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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wen Wen Ding XiaoLong Liang JiaChen Hou Ge Wang Yong Yuan Junqing Li Fei-Yue Wang Institute of Systems Engineering Macau University of Science and Technology Macau China School of Mathematics Renmin University of China Beijing China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Decentralized autonomous organizations(DAOs) enabled by blockchain and smart contracts is regarded as an effective tool to solve corporate governance problems. It can minimize the contract risks, principal-agent dilemmas, and other issues in corporate governance. However, DAOs also face a variety of governance issues, such as internal governance issues and external governance issues caused by the technical attributes of blockchain and smart contracts. DAO is a typical complex system with the characteristics of both social and engineering complexity. It is difficult for existing governance theories to solve governance problems in DAOs. The parallel intelligence theory based on the artificial systems + computational experiments + parallel execution (ACP) method is a suitable research and practical approach to solve management and control problems for complex systems. Therefore, we propose a parallel governance framework for DAO based on the parallel intelligence theory and discuss its key research issues. Finally, we construct a parallel governance system for GnosisDAO, and conduct computational experiments on its voting mechanism. The results show the defects of GnosisDAO governance mechanism and we thus propose further research directions. We consider that parallel governance represents the future trend of DAO research and practice. This paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works.

关键词： Computational modeling Digital twin Conferences Smart contracts Tools Systems modeling Market research

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Sample complexity of Linear Quadratic Gaussian (LQG) control for output feedback systems

arXiv

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arXiv 2020年

作者： Zheng, Yang Furieri, Luca Kamgarpour, Maryam Li, Na School of Engineering and Applied Sciences Harvard University United States Automatic Control Laboratory ETH Zurich Switzerland Laboratoire d'Automatique EPFL Switzerland Electrical and Computer Engineering University of British Columbia Canada

This paper studies a class of partially observed Linear Quadratic Gaussian (LQG) problems with unknown dynamics. We establish an end-to-end sample complexity bound on learning a robust LQG controller for open-loop stable plants. This is achieved using a robust synthesis procedure, where we first estimate a model from a single input-output trajectory of finite length, identify an H-infinity bound on the estimation error, and then design a robust controller using the estimated model and its quantified uncertainty. Our synthesis procedure leverages a recent control tool called Input-Output Parameterization (IOP) that enables robust controller design using convex optimization. For open-loop stable systems, we prove that the LQG performance degrades linearly with respect to the model estimation error using the proposed synthesis procedure. Despite the hidden states in the LQG problem, the achieved scaling matches previous results on learning Linear Quadratic Regulator (LQR) controllers with full state observations. Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

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Parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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Correction to: Global existence and stability of Nicholson blowflies model with harvesting and random effect

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Nonlinear Dynamics 2022年第4期108卷 4629-4629页

作者： Abbas, Syed Niezabitowski, Michal Grace, Said R. School of Basic Sciences Indian Institute of Technology Mandi Mandi India Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Engineering Mathematics Faculty of Engineering Cairo University Giza Egypt

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A Practical and High-Performance Nitrogen Dioxide Gas Sensor that Can Be Rapidly Mass-Produced Based on Gallium Nitride Quantum Dots Film

SSRN

引用

SSRN 2022年

作者： Han, Dan Chen, Yi Li, Donghui Dong, Hailiang Xu, Bingshe Sang, Shengbo He, Xiuli Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Research Institute of 6D Artificiat Intelligence Biomedical Science Taiyuan030024 China

Gallium nitride (GaN) has some challenges for gas sensing, such as high detection lower limit (LOD) or controlling the epitaxy of nanostructures. To address these issues, we exclusively designed a device to enhance the gas interaction capabilities of GaN quantum dots (QDs) film fabricated by metal-organic chemical vapor deposition (MOCVD). The GaN QDs film gas sensor exhibited a low LOD (5 ppb) to NO2 at room temperature (RT), which has much superior gas performance than other GaN-based sensors. The results manifest that many ultra-small QDs around 2 nm in diameter are stacked to form QDs of larger size, and confirm the existence of vacancies. The sensor displays a ultra-high electron mobility of 1237.37 cm2 V−1s−1, demonstrate that great shallow donor impurities in GaN QDs film that ionize easily at RT, which is greatly beneficial for gas sensing. The sensing mechanisms were analyzed based on its abundant active sites, ultrahigh carrier mobility, high surface energy and chemical activity, grain boundary barrier and good electrical conductivity. Moreover, the sensor processes high selectivity, repeatability, stability and reusability based on MOCVD technology which can realize precise control of structure, morphology and surface energy level, which is expected to achieve rapid industrialization. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

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Utilization of CNN-LSTM Model in Prediction of Multivariate Time Series for UCG 3rd

Utilization of CNN-LSTM Model in Prediction of Multivariate ...

