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arXiv 2020年

作者： Gao, Meilin Ai, Bo Niu, Yong Wu, Wen Yang, Peng Lyu, Feng Shen, Xuemin State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China University of Waterloo WaterlooONN2L 3G1 Canada School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Engineering Central South University Changsha410083 China

Future railway is expected to accommodate both train operation services and passenger broadband services. The millimeter wave (mmWave) communication is a promising technology in providing multi-gigabit data rates to onboard users. However, mmWave communications suffer from severe propagation attenuation and vulnerability to blockage, which can be very challenging in high-speed railway (HSR) scenarios. In this paper, we investigate efficient hybrid beamforming (HBF) design for train-to-ground communications. First, we develop a two-stage HBF algorithm in blockage-free scenarios. In the first stage, the minimum mean square error method is adopted for optimal hybrid beamformer design with low complexity and fast convergence;in the second stage, the orthogonal matching pursuit method is utilized to approximately recover the analog and digital beamformers. Second, in blocked scenarios, we design an anti-blockage scheme by adaptively invoking the proposed HBF algorithm, which can efficiently deal with random blockages. Extensive simulation results are presented to show the sum rate performance of the proposed algorithms under various configurations, including transmission power, velocity of the train, blockage probability, etc. It is demonstrated that the proposed anti-blockage algorithm can improve the effective rate by 20% in severely-blocked scenarios while maintaining low outage probability. Copyright © 2020, The Authors. All rights reserved.

关键词： Mean square error

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Generative Adversarial Networks (GAN) powered fast magnetic resonance imaging - Mini review, comparison and perspectives

arXiv

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arXiv 2021年

作者： Yang, Guang Lv, Jun Chen, Yutong Huang, Jiahao Zhu, Jin National Heart & Lung Institute Imperial College London LondonSW7 2AZ United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom School of Computer and Control Engineering Yantai University Yantai China Department of Physiology Development and Neuroscience University of Cambridge CB2 3EG United Kingdom School of Optics and Photonics Beijing Institute of Technology Beijing China Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom

Magnetic Resonance Imaging (MRI) is a vital component of medical imaging. When compared to other image modalities, it has advantages such as the absence of radiation, superior soft tissue contrast, and complementary multiple sequence information. However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical. Traditional compressive sensing based MRI (CS-MRI) reconstruction can speed up MRI acquisition, but suffers from a long iterative process and noise-induced artefacts. Recently, Deep Neural Networks (DNNs) have been used in sparse MRI reconstruction models to recreate relatively high-quality images from heavily undersampled k-space data, allowing for much faster MRI scanning. However, there are still some hurdles to tackle. For example, directly training DNNs based on L1/L2 distance to the target fully sampled images could result in blurry reconstruction because L1/L2 loss can only enforce overall image or patch similarity and does not take into account local information such as anatomical sharpness. It is also hard to preserve fine image details while maintaining a natural appearance. More recently, Generative Adversarial Networks (GAN) based methods are proposed to solve fast MRI with enhanced image perceptual quality. The encoder obtains a latent space for the undersampling image, and the image is reconstructed by the decoder using the GAN loss. In this chapter, we review the GAN powered fast MRI methods with a comparative study on various anatomical datasets to demonstrate the generalisability and robustness of this kind of fast MRI while providing future *** Codes 68T01 Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware

arXiv

引用

arXiv 2021年

作者： Nelson, Jon Vuffray, Marc Lokhov, Andrey Y. Albash, Tameem Coffrin, Carleton Center for Non Linear Studies Los Alamos National Laboratory Los AlamosNM United States Advanced Network Science Initiative Los Alamos National Laboratory Los AlamosNM United States Theoretical Division Los Alamos National Laboratory Los AlamosNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Department of Physics and Astronomy Center for Quantum Information and Control University of New Mexico AlbuquerqueNM United States

Quantum Annealing (QA) was originally intended for accelerating the solution of combinatorial optimization tasks that have natural encodings as Ising models. However, recent experiments on QA hardware platforms have demonstrated that, in the operating regime corresponding to weak interactions, the QA hardware behaves like a noisy Gibbs sampler at a hardware-specific effective temperature. This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs sampling performance. Experimental results indicate that the proposed protocol results in high-quality Gibbs samples from a hardware-specific effective temperature. Furthermore, we show that this effective temperature can be adjusted by modulating the annealing time and energy scale. The procedure proposed in this work provides an approach to using QA hardware for Ising model sampling presenting potential new opportunities for applications in machine learning and physics simulation. Copyright © 2021, The Authors. All rights reserved.

