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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

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The application of Deep Reinforcement Learning in Coordinated Control of Nuclear reactors

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Journal of Physics: Conference Series 2021年第1期2113卷

作者： Jing Li Yanyang Liu Xianguo Qing Kai Xiao Ying Zhang Pengcheng Yang Yue (Marco) Yang Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China Department of Engineering The University of Sydney Sydney 2006 Australia

The nuclear reactor control system plays a crucial role in the operation of nuclear power plants. The coordinated control of power control and steam generator level control has become one of the most important control problems in these systems. In this paper, we propose a mathematical model of the coordinated control system, and then transform it into a reinforcement learning model and develop a deep reinforcement learning control algorithm so-called DDPG algorithm to solve the problem. Through simulation experiments, our proposed algorithm has shown an extremely remarkable control performance.

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Impact Time and Angle Control Guidance Based on Cubic Bézier Curves: Theory and Experiment

SSRN

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SSRN 2024年

作者： Li, Heng Lv, Xiaokang Yu, Jianglong Zhang, Kun Wang, Yanyan Dong, Xiwang Institute of Artificial Intelligence Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China School of Astronautics Beihang University Beihang University Beijing100191 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

A trajectory-shaping guidance strategy with constraints on impact time, impact angle, and maximum lateral acceleration is proposed based on a cubic Bézier curve fitting method. Firstly, an optimal guidance model is formulated, in which the Bézier curves are employed to fit the guidance trajectory, ensuring compliance with the required constraints. The manipulation transforms the optimal guidance problem into an optimal Bézier guidance trajectory design problem and a trajectory tracking problem. Then, by parameterizing the Bézier curves and analyzing the trajectory constraints in detail, the optimal Bézier guidance trajectory design is transformed into optimizing the control parameters of the Bézier curves. Moreover, a multi-layer zeroth-order Adam trajectory optimization algorithm based on general and fine tuning is introduced to determine the control parameters without relying on gradient information. Subsequently, a hyperbolic tangent vector field path tracking guidance algorithm is proposed to follow the designed trajectory and fulfill the guidance missions. Finally, numerical simulations and equivalent physical experiments verify the effectiveness of the proposed guidance strategy. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Effects of land use and dissolved organic matter on pCO2 are dependent on stream orders and hydrological seasonality in a low-order karst river

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science of the Total Environment 2024年 957卷 177502页

作者： Ma, Yongmei Ni, Maofei Gu, Shijie Zhang, Liuqing Sun, Huaiwei Tang, Wei Li, Siyue Key Laboratory of Reservoir Aquatic Environment Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Eco-Environmental Engineering Guizhou Minzu University Guiyang550025 China Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University Shenzhen China School of Civil and Hydraulic Engineering Huazhong University of Science and technology Wuhan430074 China Administration office for Wu Shan Dachang lake national wetland park Chongqing404700 China School of Environmental Ecology and Biological Engineering Institute of Changjiang Water Environment and Ecological Security Hubei Key Laboratory of Novel Reactor and Green Chemical Technology Wuhan Institute of Technology Wuhan430205 China

Human activities and stream accumulation influence carbon loadings, altering the distributions and characteristics of dissolved inorganic and organic carbon in rivers. It is widely recognized that such alterations affect dissolved organic matter (DOM) components, water environment and river carbon dioxide (CO2) degassing, however, the control factors by which land use/land cover (LULC) and DOM components regulate the partial pressure of CO2 (pCO2) are unclear. Here, in the Daning karst river system, an extensive investigation was presented to investigate the role of LULC and DOM components in influencing the spatial and temporal variability of pCO2, as well as to investigate the regulating effect of stream order and hydrological rhythm on this influence. DOM quality and pCO2 levels exhibited significant spatial and temporal variations. In the 3rd – 4th order streams, pCO2 was correlated with protein and lignin compounds in the wet period and with DOM molecular weight in the dry period. Relatively high protein-like components (54.83 % ∼ 71.84 %, on average) and biological index (0.86–0.90, on average) indicated notable autochthonous processes. Significant relationships between pCO2 and water quality parameters were observed in the 3rd – 4th order streams in the wet period, demonstrating the role of runoff and upstream accumulation. Farmland increased pCO2 levels in the 3rd – 4th order streams, whereas forests could potentially mitigate river CO2 saturation. River pCO2 was well predicted by LULC under extended circular buffers (1000 and 2000 m in diameter). This study demonstrated that DOM and LULC directly or indirectly affect pCO2 and that the influences are largely regulated by hydrological seasonality and stream orders, which is better for understanding aquatic CO2 drivers. © 2024 Elsevier B.V.

