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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

Stabilization of continuous-time Markov/semi-Markov jump linear systems via finite data-rate feedback

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arXiv 2021年

作者： Wang, Jingyi Feng, Jianwen Xu, Chen Wu, Xiaoqun Lü, Jinhu College of Mathematics and Statistics Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan China State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This paper investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback control. Different from previous works, the sampling times and the jump times are independent of each other in this paper. The quantization is recursively adjusted on the sampling time, and its updating strategy does not depend on the switching in a sampling interval. In other words, the explicit value of the switching signal in a sampling interval is not necessary. The numerically testable condition is developed to ensure almost sure exponential stabilization of MJLSs under the proposed communication and control protocols. We also drop the assumption of stabilizability of all individual modes required in previous works about the switched systems. Moreover, we extend the result to the case of continuous-time semi-Markov jump linear systems (semi-MJLSs) via the semi-Markov kernel approach. Finally, some numerical examples are presented to illustrate the effectiveness on stabilization of the proposed communication and control *** Codes 93D15 © 2021, CC BY.

关键词： Markov processes

Machine Learning-Aided Quantitative Detection of Two Mixed Antibiotics in a Narrow Concentration Range Based on Oxygen Incorporation-Induced 3d Sers Substrates

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SSRN

SSRN 2024年

作者： Ye, Weichun Tan, Lin Luo, Juanjuan Liu, Qianqian Wang, Morui Li, Yuee Li, Jian Yu, Jing Dong, Chunxu Xu, Tailin State Key Laboratory of Applied Organic Chemistry College of Chemistry and Chemical Engineering Lanzhou University Lanzhou730000 China School of mathematics and statistics Lanzhou University Lanzhou730000 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China Lanzhou Institutes for Food and Drug Control Lanzhou730000 China School of Physics and Electronics Shandong Normal University Jinan250014 China School of Biomedical Engineering Shenzhen University Medical School Shenzhen518060 China

Amoxicillin (AMO) and ciprofloxacin (CIP) as frequently-used antibiotics are often simultaneously taken for anti-infection to protect pig and poultry production, while their effective therapeutic window is narrow. How to quantitatively detect the two antibiotics mixed in a narrow concentration range is essential to ensure their treatment efficacy and food safety. In this study, quantitative detection of AMO and CIP mixed in animal flesh in one order of magnitude concentration range was performed using a triple SERS signal amplification sensor combined with machine learning. The SERS sensor based on oxygen incorporation (Oi)-induced 3D NiO nanoflowers (NFs) coated with Ag nanoparticles included 3D hot spot effect from strong internal electromagnetic couple in NiO NFs, the Oi effect of NiO and synergistic charge transfer effect of between NiO and Ag. Based on the three effects, sensitive SERS detection of AMO and CIP was made with the limits of detection of 1 nM and 0.1 μM, whereas broad linear ranges were obtained. Through the use of machine learning techniques like principal component analysis and partial least square regression, qualitative and quantitative detection of AMO and CIP in their mixture from 1.33 μM to 8.69 μM was successfully achieved, where the average proportion of absolute prediction error (p ≤ 0.15) for concentration was 98.06%. © 2024, The Authors. All rights reserved.

关键词： Antibiotics

Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks

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arXiv 2023年

作者： Wang, Zhicheng Meng, Xuhui Jiang, Xiamo Xiang, Hui Karniadakis, George Laboratory of Ocean Energy Utilization of Ministry of Education School of Energy and Power Engineering Dalian University of Technology China Institute of Interdisciplinary Research for Mathematics and Applied Science School of Mathematics and Statistics Huazhong University of Science and Technology China School of Energy and Power Engineering State Key Lab of Structural Analysis Optimization and CAE Software for Industrial Equipment Research Institute for Carbon Neutrality Dalian University of Technology China Research Institute for Carbon Neutrality Dalian University of Technology China Division of Applied Mathematics School of Providence RI United States

Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for fluid flows and are especially effective for inverse and hybrid problems. However, vanilla PINNs often fail in forward problems, especially at high Reynolds (Re) number flows. Herein, we study systematically the classical lid-driven cavity flow at Re = 2, 000, 3, 000 and 5, 000. We observe that vanilla PINNs obtain two classes of solutions, one class that agrees with direct numerical simulations (DNS), and another that is an unstable solution to the Navier-Stokes equations and not physically realizable. We attribute this solution multiplicity to singularities and unbounded vorticity, and we propose regularization methods that restore a unique solution within 1% difference from the DNS solution. In particular, we introduce a parameterized entropy-viscosity method as artificial eddy viscosity and identify suitable parameters that drive the PINNs solution towards the DNS solution. Furthermore, we solve the inverse problem by subsampling the DNS solution, and identify a new eddy viscosity distribution that leads to velocity and pressure fields almost identical to their DNS counterparts. Surprisingly, a single measurement at a random point suffices to obtain a unique PINNs DNS-like solution even without artificial viscosity, which suggests possible pathways in simulating high Reynolds number turbulent flows using vanilla PINNs. © 2023, CC BY-NC-SA.