关键词： Ising model

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Fast Fuzzy Clustering Algorithm for Time Series in Industrial Processes

Fast Fuzzy Clustering Algorithm for Time Series in Industria...

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International Conference on computer and Communications (ICCC)

作者： He Ji Jinkuan Wang Yinghua Han Qiang Zhao College of Information Science and Engineering Northeastern University Shenyang China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

Process industry data is often continuously measured to capture fluctuations and changes of a production processes, and this sequentially dataset can be looked as time series. We usually don't know the correlation and classification between these data in advance. Clustering is an effective way to solve this problem, and it have been used in many fields in recent years. There are many different fuzzy clustering methods have been proposed, fuzzy c-means clustering (FCM) and fuzzy c-medoids clustering (FCMdd) are most common algorithm among them. But all of these algorithms use Euclidean distance as the similarity measure and align two times series time-by-time. These methods cannot adapt to the complex industrial environment. In this paper, dynamic time warping (DTW) is selected as the distance of the FCMdd method to adapt to complex industrial production environments, and a type of constraint is introduced to speed up it. The novel clustering method we proposed can not only speed up the clustering algorithm, but also improve the accuracy in some case. Three sets of open source sensor and simulated datasets are selected to evaluate this algorithm and achieved satisfactory results.

关键词： Time series analysis Clustering algorithms Euclidean distance Heuristic algorithms Industries Time measurement Production

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Identification of Cancer Development Related Pathways Based on Co-Expression Analyses

Identification of Cancer Development Related Pathways Based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Zhihang Wang Chaoyi Yin Peishuo Sun Ying Xu Huiyan Sun School of Artificial Intelligence Jilin University Changchun Innovation Group of Marine Engineering Materials and Corrosion Control Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China Computational Systems Biology Lab Institute of Bioinformatics University of Georgia Athens USA School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and biological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, subtype) tend to share highly unique co-expression patterns, providing considerably stronger discerning power than differential expressions for cancer staging (and grading, subtyping). Based on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that become increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, ribosome, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among ribosome, proteasome and oxidative phosphorylation genes tend to become increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to become increasingly stronger with cancer progression. We anticipate that co-expression-based analyses like we present here will become a key technique for functional studies of cancer development and evolution.

关键词： Cancer Correlation Indexes Biological processes Physiology Immune system Genetic expression

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Infrastructure-free Multi-robot Localization with Ultrawideband Sensors

Infrastructure-free Multi-robot Localization with Ultrawideb...

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American control Conference

作者： Samet Guler Mohamed Abdelkader Jeff S. Shamma Computer Electrical and Mathematical Science and Engineering Division (CEMSE) KAUST Robotics Intelligent Systems and Control (RISC) Lab Thuwal 23955-6900 Saudi Arabia

ISBN: (纸本)9781538679012;9781538679265

Swarm applications use motion capture system or GPS sensors as localization systems. However, motion capture systems provide local solutions, and GPS sensors are not reliable in occluded environments. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard localization framework for multi-robot systems. Our framework consists of an anchor robot with three ultrawideband (UWB) sensors and a tag robot with a single UWB sensor. The anchor robot utilizes the three UWB sensors as a localization infrastructure and estimates the tag robot's location by using its on-board sensing and computational capabilities solely, without explicit inter-robot communication. We utilize a dual Monte-Carlo localization approach to capture the agile maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor experiments on a two-drone setup. The proposed framework with the dual MCL algorithm yields accurate estimates for various speed profiles of the tag robot, outperforms the standard particle filter and extended Kalman filter, and suffice for a relative position maintenance application.