关键词： Carbon sequestration

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Self-biased magnetoelectric switching at room temperature in three-phase ferroelectric-antiferromagnetic-ferrimagnetic nanocomposites (vol 4, pg 333, 2021)

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NATURE ELECTRONICS 2021年第6期4卷 448-448页

作者： Wu, Rui Zhang, Di Maity, Tuhin Lu, Ping Yang, Jie Gao, Xingyao Zhao, Shishun Wei, Xiucheng Zeng, Hao Kursumovic, Ahmed Tian, Guang Li, Weiwei Yun, Chao Wang, Yongqiang Ren, Zengyao Zhou, Ziyao Liu, Ming Zhang, Kelvin H. L. Jia, Quanxi Yang, Jinbo Wang, Haiyan MacManus-Driscoll, Judith L. Department of Materials Science & Metallurgy University of Cambridge Cambridge UK Materials Engineering Purdue University West Lafayette IN USA Sandia National Laboratories Albuquerque NM USA State Key Laboratory for Artificial Microstructure and Mesoscopic Physics School of Physics Peking University Beijing People’s Republic of China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education & International Center for Dielectric Research Xi’an Jiaotong University Xi’an People’s Republic of China Department of Physics University at Buffalo—the State University of New York Buffalo NY USA Materials Science and Technology Division Los Alamos National Laboratory Los Alamos NM USA School of Materials Science and Engineering University of Science and Technology Beijing Beijing People’s Republic of China State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Xiamen University Xiamen People’s Republic of China Department of Materials Design and Innovation University at Buffalo—the State University of New York Buffalo NY USA

Magnetoelectric systems could be used to develop magnetoelectric random access memory and microsensor devices. One promising system is the two-phase 3-1-type multiferroic nanocomposite in which a one-dimensional magnetic column is embedded in a three-dimensional ferroelectric matrix. However, it suffers from a number of limitations including unwanted leakage currents and the need for biasing with a magnetic field. Here we show that the addition of an antiferromagnet to a 3-1-type multiferroic nanocomposite can lead to a large, self-biased magnetoelectric effect at room temperature. Our three-phase system is composed of a ferroelectric Na0.5Bi0.5TiO3matrix in which ferrimagnetic NiFe2O4nanocolumns coated with antiferromagnetic p-type NiO are embedded. This system, which is self-assembled, exhibits a magnetoelectric coefficient of up to 1.38 × 10–9s m–1, which is large enough to switch the magnetic anisotropy from the easy axis (Keff= 0.91 × 104J m–3) to the easy plane (Keff= –1.65 × 104J m–3).

关键词： Nanocomposites

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Zero-Forcing Based Downlink Virtual MIMO-NOMA Communications in IoT Networks

arXiv

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arXiv 2022年

作者： Shi, Zheng Wang, Hong Fu, Yaru Yang, Guanghua Ma, Shaodan Hou, Fen Tsiftsis, Theodoros A. Institute of Physical Internet The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Information Systems Technology and Design Singapore University of Technology and Design Singapore The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China

To support massive connectivity and boost spectral efficiency for internet of things (IoT), a downlink scheme combining virtual multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to de-multiplex the input data streams. Only statistical channel state information (CSI) is available at base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed-form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with higher transmission rate under benign channel conditions. © 2022, CC BY.

关键词： Internet of things

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New method of precise measurement for radial rotation motion error

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Jiliang Xuebao/Acta Metrologica Sinica 2006年第SUPPL.期27卷 80-84页

作者： Lei, Xian-Qing Li, Yan Li, Ji-Shun Ma, Wei Xue, Yu-Jun Cui, Zhi-Zhong School of Machinery and Precision Instrument Engineering Xi'an University of Technology Xi'an 710048 China Henan Key Laboratory of Modern Mechanical Design and Transmission System Luoyang 471003 China Henan Key Laboratory of Advanced Manufacturing Technology Luoyang 471003 China School of Mechatronics Engineering Henan University of Science and Technology Luoyang 471003 China

The roundness error and rotation motion error of the least square center of measured section are separated by applying the roundness error separation principle based on the three-point method. Furthermore, the relationship between the pure rotation motion error and the initial position(linear eccentricity) of the least square center of measured section is described. The mathematical expression for separating the pure rotation motion error is put forward, in which the orthogonality of the trigonometric function and the above-obtained relationship is used. The experimental result indicates that the precision measurement of the radial rotation motion error of the gyro-axle can be achieved by three-point method.