关键词： Machine learning

Enhanced Thermal Performance of Power Module with Integrated AlN Ceramic Heatsink

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Enhanced Thermal Performance of Power Module with Integrated...

Power Electronics and Application Symposium (PEAS), IEEE International

作者： Liang Wang Mingyu Xu Miaomiao Li Jiakun Gong Senhao Liang Jiawei Zhang Zheng Zeng State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing China College of Mathematics and Statistics Chongqing University Chongqing China School of Foreign Languages and Cultures Chongqing University Chongqing China Substation Maintenance Center EHV Company of State Grid Jilin Electric Power Co. Ltd. Changchun Changchun China

Enhanced thermal performance is increasingly vital for the power module in high capacity application. However, the presence of multiple layers and high thermal resistance in the power module impedes the reduction of junction temperature. In this paper, the concept of power module with an integrated aluminum nitride (AIN) ceramic heatsink is proposed to achieve low thermal resistance packaging. The thermal simulation results demonstrate a significant 64% reduction in thermal resistance between the traditional power module and the power module with an AlN ceramic heatsink. Besides, the detail and straightforward manufacturing process of the ceramic heatsink is introduced. The junction temperature between AlN ceramic heatsink and conventional power module under different voltage, current, output power and fluid velocity are comprehensive compared in the experiment. It is observed that a maximum reduction of 37°C in junction temperature is achieved, the maximum junction temperature difference between the traditional and ceramic heatsink power modules is respectively 42°C and 18°C. The enormous advantages of the power module with an integrated AlN ceramic heatsink in thermal enhancement is verified.

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Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning

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arXiv 2025年

作者： Wang, Xiaoyang Zhang, Chengqian Wang, Zhenyu Liu, Hanyu Lv, Jian Wang, Han Weinan, E. Ma, Yanming National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China HEDPS CAPT College of Engineering Peking University Beijing100871 China AI for Science Institute Beijing100080 China Center for Machine Learning Research Peking University Beijing100871 China School of Mathematical Sciences Peking University Beijing100871 China

The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant bottleneck for both experimental and theoretical investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be well addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal structure exploration, physics-informed screening, and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-Tc superconductors with predicted Tc > 200 K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-Tc hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary hydrides. © 2025, CC BY.

关键词： High temperature superconductors

Automatic Depth Optimization for Quantum Approximate Optimization Algorithm

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arXiv 2022年

作者： Pan, Yu Tong, Yifan Yang, Yi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Mathematics and Statistics McGill University MontrealQCH3A 0B9 Canada

Quantum Approximate Optimization Algorithm (QAOA) is a hybrid algorithm whose control parameters are classically optimized. In addition to the variational parameters, the right choice of hyperparameter is crucial for improving the performance of any optimization model. Control depth, or the number of variational parameters, is considered as the most important hyperparameter for QAOA. In this paper we investigate the control depth selection with an automatic algorithm based on proximal gradient descent. The performances of the automatic algorithm are demonstrated on 7-node and 10-node Max-Cut problems, which show that the control depth can be significantly reduced during the iteration while achieving an sufficient level of optimization accuracy. With theoretical convergence guarantee, the proposed algorithm can be used as an efficient tool for choosing the appropriate control depth as a replacement of random search or empirical rules. Moreover, the reduction of control depth will induce a significant reduction in the number of quantum gates in circuit, which improves the applicability of QAOA on Noisy Intermediate-scale Quantum (NISQ) devices. © 2022, CC BY.

关键词： Optimization

Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting

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arXiv 2022年

作者： Xing, Wei W. Zhang, Ziyang Shah, Akeel A. Key Laboratory of Low-grade Energy Utilisation Technologies and Systems MOE Chongqing University Chongqing400030 China School of Mathematics and Statistics University of Sheffield Hounsfield Road SheffieldS3 7RH United Kingdom College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health during repeated cycling. To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems. Unless multiple battery data sets are used for a direct prediction of the end-of-life, which is useful for ball-park estimates, such an approach is infeasible since the features are not known for future cycles. In this paper, we develop a highly-accurate method that can overcome this limitation, by using a modified Gaussian process dynamical model (GPDM). We introduce a kernelised version of GPDM for a more expressive covariance structure between both the observable and latent coordinates. We combine the approach with transfer learning to track the future state-of-health up to end-of-life. The method can incorporate features as different physical observables, without requiring their values beyond the time up to which data is available. Transfer learning is used to improve learning of the hyperparameters using data from similar batteries. The accuracy and superiority of the approach over modern benchmarks algorithms including a Gaussian process model and deep convolutional and recurrent networks is demonstrated on three data sets, particularly at the early stages of battery lifetime. Copyright © 2022, The Authors. All rights reserved.

关键词： Lithium-ion batteries

An integrated framework for accelerating reactive flow simulation using GPU and machine learning models

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arXiv 2023年

作者： Mao, Runze Wang, Yingrui Zhang, Min Li, Han Xu, Jiayang Dong, Xinyu Zhang, Yan Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Beijing100080 China Shanghai SenseTime Intelligent Technology Co. LTD Shanghai200233 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU. © 2023, CC BY.

关键词： Graphics processing unit