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Using Artificial Neural Networks to Estimate the Equivalent Resistivity from typical Transmission Line Towers Grounding Arrangement in a Two-Layer Soil

Using Artificial Neural Networks to Estimate the Equivalent ...

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International Symposium on Lightning Protection (XII SIPDA)

作者： Raphael Batista João R. Souza Graduate Program in Automation and Control Engineering IFMG Betim MG Brazil Dept. of Electroeletronics and Computing CEFET-MG Alameda das Perdizes 61 Contagem MG Brazil

This paper presents a procedure to estimate a homogeneous equivalent resistivity for typical transmission line tower ground arrangements that are immersed in a stratified soil model. This technique is based on artificial neural network training due to a dataset developed in a numerical routine to compute grounding resistance. The results obtained from Levemberg-Marquadt algorithm and Bayesian regularization show good accuracy compared to the applied dataset and to the measurement data from a 230 kV transmission line, a practical case analyzed. We note medians lower than 5% and acceptable values of standard deviation for practical cases, which suggests that the proposed procedure can be used for real-world conditions.

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Improved quantum algorithm for A-optimal projection

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Physical Review A 2020年第5期102卷 052402-052402页

作者： Shi-Jie Pan Lin-Chun Wan Hai-Ling Liu Qing-Le Wang Su-Juan Qin Qiao-Yan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing 100878 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China School of Control and Computer Engineering North China Electric Power University Beijing 102206 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 China

Dimensionality reduction algorithms, which reduce the dimensionality of a given data set whereas preserving the information of the original data set as well as possible, play an important role in machine learning and data mining. Duan et al. proposed a quantum version of the A-optimal projection algorithm (AOP) for dimensionality reduction [Phys. Rev. A 99, 032311 (2019)] and claimed that the algorithm has exponential speedups on the dimensionality of the original feature space n and the dimensionality of the reduced feature space k over the classical algorithm. In this paper, we correct the time complexity of the algorithm of Duan et al. to Oκ4sksεspolylogsmnε, where κ is the condition number of a matrix that related to the original data set, s is the number of iterations, m is the number of data points, and ε is the desired precision of the output state. Since the time complexity has an exponential dependence on s, the quantum algorithm can only be beneficial for high-dimensional problems with a small number of iterations s. To get a further speedup, we propose an improved quantum AOP algorithm with time complexity Osκ6kεpolylognmε+s2κ4εpolylogκkε and space complexity O[log2(nk/ε)+s]. With space complexity slightly worse, our algorithm achieves, at least, a polynomial speedup compared to the algorithm of Duan et al.. Also, our algorithm shows exponential speedups in n and m compared with the classical algorithm when κ,k, and 1/ε are O[polylog(nm)].

关键词： Machine learning Quantum algorithms Quantum computation

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Distributed State Estimation for Heterogeneous Mobile Sensor Networks with Sensor Faults

Distributed State Estimation for Heterogeneous Mobile Sensor...

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第三十八届中国控制会议

作者： Yingrong Yu Siting Peng Qingdong Li Xiwang Dong Zhang Ren Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing Electro-Mechanical Engineering Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Consensus-based distributed state estimation for homogeneous mobile sensor networks has been widely studied currently. However, the sensors used for target detection and tracking in practical applications usually have different physical properties, different measuring range or accuracy. In order to realize data exchange between sensors which are of heterogeneity and to obtain a consistent and precise target state estimation result, an improved Cubature Kalman Filtering(CKF) method based on weighted average consensus theory is put forward herein. Moreover, the adverse impacts caused by sensor faults have been taken into consideration in modeling. The stochastic boundedness of the estimation errors for the discussed sensor networks is proved in the paper. Finally, numerical simulations are provided to demonstrate the validity of the proposed method.

关键词： Cubature Kalman Filter weighted average consensus theory target state estimation mobile sensor networks stochastic boundedness

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Chinese image caption of Inceptionv4 and double-layer GRUs based on attention mechanism

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Journal of Physics: Conference Series 2021年第1期1861卷

作者： Yongbin Pan Lidan Wang Shukai Duan Xiuling Gan Liangyi Hong College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

In recent years, there has been a wave of research on English image caption at home and abroad. However, due to the particularity of Chinese image caption task, the research on Chinese image caption has not made good progress. In order to solve this problem, a new Chinese image caption model is implemented. Firstly, the AI challenge dataset is enhanced, and then the Chinese text data of the dataset is preprocessed by Chinese word segmentation tool word2vec. Secondly, based on the encoder-decoder framework, the image visual features are extracted by Inceptionv4 network, the attention mechanism is incorporated in the process of feature extraction and the Chinese sentences are generated by double-layer GRUs network. In the process of training, Adam is used to optimize the algorithm. Finally, A GUI interface is designed to better show the experimental effect. Experiments show that the new Chinese image caption model can automatically generate more fluent and more accurate Chinese caption sentences, and the trained model has excellent performance in many evaluation indexes.