关键词： Error separation technique(EST) Metrology Rotation motion Three-point method

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Effect Analysis of Microwave Power Transmitting Background Noise on Pilot Signal Reception

Effect Analysis of Microwave Power Transmitting Background N...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Shi-Wei Dong Ying Wang Yazhou Dong Feng Gao Weiwei Wang Yafeng Li Qiangli Xi Qiaoshan Zhang Zhengjun Li National Key Lab. of Science and Technology on Space Microwave CAST(Xi'an) Xi'an China Shaanxi Key Laboratory of Space Solar Power Station System School of Mechano-Electronic Engineering Xi'an China Deep Space Exploration Lab. Hefei China R&D and Marketing Department China Academy of Space Technology Beijing China Department of Antenna Engineering CAST(Xi'an) Xi'an China China Academy of Space Technology (Xi'an) Xi'an China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

The reception and process of the pilot signal determines the beam steering accuracy in retro-directive microwave power transmission (MPT) systems. The high power transmitting array presented high background noise level to the pilot signal receiver and thus reduced the signal-noise-ratio (SNR). Analysis method was put forward on the effect investigation. And a study case was discussed in this short paper, and the background noise effect was evaluated with respect to phase detection margin. This work addresses a fundamental problem and is expected to contribute to retro-directive MPT system design.

关键词： Microwave integrated circuits Direction-of-arrival estimation Power transmission Receivers Microwave theory and techniques Background noise Microwave FET integrated circuits

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Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

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Physical Review C 2022年第3期105卷 034611-034611页

作者： X. Zhang (张鑫) X. Liu (刘星泉) Y. Huang (黄宇) W. Lin (林炜平) H. Zheng (郑华) R. Wada A. Bonasera Z. Chen (陈志强) L. Chen (陈蕾) J. Han (韩纪锋) R. Han (韩瑞) M. Huang (黄美容) Q. Hu (胡强) Q. Leng (冷强钟) C. W. Ma (马春旺) G. Qu (曲国峰) P. Ren (任培培) G. Tian (田国玉) Z. Xu (徐子虚) Z. Yang (杨振雷) L. Zhang (张丽娜) Key Laboratory of Radiation Physics and Technology of the Ministry of Education Sichuan University Chengdu 610064 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710119 China Cyclotron Institute Texas A&M University College Station Texas 77843 USA Laboratori Nazionali del Sud INFN via Santa Sofia 62 95123 Catania Italy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China College of Physics and Electronics information Inner Mongolia University for Nationalities Tongliao 028000 China School of Physics Henan Normal University Xinxiang 453007 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China School of Information Sciences and Engineering Lanzhou University Lanzhou 730000 China

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact-parameter-sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact-parameter determination using the deep CNN method, compared with using conventional methods. This result reveals the importance of selecting a reliable impact-parameter-determination method in the experimental deduction of the nuclear stopping power as well as other observables.

关键词： Low & intermediate energy heavy-ion reactions Convolutional neural networks Machine learning Nuclear data analysis & compilation

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Robust guided image filtering

arXiv

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arXiv 2017年

作者： Liu, Wei Chen, Xiaogang Shen, Chunhua Yu, Jingyi Wu, Qiang Yang, Jie Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China College of Communication and Art Design University of Shanghai for Science and Technology Shanghai200093 China School of Computer Science University of Adelaide AdelaideSA5005 Australia College of Engineering University of Delaware NewarkDE19176 United States School of Computing and Communications University of Technology SydneyNSW2007 Australia

The process of using one image to guide the filtering process of another one is called Guided Image Filtering (GIF). The main challenge of GIF is the structure inconsistency between the guidance image and the target image. Besides, noise in the target image is also a challenging issue especially when it is heavy. In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above. The data term makes our model simultaneously denoise the target image and perform GIF which is robust against the heavy noise. The smoothness term is able to make use of the property of both the guidance image and the target image which is robust against the structure inconsistency. While the resulting model is highly non-convex, it can be solved through the proposed Iteratively Re-weighted Least Squares (IRLS) in an efficient manner. For challenging applications such as guided depth map upsampling, we further develop a data-driven parameter optimization scheme to properly determine the parameter in our model. This optimization scheme can help to preserve small structures and sharp depth edges even for a large upsampling factor (8× for example). Moreover, the specially designed structure of the data term and the smoothness term makes our model perform well in edge-preserving smoothing for single-image tasks (i.e., the guidance image is the target image itself). It performs well in several challenging applications in avoiding halos, gradient reversals and properly preserving edges with noise/texture being well smoothed. Through extensive experimental results, we show that the proposed RGIF can have promising performance in many applications such as guided depth map upsampling, flash/no flash filtering, detail enhancement, HDR tone mapping, structure smoothing and clip-art JPEG compression artifact removal. This paper is an extension of our previous work [1], [2]. Copyright © 2017, T

关键词： Image enhancement

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