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Optimized online filter stack spectrometer for ultrashort X-ray pulses

Nuclear science and Techniques 2024年第5期35卷 85-98页

作者： Jia-Xing Wen Ge Ma Ming-Hai Yu Yu-Chi Wu Yong-Hong Yan Shao-Yi Wang Huai-Zhong Gao Lu-Shan Wang Yu-Gang Zhou Qiang Li Yue Yang Fang Tan Xiao-Hui Zhang Jie Zhang Wen-Bo Mo Jing-Qin Su Wei-Min Zhou Yu-Qiu Gu Ming Zeng Zong-Qing Zhao Science and Technology on Plasma Physics Laboratory Laser Fusion Research CenterCAEPMianyang 621900China Department of Engineering Physics Tsinghua UniversityBeijing 100084China Key Laboratory of Particle and Radiation Imaging(Tsinghua University) Ministry of EducationBeijing 100084China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China

Currently,with the advent of high-repetition-rate laser-plasma experiments,the demand for online diagnosis for the X-ray spectrum is increasing because the laser-plasma-generated X-ray spectrum is very important for characterizing electron dynamics and *** this study,scintillators and silicon PIN(P-type–intrinsic-N-type semiconductor)diodes were used to construct a wideband online filter stack *** X-ray sensor and filter arrangement was optimized using a genetic algorithm to minimize the condition number of the response ***,the unfolding error was significantly reduced based on numerical *** detector responses were quantitatively calibrated by irradiating the scintillator and PIN diode with various nuclides and comparing the measuredγ-ray peaks.A prototype 15-channel spectrometer was developed by integrating an X-ray detector with front-and back-end *** prototype spectrometer could record X-ray pulse signals at a repetition rate of 1 ***,an optimized spectrometer was employed to record the real-time spectra of laser-driven bremsstrahlung *** optimized spectrometer offers a compact solution for spectrum diagnostics of ultrashort X-ray pulses,exhibiting improved accuracy in terms of spectrum measurements and repetition rates,and could be widely used in next-generation high-repetition-rate high-power laser facilities.

关键词： Filter stack spectrometer Laser plasma diagnostics X-ray diagnostics Scintillator PIN diode

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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

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Computer systems science & engineering 2023年第11期47卷 2707-2724页

作者： Xinfei Li Xiaolan Xie Yigang Tang Qiang Guo Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006China School of Information Science and Engineering Guilin University of TechnologyGuilin541006China

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource *** proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource *** variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion *** permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of ***,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization *** Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.

关键词： Cloud resource prediction variational modal decomposition permutation entropy long and short-term neural network stacking integration

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Mn-based MXene with high lithium-ion storage capacity

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Nano Research 2024年第5期17卷 4181-4191页

作者： Yanyan Wu Dongqing Liu Xiaonan Wang Usman Ghani Muhammad Asim Mushtaq Jinfeng Yang Huarui Sun Panagiotis Tsiakaras Xingke Cai School of Science and Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System Harbin Institute of Technology ShenzhenShenzhen 5180552 China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China Institute for Advanced Study Shenzhen UniversityShenzhen 518060China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Laboratory of Alternative Energy Conversion Systems Department of Mechanical EngineeringSchool of EngineeringUniversity of ThessalyPedion Areos 38834Greece

3d-transition metal(Fe,Co,Ni,and Mn)-based MXene materials have been predicted to demonstrate exceptional electrochemical performance because of their good electrical conductivity and the presence of metallic atoms with multiple charge ***,until now,there have been no reports on MXenes based on Fe,Co,Ni,and Mn,due to the lack of 3d-metal-layered ***,we successfully synthesized the first 3d-transition metal-based MXenes,Mn_(2)CT_(x) by exfoliating a layered precursor derived from the anti-perovskite bulk *** as-prepared Mn_(2)CT_(x) MXene nanosheets were employed as anode materials in lithium-ion batteries,which exhibited stable storage capacity of 764.7 mAh·g^(-1) at 0.5 C,placing its storage capacities at an upper-middle level compared with other reported MXene materials as well as other Mn-based anode ***,this study opens a new avenue for MXene research by synthesizing 3d-transition metal-based MXenes for electrochemical applications.

