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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： complex networks

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： complex networks

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Effective Throughput Optimization of SAG Milling Process Based on BPNN and Genetic Algorithm

Effective Throughput Optimization of SAG Milling Process Bas...

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Industrial Cyber-Physical Systems (ICPS)

作者： Zhenhong Liao Ce Xu Wen Chen Qifu Chen Feng Wang Jinhua She 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 Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Chagnsha China Hunan Valin Lianyuan Iron And Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

Grinding is an energy-consuming process in mineral processing industry. Improving grinding processing capacity per unit power consumption is an effective means to reduce grinding production cost. In this paper, a new index for evaluating the effective processing throughput of SAG milling is proposed. The production process model is established by BP neural network (BPNN). Through combining the process mechanism and production constraints, the genetic algorithm is adopted to optimize the operating parameters of the SAG milling process to maximize the effective throughput, thus improving the grinding efficiency. The experimental results showed that through optimization of effective throughput function proposed in this paper, the SAG mill processing capacity has been increased by 4% and the operating power drawn reduced by 1.12%. It has important guiding significance for the actual production process.

关键词： Industries Power demand Neural networks Milling Throughput Minerals Materials processing

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Pressure-tunable large anomalous Hall effect of the ferromagnetic kagome-lattice Weyl semimetal Co3Sn2S2

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Physical Review B 2019年第16期100卷 165145-165145页

作者： Xuliang Chen Maoyuan Wang Chuanchuan Gu Shuyang Wang Yonghui Zhou Chao An Ying Zhou Bowen Zhang Chunhua Chen Yifang Yuan Mengyao Qi Lili Zhang Haidong Zhou Jianhui Zhou Yugui Yao Zhaorong Yang Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory Chinese Academy of Sciences Hefei 230031 China Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing 100081 China Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Physical Sciences University of Science and Technology of China Hefei 230026 China Institutes of Physical Science and Information Technology Anhui University Hefei 230601 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201204 China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201204 China Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996-1200 USA Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education) School of Physics and Astronomy Shanghai JiaoTong University Shanghai 200240 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China

We report a systematic high-pressure study of magnetic topological semimetal Co3Sn2S2 through measurements of synchrotron x-ray diffraction (XRD), magnetization, electrical, and Hall transports combined with first-principle theoretical calculations. No evident trace of structural phase transition is detected through synchrotron x-ray diffraction over the measured pressure range of 0.2–50.9 GPa. We find that the ferromagnetism and the anomalous Hall resistivity are monotonically suppressed as increasing pressure and almost vanish around 22 GPa. The anomalous Hall conductivity varies nonmonotonically against pressure at low temperatures, involving competition between original and emergent Weyl nodes. Combined with first-principle calculations, we reveal that the intrinsic mechanism due to the Berry curvature dominates the anomalous Hall effect under high pressure.

关键词： Anomalous Hall effect Pressure effects Transport phenomena Topological materials First-principles calculations X-ray powder diffraction

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Cubic singularities in binary linear electromechanical oscillators

arXiv

引用

arXiv 2023年

作者： Zhou, Xin Jing, Hui Ren, Xingjing Zhang, Jianqi Huang, Ran Li, Zhipeng Sun, Xiaopeng Wu, Xuezhong Qiu, Cheng-Wei Nori, Franco Xiao, Dingbang College of Intelligence Science and Technology NUDT Changsha410073 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Innovation Academy of Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan430071 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wako-shi Saitama351-0198 Japan Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore

Singularities arise in diverse disciplines and play a key role in both exploring fundamental laws of physics and making highly-sensitive sensors [1–14]. Higher-order (≥ 3) singularities, with further improved performance [15–19], however, usually require exquisite tuning of multiple (≥ 3) coupled degrees of freedom [15–17] or nonlinear control [18, 19], thus severely limiting their applications in practice. Here we propose theoretically and confirm using mechanics experiments that, cubic singularities can be realized in a coupled binary system without any nonlinearity, only by observing the phase tomography of the driven response. By steering the cubic phase-tomographic singularities in an electrostatically-tunable micromechanical system, enhanced cubic-root response to frequency perturbation and voltage-controlled nonreciprocity are demonstrated. Our work opens up a new phase-tomographic method for interacted-system research, and sheds new light on building and engineering advanced singular devices with simple and well-controllable elements, with a wide range of applications including precision metrology, portable nonreciprocal devices, and on-chip mechanical computing. © 2023, CC BY-SA.

关键词： Degrees of freedom (mechanics)

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Enhancement of quantum heat engine by encircling a Liouvillian exceptional point

arXiv

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arXiv 2023年

作者： Bu, J.-T. Zhang, J.-Q. Ding, G.-Y. Li, J.-C. Zhang, J.-W. Wang, B. Ding, W.-Q. Yuan, W.-F. Chen, L. Özdemir, S.K. Zhou, F. Jing, H. Feng, M. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Innovation Academy of Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan430071 China University of the Chinese Academy of Sciences Beijing100049 China Research Center for Quantum Precision Measurement Guangzhou Institute of Industry Technology Guangzhou511458 China Department of Engineering Science and Mechanics Materials Research Institute Pennsylvania State University University Park State CollegePA16802 United States Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Physics Zhengzhou University Zhengzhou450001 China