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GAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation

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Journal of Physics: Conference Series 2021年第1期1861卷

作者： Xiuling Gan Lidan Wang Qi Chen Yongjie Ge Shukai Duan School of Electronic & Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

Deep learning has shown great advantages in biomedical image segmentation. The classic model U-Net uses a stacked encoding-decoding structure of convolution operations for feature extraction and pixel-level classification. The stacking of convolutional layers can expand the receptive field, but it is still a local operation and cannot capture long-distance dependence. Therefore, in this work, we propose a Global Attention Mechanism that combines channel attention module and spatial attention module and integrates different convolutions in it. Besides, we design a residual module for the traditional up and down sampling blocks. And finally, we combine them with U-Net to propose a new global attention network GAU-Net. We perform experiments on the dataset BraTS2018. Our model has increased the mIoU from 0.65 to 0.75 with only 5.4% of U-Net parameters. At the same time, the inference time is also significantly shortened with relatively good performance.

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Customized quantum annealing schedules

arXiv

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arXiv 2021年

作者： Khezri, Mostafa Dai, Xi Yang, Rui Albash, Tameem Lupascu, Adrian Lidar, Daniel A. Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Physics and Astronomy University of Waterloo WaterlooON Canada Institute for Quantum Computing University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Center for Quantum Information and Control CQuIC University of New Mexico AlbuquerqueNM87131 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States Department of Physics University of Southern California Los AngelesCA90089 United States

In a typical quantum annealing protocol, the system starts with a transverse field Hamiltonian which is gradually turned off and replaced by a longitudinal Ising Hamiltonian. The ground state of the Ising Hamiltonian encodes the solution to the computational problem of interest, and the state overlap with this ground state gives the success probability of the annealing protocol. The form of the annealing schedule can have a significant impact on the ground state overlap at the end of the anneal, so precise control over these annealing schedules can be a powerful tool for increasing success probabilities of annealing protocols. Here we show how superconducting circuits, in particular capacitively shunted flux qubits (CSFQs), can be used to construct quantum annealing systems by providing tools for mapping circuit flux biases to Pauli coefficients. We use this mapping to find customized annealing schedules: appropriate circuit control biases that yield a desired annealing schedule, while accounting for the physical limitations of the circuitry. We then provide examples and proposals that utilize this capability to improve quantum annealing performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Annealing

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Graph wavelet neural network

arXiv

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arXiv 2019年

作者： Xu, Bingbing Shen, Huawei Cao, Qi Qiu, Yunqi Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. Copyright © 2019, The Authors. All rights reserved.

关键词： Wavelet transforms

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Cyber-Physical-Social Systems for Smart City: An Implementation Based on Intelligent Loop

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IFAC-PapersOnLine 2020年第5期53卷 501-506页

作者： Gang Xiong Xiaoyu Chen Nan Shuo Yisheng Lv Fenghua Zhu Tianci Qu Peijun Ye State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China Graduate School of System Design Tokyo Metropolitan University Tokyo 191-0065 Japan

Cyber-Physical-Social Systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

关键词： CPSS Intelligent Loop Human-Cyber-Physical Ternary Space

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A gray-box approach for curriculum learning

arXiv

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arXiv 2019年

作者： Foglino, Francesco Leonetti, Matteo Sagratella, Simone Seccia, Ruggiero School of Computing University of Leeds Leeds United Kingdom Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 Roma00185 Italy

Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in the literature provides only initial heuristic solutions, with little to no guarantee on their quality. We define a new gray-box function that, including a suitable scheduling problem, can be effectively used to reformulate the curriculum learning problem. We propose different efficient numerical methods to address this gray-box reformulation. Preliminary numerical results on a benchmark task in the curriculum learning literature show the viability of the proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Collaborative Optimization of Cyber Physical Social Systems for Urban Transportation Based on Knowledge Automation

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IFAC-PapersOnLine 2020年第5期53卷 572-577页

The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

关键词： CPSS knowledge automation urban transportation Collaborative Optimization

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