关键词： Mn_(3)GaC Mn_(2)CT_(x) MXenes lithium-ion battery anode materials negative fading capacity

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Programmable array antenna based on nematic liquid crystals for the Ka-band

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Journal of Southeast University(English Edition) 2025年第1期41卷 78-83页

作者： WANG Qiang KE Junchen BAI Lin Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic TechnologyGuilin 541004China School of Optoelectronic Engineering Guilin University of Electronic TechnologyGuilin 541004China School of Information Science and Engineering Southeast UniversityNanjing 211189China

A programmable low-profile array antenna based on nematic liquid crystals(NLCs)is *** antenna unit comprises a square patch radiating structure and a tunable NLC-based phase shifter capable of achieving a phase shift exceeding 360°with high ***,the above 64 antenna units are periodically arranged into an 8×8 NLC-based antenna array,and the bias voltage of the NLC-based phase shifter loaded on the antenna unit is adjusted through the control of the field-programmable gate array(FPGA)programming *** configuration enables precise phase changes for all 64 *** simulation,sample processing,and experimental measurements of the antenna array are conducted to validate the performance of the *** numerical and experimental results demonstrate that the proposed antenna performs well within the frequency range of 19.5-20.5 GHz,with a 3 dB relative bandwidth of 10%and a maximum main lobe gain of 14.1 dBi.A maximum scanning angle of±34°is achieved through the adjustment of the FPGA programming *** NLC-based programmable array antenna shows promising potential for applications in satellite communication.

关键词： array antenna nematic liquid crystals electronically beam scanning field programmable gate array(FPGA)

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Sensitivity-Enhanced Hot-Wire Anemometer by Using Cladding-Etched Fiber Bragg Grating

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Photonic Sensors 2023年第3期13卷 1-7页

作者： Yuhan TANG Xuke CHEN Jiarui ZHANG Dajuan LV Liangming XIONG Xinyong DONG School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangzhou 510006China Institute of Optoelectronic Technology China Jiliang UniversityHangzhou 310018China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Wuhan 430073China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai 519000China

A sensitivity-enhanced hot-wire anemometer based on a cladding-etched optical fiber Bragg grating(FBG)coated with a layer of silver film and optically heated by using a 1480nm laser diode is *** silver film absorbs the laser power to heat the FBG to a certain high temperature and the airflow cools down the FBG hot-wire with the cooling effect and hence the Bragg wavelength of the FBG is determined by the airflow *** measurement results show that the heating efficiency of the FBG hot wire is improved by 3.8times in magnitude by etching the fiber cladding from 125μm down to 73.4μm,and the achieved airflow velocity sensitivities,under a laser power of 200mW,are–3180pm/(m/s),–889pm/(m/s),–268pm/(m/s),and–8.7pm/(m/s)at different airflow velocities of 0.1m/s,0.5m/s,1.5m/s,and 17m/s,*** comparison,the sensitivities are only–2193 pm/(m/s),–567 pm/(m/s),–161 pm/(m/s),and–4.9pm/(m/s)for the reference anemometer without cladding etching even at a much higher heating laser power of *** results prove that the method by using a cladding-etched FBG to improve sensitivity of FBG-based hot-wire anemometers works and the sensitivity is improved significantly.

关键词： Fiber Bragg grating hot-wire anemometer optical fiber sensors

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Physics-Based Predictions of the Month-by-Month Summer Western North Pacific Anomalous Anticyclone

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Journal of Climate 2025年第10期38卷 2187-2203页

作者： Zhu, Zhiwei Yang, Ying Liu, Boqi State Key Laboratory of Climate System Prediction and Risk Management Key Laboratory of Meteorological Disaster Ministry of Education Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China

This study identified the distinct predictors of the western North Pacific anomalous anticyclone (WNPAC) in June, July, and August and revealed their independent influencing mechanisms on the formation of the summer WNPAC in each corresponding month. For the June WNPAC (AC_Jun), the North Atlantic warming (NAW) in April–May induces subsequently enhanced convection over the Indian Ocean and suppressed the convection over the tropical central Pacific, leading to AC_Jun via the Gill-type response. Meanwhile, the warming tendency over the western North Pacific from February to April, which reflects the slow decay of El Niño, results in AC_Jun through Indian Ocean warming. For the July WNPAC (AC_Jul), the cooling tendency from April to June over the central and eastern tropical Pacific is the sole predictor, which results in AC_Jul through the wind–evaporation–SST feedback. For the August WNPAC (AC_Aug), the tropical Atlantic warming (TAW) in June and July and the Bering Strait sea ice slow melting (BSSM) from April to May are the two predictors. While TAW has a similar influence on AC_Aug to that of NAW on AC_Jun, BSSM makes for AC_Aug by strengthening the westerly jet and subsequently atmosphere–ocean coupling over the North Pacific. Using these predictors, three physics-based empirical models (PEMs) for the seasonal predictions of AC_Jun, AC_Jul, and AC_Aug are conducted, which all outperform the National Centers for Environmental Prediction Climate Forecast system (CFS), shedding light on improving the seasonal prediction of the East Asian summer climate. © 2025 American Meteorological Society.

关键词： Heat flux

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