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arXiv 2024年

作者： Li, Maochun Gao, Shoufei Sun, Yizhi Zhao, Xiaoming Luo, Wei Hu, Qingbo Chen, Hao Wu, Helin Hui, Fei Wang, Yingying Yan, Miao Ding, Wei Tianjin Key Laboratory of Quantum Precision Measurement Technology Tianjin Navigation Instruments Research Institute Tianjin300131 China Guangdong Provincial Key Laboratory of Optical Fibre Sensing and Communication Institute of Photonics Technology Jinan University Guangzhou510632 China College of Physics & Optoelectronic Engineering Jinan University Guangzhou510632 China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China Co. Ltd. Jiangsu226010 China Peng Cheng Laboratory Shenzhen518055 China

We present a groundbreaking navigation-grade interferometric air-core fibre optic gyroscope (IFOG) using a quadrupolar-wound coil of four-tube truncated double nested antiresonant nodeless fibre (tDNANF). This state-of-the-art tDNANF simultaneously achieves low loss, low bend loss, single-spatial-mode operation, and exceptional linear polarization purity over a broad wavelength range. Our 469 m tDNANF coil demonstrated a polarization extinction ratio (PER) of ~20 dB when illuminated by an amplified spontaneous emission (ASE) source spanning 1525-1565 nm. Under these conditions, the gyro archives an angular random walk (ARW) of 0.0038 deg h-1/2 and a bias-stability (BS) drift over 8500 s of 0.0014 deg h-1, marking the first instance of navigation-grade performance in air-core FOGs. Additionally, we validated the low thermal sensitivity of air-core FOGs, with reductions of 9.24/10.68/6.82 compared to that of conventional polarization-maintaining solid-core FOGs of the same size across various temperature ranges. These results represent a significant step towards long-standing promise of high-precision inertial navigation applications with superior environmental adaptability. Copyright © 2024, The Authors. All rights reserved.

关键词： Gyroscopes

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A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

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Computer Modeling in engineering & Sciences 2020年第11期125卷 579-596页

作者： Xing Deng Haijian Shao Liang Shi Xia Wang Tongling Xie School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China Department of Electrical and Computer Engineering National University of Singapore117583Singapore The People’s Hospital of RUGAO Nantong226500China

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff *** of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation *** this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of *** benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

关键词： COVID-19 detection deep learning transfer learning pre-trained models

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MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE TRANSFER

MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE T...

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GLOBECOM 2022 - 2022 IEEE Global communications Conference

作者： Huaji Zhou Xu Wang Jing Bai Zhu Xiao Science and Technology on Communication Information Security Control Laboratory Jiaxing China School of Artificial Intelligence Xidian University Xi'an China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781665435413

Deep learning-based recognition of radio signal modulation has emerged as a current research hotspot with significant practical potential. However, in practical applications, radio modulation signal data acquisition is complicated to obtain, and label samples are costly and time-consuming to meet the data dependence of deep learning. Transfer learning allows pretrained networks to be reused on large-scale datasets, making it a kind of solution for modulation signal recognition in limited data. The method of suppressing small singular values in the feature vector is employed in this paper to realize selective knowledge transfer for modulation signal recognition, while stochastic normalization is employed to replace the batch normalization layer to avoid over-fitting. We tested the stochastic normalized selective knowledge transfer method on the RML2016.10A and RML2016.04C datasets, with an SNR of 6dB signal samples, and found that it can lead to average growth of 15.77% and 10.32% when compared to direct training, and 6.1% and 2.73% when compared to vanilla fine-tuning. In addition, we check up under a variety of SNR conditions to ensure that our method is effective.

关键词： Training Deep learning Transfer learning Data acquisition Modulation Global communication Knowledge transfer

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A Face Mask Detection Model Based on Improved Genetic Algorithm-Optimized Convolution Neural Network in Covid-19 Pandemic

SSRN

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SSRN 2023年

作者： Liu, Lulin Wang, Xu Du, Sheng School of Future Technology China University of Geosciences Wuhan430074 China College of Communication Engineering Jilin University Changchun130000 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Wearing masks is an essential means of ensuring public safety under the pandemic of COVID-19. Detecting whether people wear masks correctly is vital for preventing and controlling new crown epidemics. In response to the poor effectiveness of current basic deep learning methods for mask-wearing detection, this paper proposes a convolutional neural network optimized by an improved genetic algorithm to detect face masks. An improved genetic algorithm is used to optimize the parameters in the convolutional neural network, and then the effect is compared with those of other papers. The result shows that the proposed method is more effective in detecting the wearing of masks with a correct rate of up to 99.125% and can provide practical help and guidance for preventing and controlling the COVID-19 epidemic. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Multi-segment Bidding Behaviors of Power Units: Price-capacity-pair Analysis and Modeling 36

Multi-segment Bidding Behaviors of Power Units: Price-capaci...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Hou, Shuangshuang Wang, Ying Li, Lili Zhao, Jing Ning, Jia Zhang, Kaifeng Southeast University College of Software Suzhou China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China State Grid Electric Power Research Institute Nari Group Corporation Nanjing China Nanjing Institute of Technology School of Electric Power Engineering Nanjing China