引用

3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Xiao, Yuteng Yin, Hongsheng Xia, Kaijian Zhang, Yundong Qi, Honggang School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Digital Multi-Media Chip Technology Vimicro Corporation 16/F Shining Tower 35 Xueyuan Road Haidian District Beijing100191 China School of Computer Science and Technology University of Chinese Academic of Sciences Beijing100049 China

ISBN: (纸本)9783030624620

Underground Coal Gasification (UCG) is a serious attempt to clean and efficient use of coal. However, existing methods for predicting UCG are mostly single-variable and not very accurate, which hinders the development of UCG to some extent. Based on the spatio-temporal characteristics, we proposed the CNN-LSTM model. Initially, we reconstructed the UCG concentration data so that they represent spatial and temporal properties, respectively. Then, the spatial features are extracted from the spatial concentration matrices by the CNN at the bottom of the model. Thirdly, the LSTM captures the temporal dependencies among the spatial features. Finally, the final predictions are obtained from the last layer of the LSTM. Based on the data from China Huating coal UCG field test, the experiment shows that our model outperforms prior methods. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Study of Structure Evolution of Ultra-Rapidly Annealed Fe75.3ni10b14cu0.7 Alloys

SSRN

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SSRN 2023年

作者： Kolano – Burian, Aleksandra Kowalczyk, Maciej Grabias, Agnieszka Blyskun, Piotr Gazinska, Malgorzata Zackiewicz, Przemyslaw Wójcik, Anna Chulist, R. Maziarz, Wojciech Pilśniak, Adam Bernacki, Krzysztof Faculty of Materials Science and Engineering Warsaw University of Technology Woloska 141 Warszawa05-507 Poland Lukasiewicz Research Network Institute of Non-Ferrous Metals Sowińskiego 5 Gliwice44-100 Poland Lukasiewicz Research Network Institute of Microelectronics and Photonics al. Lotników 32/46 Warszawa02-668 Poland The Aleksander Krupkowski Institute of Metallurgy and Materials Science Polish Academy of Sciences 25 Reymonta St. Kraków30-059 Poland Faculty of Chemistry Wroclaw University of Science and Technology Norwida 4/6 Wroclaw50-373 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

The article concerns modern soft magnetic materials made by rapid solidification and subsequent ultra-rapid annealing. Due to the lower amount of non-magnetic elements, higher magnetisation is possible. Unfortunately, the lack of those elements causes difficulty during isothermal nanocrystallisation operation, being crucial from the final product properties point of *** of isothermal heat treatment, ultra-rapid annealing is implemented. It reflects in materials with good magnetic softness and higher possible magnetisation *** amorphous alloy with Fe75.3Ni10B14Cu0.7 chemical composition was heat treated between cooper blocks at 773K in the range of 1-60s. To study structure evolution, we brought XRD, DSC, Flash DSC, Mössbauer spectroscopy, TEM, and HRTEM techniques to *** on the DSC results, we estimated the minimum heating speed to obtain optimal structure at 6000K/min. The Mössbauer results allowed us to study annealing time-dependent local atomic ordering in the amorphous state. A comparison of X-Ray and synchrotron diffraction results drove us to the conclusion that X-Ray measurements are sufficient to determine the material structure. TEM shows the formation of α-Fe crystallites even after an annealing time of 0.5s. The increase in annealing time leads to an increase in the crystalline phase volume fraction and growth of α-Fe crystallite size. © 2023, The Authors. All rights reserved.

关键词： Differential scanning calorimetry

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Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

arXiv

引用

arXiv 2022年

作者： Gao, Jiaqi Huang, Zhizhong Lei, Yiming Shan, Hongming Wang, James Z. Wang, Fei-Yue Zhang, Junping The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China The College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology China Qingdao Academy of Intelligent Industries Qingdao266109 China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://***/bridgeqiqi/DREAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Label-consistency based graph neural networks for semi-supervised node classification

arXiv

引用

arXiv 2020年

作者： Xu, Bingbing Huang, Junjie Hou, Liang Shen, Huawei Gao, Jinhua Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network (LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification. We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Sliding sequential CVAE with time variant socially-aware rethinking for trajectory prediction

arXiv

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arXiv 2021年

作者： Zhou, Hao Ren, Dongchun Yang, Xu Fan, Mingyu Huang, Hai National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China Research Center for Autonomous Vehicles Meituan Beijing China College of Computer Science Wenzhou University Wenzhou China National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China

Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore. Second, the prediction results of multiple pedestrians might be impractical in the prediction horizon, i.e., the predicted trajectories might collide with each other. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The cascaded CVAE module first estimates the future trajectories in a sequential pattern. Specifically, each CVAE concatenates the past trajectories and the predicted points so far as the input and predicts the location at the following time step. Then, the socially-aware regression module generates offsets from the estimated future trajectories to produce the socially compliant final predictions, which are more reasonable and accurate results than the estimated trajectories. Moreover, considering the large model parameters of the cascaded CVAE module, a slide CVAE module is further exploited to improve the model efficiency using one shared CVAE, in a slidable manner. Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone Dataset (SDD) and ETH/UCY of approximately 38.0% and 22.2%, respectively. © 2021, CC BY.

关键词： Trajectories

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