关键词： Ultra wideband technologies Robot motion capture Sensors Particle filters multi-robot systems Extended Kalman filters Frameworks

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An Online Approach to Physical Watermark Design

arXiv

引用

arXiv 2019年

作者： Liu, Hanxiao Mo, Yilin Yan, Jiaqi Xie, Lihua Johansson, Karl H. School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Automation and BNRist Tsinghua University China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper considers the problem of designing physical watermark signals in order to optimally detect possible replay attack in a linear time-invariant system, under the assumption that the system parameters are unknown and need to be identified online. We first provide a replay attack model, where an adversary replays the previous sensor data in order to fool the system. A physical watermarking scheme, which leverages a random control input as a watermark to detect the replay attack, is then introduced. The optimal watermark signal design problem is cast as an optimization problem, which aims to achieve the optimal trade-off between control performance and intrusion detection. An online watermarking design and system identification algorithm is provided to deal with systems with unknown parameters. We prove that the proposed algorithm converges to the optimal one and characterize the almost sure convergence rate. A numerical example and an industrial process example are provided to illustrate the effectiveness of the proposed strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Intrusion detection

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Distributed barrier certificates for safe operation of inverter-based microgrids

arXiv

引用

arXiv 2019年

作者： Kundu, Soumya Nandanoori, Sai Pushpak Kalsi, Karan Geng, Sijia Hiskens, Ian A. Optimization and Control Group Pacific Northwest National Laboratory United States Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Inverter-interfaced microgrids differ from the traditional power systems due to their lack of inertia. Vanishing timescale separation between voltage and frequency dynamics makes it critical that faster-timescale stabilizing control laws also guarantee by-construction the satisfaction of voltage limits during transients. In this article, we apply a barrier functions method to compute distributed active and reactive power setpoint control laws that certify satisfaction of voltage limits during transients. Using sum-of-squares optimization tools, we propose an algorithmic construction of these control laws. Numerical simulations are provided to illustrate the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric inverters

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Research on STLF Method Based on One-Dimensional Convolution and Slope Feature

Research on STLF Method Based on One-Dimensional Convolution...

引用

IEEE International Conference on Power Data science (ICPDS)

作者： Qi Zeng Haihui Pan Bin Chen Zhifang Liao School of Computer Science and Engineering Central South University Changsha China State Grid Hunan Dispatching and Control Center State Grid Changsha China

ISBN: (数字)9781728137759

ISBN: (纸本)9781728137766

Short-term load forecasting is of great importance to how to efficiently utilize generating units, optimize resource allocation and ensure the normal transmission of power in power system. This paper mainly studies how to improve the accuracy of short-term load forecasting through feature engineering and deep learning technology. Specifically, we construct a feature based on the slope of load data according to the changing trend of load data. We applied the proposed method to two real datasets of Hunan power grid and obtained MAPE values of 0.5758 and 0.5745 respectively. The experimental results show the effectiveness of the proposed method.

关键词： Convolution Load modeling Load forecasting Predictive models Machine learning Analytical models Neural networks

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Data-Driven Student Learning Performance Prediction based on RBF Neural Network

引用

International Journal of Performability engineering 2019年第6期15卷 1560-1569页

作者： Mi, Chunqiao School of Computer Science and Engineering Huaihua University Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

With the expansion of college enrollment in recent years, the quality of students' learning is beginning to decline. At present, education quality governance has become the internal demand of the reform and development of higher education. Learning performance prediction is an important means to effectively resolve the academic crisis and improve the overall education quality. In this study, firstly, the current status and problems about learning performance prediction were analyzed from the perspective of basic data, evaluation indicators, and prediction methods. Secondly, driven by ten items of basic learning situation data, a learning performance prediction model based on the RBF neural network was established, which included three layers in network topology: the input layer, hidden layer, and output layer. The activation functions of the hidden layer and output layer were a Gauss radial basis function and linear function, respectively. The modeling process included three steps: forward propagation computing prediction loss, error backward propagation adjusting network parameters, and network optimization determining model hyperparameters. The obtained results showed that the trained model had small relative root mean square error values for both the training data and testing data. When comparing the original observation values and model predicted values, it was observed that most of the sample points were evenly distributed on both sides of the diagonal line of the contrast graph, which indicates that the RBF neural network model employed in this study is promising in learning performance prediction. It is of good reference significance for promoting more accurate and efficient learning performance prediction and improving the efficiency and effectiveness of education quality governance. © 2019 Totem Publishers Ltd. All rights reserved.

关键词： Network topology

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