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Correlating the Effect of Covid-19 Lockdown with Mobility Impacts: A Time Series Study Using Noise Sensors Data

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Transportation Research Procedia 2022年 62卷 115-122页

作者： Antonio Pascale Simona Mancini Pedro M. d’Orey Claudio Guarnaccia Margarida C. Coelho Centre for Mechanical Technology and Automation Dept. of Mechanical Engineering University of Aveiro Campus Universitário de Santiago 3801-193 Aveiro Portugal Department of Information and Electric Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 I-84084 Fisciano Italy Real-Time and Embedded Computing Systems Research Centre (CISTER) ISEP IPP Rua Dr. António Bernardino de Almeida 431 4200-072 Porto Portugal Dept. of Civil Engineering University of Salerno Via Giovanni Paolo II 132 I-84084 Fisciano Italy

The Covid-19 crisis forced governments around the world to rapidly enact several restrictions to face the associated health emergency. The Portuguese government was no exception and, following the example of other countries, established various limitations to flat the contagions curve. This led to inevitable repercussions on mobility and environmental indicators including noise. This research aims to assess the impact of the lockdown due to Covid-19 disease on the noise levels recorded in the city of Porto, Portugal. Data from four noise sensors located in strategic spots of the city were used to calibrate and validate Time Series Models, allowing to impute the missing values in the datasets and rebuild them. The trend and the cyclic information were extracted from the reconstructed datasets using decomposition techniques. Finally, a Spearman correlation analysis between noise levels values and traffic volumes (extracted from five inductive loop detectors, located nearby the noise sensors) was performed. Results show that the noise levels series present a daily seasonal pattern and the trends values decreased from 6.7 to 7.5 dBA during the first lockdown period (March-May 2020). Moreover, the noise levels tend to gradually rise after the removal of restrictions. Finally, there is a monotonic relationship between noise levels and traffic volumes values, as confirmed by the positive moderate-to-high correlation coefficients found, and the sharp drop of the former during March-May 2020 can be attributed to the strong reduction of road traffic flows in the city.

关键词： Traffic Noise Time Series Models Covid-19 Inductive Loops

Scaling Solar Photocatalytic Hydrogen Production in China: Integrated Geospatial-Meteorological Analysis

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SSRN

SSRN 2024年

作者： Li, Yinan Li, Lanyu Yuan, Hongkuan He, Keji Chen, Hong Xie, Jianping Wang, Biao Wang, Xiaonan School of Physical Science and Technology Southwest University Chongqing400715 China Department of Chemical and Biomolecular Engineering National University of Singapore Singapore117585 Singapore Department of Chemical Engineering Tsinghua University Beijing100084 China China Oilfield Services Limited Yanjiao065201 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China School of Resources and Environment Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu611731 China Chongqing key Laboratory of Micro Nano Structure Optoelectronics School of Physical Science and Technology Southwest University Chongqing400715 China

Solar photocatalytic hydrogen production is considered a promising technology owing to its sustainable nature, while facing the challenges of improving and maintaining photocatalytic efficiency under prolonged variable weather conditions. Herein, we screen out the MoTe2/ZrS2 system with a theoretical solar-to-hydrogen conversion efficiency of 10.37%. To improve the actual efficiency among all 97,711 grid cells in China, we propose an original meteorological data-driven machine learning model to optimize photocatalytic H2 production and results show that the annual average STH efficiency gives a mean of 3.16% within a range of 0.44% – 4.91% . Incorporating geospatial data, we determine that China's photocatalytic H2 potential is about 216.65 Mt/year, which has been improved by almost sixfold compared to that without optimization, and the country’s 2060 hydrogen demand can be met by using 11.08% of its total land area. Compared with photovoltaic electrolysis, a photocatalyst cost of 23 USD/m2cat/103hrs would make photocatalysis economically competitive. Last, considering hydrogen production fluctuations, we explore optimal operation of hydrogen storage and utilization facilities to fulfil downstream demands. © 2024, The Authors. All rights reserved.

关键词： Hydrogen production

ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

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arXiv 2023年

作者： Zhong, Tianyang Wei, Yaonai Yang, Li Wu, Zihao Liu, Zhengliang Wei, Xiaozheng Li, Wenjun Yao, Junjie Ma, Chong Li, Xiang Zhu, Dajiang Jiang, Xi Han, Junwei Shen, Dinggang Liu, Tianming Zhang, Tuo School of Automation Northwestern Polytechnical University Xi'An710072 China School of Computing The University of Georgia Athens30602 United States Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China School of life science and technology University of Electronic Science and Technology of China Chengdu611731 China

Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT. © 2023, CC BY.

