TechnologyChangshaHunan 410073China Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Shanghai Artificial Intelligence Laboratory Shanghai 200232China School of Mathematical Sciences SCMSSCAMand CCSBFudan UniversityShanghai 200433China

Detection in high fidelity of tipping points,the emergence of which is often induced by invisible changes in internal structures or/and external interferences,is paramountly beneficial to understanding and predicting complex dynamical systems(CDSs).Detection approaches,which have been fruitfully developed from several perspectives(e.g.,statistics,dynamics,and machine learning),have their own advantages but still encounter difficulties in the face of high-dimensional,fluctuating ***,using the reservoir computing(RC),a recently notable,resource-conserving machine learning method for reconstructing and predicting CDSs,we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs.

关键词： computing record ping

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Mask-guided Multi-scale Spatial-Spectral Transformer for Snapshot Compressive Imaging

Mask-guided Multi-scale Spatial-Spectral Transformer for Sna...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yin, Heyuan Yang, Jingxiang Liu, Jia Xiao, Liang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems Ministry of Industry and Information Technology China

ISBN: (纸本)9798350368741

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST. © 2025 IEEE.

关键词： hyperspectral reconstruction Snapshot Compressive Imaging spatial-spectral transformer

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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