Quantum heat engines are expected to outperform the classical counterparts due to quantum coherences involved. Here we experimentally execute a single-ion quantum heat engine and demonstrate, for the first time, the dynamics and the enhanced performance of the heat engine originating from the Liouvillian exceptional points (LEPs). In contrast to the topological effects solely concerned for the LEPs, here we focus on the thermodynamic effects, which can be understood by the Landau-Zener-Stückelberg process under decoherence. We witness a positive net work from the quantum heat engine if the heat engine cycle dynamically encircles an LEP. Further investigation reveals that, under the same circumstance of the heat engine cycles, the largest net work occurs when the system is operated close to the LEP. We argue that our observation of the enhanced performance of the quantum heat engine benefits from the eigen-energy landscape close to the LEP and the topological transition induced by the LEP. Therefore, our results open a new possibility to explore the advantages of the LEPs in quantum systems from both the thermodynamic and topological aspects. © 2023, CC BY.

关键词： Topology

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Chiral quantum heating and cooling with an optically controlled ion

arXiv

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arXiv 2024年

作者： Bu, J.-T. Zhang, J.-Q. Ding, G.-Y. Li, J.-C. Zhang, J.-W. Wang, B. Ding, W.-Q. Yuan, W.-F. Chen, L. Zhong, Q. Kecebas, A. Özdemir, Ş.K. Zhou, F. Jing, H. Feng, M. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Innovation Academy of Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan430071 China University of the Chinese Academy of Sciences Beijing100049 China Research Center for Quantum Precision Measurement Guangzhou Institute of Industry Technology Guangzhou511458 China Department of Engineering Science and Mechanics Materials Research Institute Pennsylvania State University State College University ParkPA16802 United States Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Department of Physics Zhejiang Normal University Jinhua321004 China

Quantum heat engines and refrigerators are open quantum systems, whose dynamics can be well understood using a non-Hermitian formalism. A prominent feature of non-Hermiticity is the existence of exceptional points (EPs), which has no counterpart in closed quantum systems. It has been shown in classical systems that dynamical encirclement in the vicinity of an EP, whether the loop includes the EP or not, could lead to chiral mode conversion. Here, we show that this is valid also for quantum systems when dynamical encircling is per- formed in the vicinity of their Liouvillian EPs (LEPs) which include the effects of quantum jumps and associated noise - an important quantum feature not present in previous works. We demonstrate, using a Paul-trapped ultracold ion, the first chiral quantum heating and refrigeration by dynamically encircling a closed loop in the vicinity of an LEP. We witness the cycling direction to be associated with the chirality and heat release (absorption) of the quantum heat engine (quantum refrigerator). Our experiments have revealed that not only the adiabaticity-breakdown but also the Landau-Zener-Stückelberg process play an essential role during dynamic encircling, resulting in chiral thermodynamic cycles. Our observations contributes to further understanding of chiral and topological features in non-Hermitian systems and pave a way to exploring the relation between chirality and quantum thermodynamics. © 2024, CC BY.

关键词： Refrigerators

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Blockchain for Energy Market: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Jiang, Tianqi Luo, Haoxiang Yang, Kun Sun, Gang Yu, Hongfang Huang, Qi Vasilakos, Athanasios V. Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Guangyuan Power Supply Company State Grid Sichuan Electric Power Company Guangyuan628033 China Peng Cheng Laboratory Shenzhen518066 China Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu611731 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China Center for AI Research University of Agder Grimstad4879 Norway

The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. Additionally, the energy market can address future production control and environmental concerns such as energy shortages and environmental deterioration. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. These factors have hindered the smooth operation of the energy market. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the

关键词： Deterioration

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Society 5.0: Metaverse Facilitated Human-centric “5H” Services Across Cyber-Physical-Social Spaces

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IFAC-PapersOnLine 2024年第3期58卷 159-164页

作者： Xiao Wang Jun Huang QingHua Ni Zhe Wan HuaDan Xue Larry Stapleton Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China Beijing Normal University Beijing 100875 China Department of Radiology Peking Union Medical College Hospital Shuaifuyuan Beijing 100730 Dongcheng District China Brownsbarn Thomastown Co. Kilkenny Ireland R95D8P2 Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Cyber-Physical-Social Systems bridge the gap between social resource organization and distribution, and give birth to Society 5.0, which represents a transformative vision aimed at realizing the human-centric paradigm for more efficient and effective sustainable development. In this paper, we discuss the decentralized paradigm in fostering human needs-driven smart services, in line with Maslow’s hierarchy of needs theory. Societies 5.0 is committed to optimizing the use of natural, artificial, and social resources by enhancing democratic participation and ensuring that decision-making processes are more transparent, reliable, and aligned with the needs of the populace in sustainable ways. Moreover, our discussion extends to how Society 5.0’s principles correlate with the United Nations Sustainable Development Goals (SDGs), providing a framework that supports health, education, and poverty reduction, among other targets. This approach also aligns with the Human Development Index (HDI), reinforcing the notion that technological progress in Society 5.0 does not overshadow but rather complements and enhances human values, paving a synergistic path towards sustainable development that is both advanced and fundamentally human-centric.

关键词： Cyber-Physical-Social Systems Society 5.0 Human-Centric Services Socio-technical synergistic sustainable development

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