关键词： Semantics

A Kind of Lean Approach for Removing Wastes From Non-Manufacturing Process With Various Facilities

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 307-315页

作者： Guangyu Xiong Xiuqin Shang Gang Xiong Timo R.Nyberg IEEE the Silk Road Business School Sanya University the Department of Industrial Engineering and Management School of ScienceAalto University the State Key Laboratory for Management and Controlof Complex Systems Institute of AutomationChinese Academy of Sciences(CAS) Qingdao Academy of Intelligent Industries the Cloud Computing Center CAS the Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationCAS the School of Science at Aalto University

It is important to identify and remove the wastes not only from manufacturing process, but also from nonmanufacturing process. In the last several decades, significant research achievements and practice benefits have been achieved about removing wastes from manufacturing process. Since the1990 s, some researchers and lean practitioners have paid more attention to removing waste from non-manufacturing *** on the authors' research work and industrial practice, the paper introduces a kind of lean approach for removing waste from non-manufacturing process. In its case study, the order handling process in a value chain is described with respect to a factory and its downstream distribution centers(DCs). The paper proposes a lean approach solution for creating the improved order handling process, and analyze how great improvements in performance can be achieved. As a result, the significant achievement has created a win-win scenario for both the nonmanufacturing process in a factory and non-manufacturing facilities(like DCs) across the value chain. It demonstrates that improvements have been made by removing waste from the non-manufacturing process that takes place within a factory as well as with external participants through the whole value chain. Likewise, the proposed lean approach has helped the case companies to achieve greater levels of efficiency and more benefits. Finally, some conclusions are drawn.

关键词： Lean approach non-manufacturing process optimized order handling process removing wastes value chain win-win scenario

Model-free design of stochastic lqr controller from reinforcement learning and primal-dual optimization perspective

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arXiv 2021年

作者： Li, Man Qin, Jiahu Zheng, Wei Xing Wang, Yaonan Kang, Yu Department of Automation University of Science and Technology of China Hefei230027 China School of Computing Engineering and Mathematics Western Sydney University SydneyNSW2751 Australia College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha410082 China

To further understand the underlying mechanism of various reinforcement learning (RL) algorithms and also to better use the optimization theory to make further progress in RL, many researchers begin to revisit the linear-quadratic regulator (LQR) problem, whose setting is simple and yet captures the characteristics of RL. Inspired by this, this work is concerned with the model-free design of stochastic LQR controller for linear systems subject to Gaussian noises, from the perspective of both RL and primal-dual optimization. From the RL perspective, we first develop a new model-free off-policy policy iteration (MF-OPPI) algorithm, in which the sampled data is repeatedly used for updating the policy to alleviate the data-hungry problem to some extent. We then provide a rigorous analysis for algorithm convergence by showing that the involved iterations are equivalent to the iterations in the classical policy iteration (PI) algorithm. From the perspective of optimization, we first reformulate the stochastic LQR problem at hand as a constrained non-convex optimization problem, which is shown to have strong duality. Then, to solve this non-convex optimization problem, we propose a model-based primal-dual (MBPD) algorithm based on the properties of the resulting Karush-Kuhn-Tucker (KKT) conditions. We also give a model-free implementation for the MB-PD algorithm by solving a transformed dual feasibility condition. More importantly, we show that the dual and primal update steps in the MB-PD algorithm can be interpreted as the policy evaluation and policy improvement steps in the PI algorithm, respectively. Finally, we provide one simulation example to show the performance of the proposed algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

Comparative evaluation of training schemes for the locally recurrent probabilistic neural network 28

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Comparative evaluation of training schemes for the locally r...

28th International Scientific Conference Electronics, ET 2019 - Proceedings

作者： Dukov, Nikolay Tinkov Ganchev, Todor Dimitrov Department of Computer Science and Engineering Faculty of Computing and Automation Technical University of Varna 1 Studentska str. Varna9000 Bulgaria

ISBN: (纸本)9781728125749

In the present study we evaluate the performance of various training schemes for the locally recurrent probabilistic neural network and seek for advantageous tradeoffs between required training time and classification accuracy. Specifically, we consider training schemes which make use of a simple incremental procedure for adjusting sigma, as well as methods based on particle swarm optimization or differential evolution in different configurations. The experimental evaluation was carried out in common experimental protocol based on the Parkinson speech dataset. The experimental results show that with a proper training configuration a high accuracy can be achieved even with limited training data. © 2019 IEEE.

关键词： Particle swarm optimization (PSO)

Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

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arXiv 2022年

作者： Zhang, Jie Zhao, Yihui Bao, Tianzhe Li, Zhenhong Qian, Kun Frangi, Alejandro F. Xie, Sheng Quan Zhang, Zhi-Qiang The School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom The School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China The School of Rehabilitation Sciences and Engineering University of Health and Rehabilitation Sciences Qingdao261000 China The School of Computing University of Leeds LeedsLS2 9JT United Kingdom The Alan Turing Institute LondonNW1 2DB United Kingdom The Department of Electrical Engineering KU Leuven Leuven3000 Belgium

Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performance of a pre-trained data-driven model using the data from specific subject(s) may be seriously degraded when validated using the data from a new subject, hindering the utility of the personalised musculoskeletal model in clinical applications. This paper develops an active physics-informed deep transfer learning framework to enhance the dynamic tracking capability of the musculoskeletal model on the unseen data. The salient advantages of the proposed framework are twofold: 1) For the generic model, physics-based domain knowledge is embedded into the loss function of the data-driven model as soft constraints to penalise/regularise the data-driven model. 2) For the personalised model, the parameters relating to the feature extraction will be directly inherited from the generic model, and only the parameters relating to the subject-specific inference will be fine-tuned by jointly minimising the conventional data prediction loss and the modified physics-based loss. In this paper, we use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Moreover, convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework, and the physics law between muscle forces and joint kinematics is utilised as the soft constraints. Results of comprehensive experiments on a self-collected dataset from eight healthy subjects indicate the effectiveness and great generalization of the proposed framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

A Learning Convolutional Neural Network Approach for Network Robustness Prediction

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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