ISBN: (纸本)9781665437127

With the deregulation of the power market, power units are allowed to make strategic biddings. The bidding results of the strategic bidding market are substantially different from the ones in the previous regulated market. Such differences in the bidding results will further have impacts on the power flow and system security. Thus, it is important to analyze and establish a model to simulate the bidding behaviors of the power units. This paper proposes a price-capacity-pairs model to simulate multi-segment bidding behavior. We select five vital factors that affect biddings behaviors as input: load, capacity, supply-demand ratio, bidders' bidding tendency and unit type. Based on the three-segment bidding rule, the price-capacity pair is used. For uncertain price or capacity, we choose a truncated normal distribution model. Scenario generation and reduction are achieved by Monte Carlo simulation and fuzzy C-means clustering algorithm. We apply our method to a case study based on Guangdong's market in China. The simulation of wind power units, coal power units and gas power units has verified the correctness of the model. © 2021 IEEE.

关键词： Power markets

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A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Network

A Spectrum Sensing Method Based on CNN-LSTM Deep Neural Netw...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Shujian Zhang Zhan Xu Lu Tian Xiaolong Yang Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665405836

Spectrum sensing can effectively improve the low utilization of spectrum resources and is one of the crucial components of cognitive radio networks. This paper proposes a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network cascaded spectrum sensing model. The model uses CNN to analyze the Short-Time Fourier transform spectrogram of the blind signal. Then the generated feature vector or feature map is passed to the LSTM according to the timestamp. Finally, it detects a signal in a specific spectrum and classifies the signal type to identify multiple signals accurately. The neural network model improves the detection probability by simultaneously acquiring the spatial and temporal characteristics of the blind signal. The experimental results show that the method in this paper can detect a variety of signals with higher detection probability within a wide range of SNR, especially under the condition of low SNR.

关键词： Deep learning Fourier transforms Simulation Conferences Neural networks Sensors Convolutional neural networks

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Adaptive Hybrid Potential Evapotranspiration (Pet) Prediction Method Based on Automatic Machine Learning

SSRN

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SSRN 2024年

作者： Zhang, Chenjia Xu, Tianxin Zhang, Yan Abdu Kaimu, Abullimiti Guo, Lirui Zhang, Yutong Ma, Daokun Xinjiang Agricultural University China College of Information and Electrical Engineering China Agricultural University Haidian District Beijing100089 China School of Yi Language and Culture Xichang University Sichaun Xichang831000 China Beijing Lianchuang Siyuan Measurement and Control Technology Co. Ltd. Beijing Beijing615000 China College of Information Engineering Yangzhou University Hanjiang District Jiangsu Yangzhou225009 China College of Resources and Environment China Agricultural University Haidian District Beijing100094 China

An adaptive hybrid model based on automatic machine learning for short-term PET prediction is proposed coupling neural network and PET formula. 105 standard weather stations in Xinjiang were used as data sets (43 of them had missing data) to test model. According to modules, networks and PET formulas used in the prediction process, corresponding labels were generated in each dataset forecast result. Ratio of training set and test set for each data set was 8:2. Grid search was used to optimize the best hyperparameter combination. In test set, the average absolute error (MAE) and average squared error (MSE) of the model prediction were 0.338mm and 0.270. We prove that the neural network applicability varies among the used data sources, and that GRU and 1 DCNN are more suitable for the selected datasets, while LSTM and MLP are not applicable. We find evidences that applicability of neural networks and PET formulas is independent of geographic region and degree of drought. In 2023, method of rolling prediction for 1-15 days is verified, PET prediction error based on neural network is significantly smaller than useing weather forecast data to calculate PET. In addition, by comparison，we determined that adaptive input length can effectively reduce the prediction error, MAE was 27.52% smaller than fixed input length, and MSE was 45.76% smaller than fixed input length. © 2024, The Authors. All rights reserved.

关键词： Statistical tests

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A Novel Integer Frequency Offset Estimation Method for OFDM Based on Preamble

A Novel Integer Frequency Offset Estimation Method for OFDM ...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Xinxin Liu Zhan Xu Lu Tian Xiaolong Yang Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665405836

Orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Although the existence of integer frequency offset in OFDM will not destroy the orthogonality between subcarriers, it will cause a cyclic shift of the frequency domain data after FFT transformation at the receiving end, which will affect the demodulation of data. In this paper, we propose a novel integer frequency offset estimation method based on the similarity property of adjacent subcarriers of the preamble. This method not only guarantees the original accuracy and performance of the conventional method but also achieves less resource consumption and lower computational complexity.

关键词： OFDM Frequency-domain analysis Estimation Logic gates Frequency conversion Hardware Frequency estimation

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A Unified NOMA Framework in Beam-Hopping Satellite communication Systems

arXiv

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arXiv 2024年

作者： Zhang, Xuyang Yue, Xinwei Li, Tian Han, Zhihao Wang, Yafei Ding, Yong Liu, Rongke The Key Laboratory of Information and Communication Systems Ministry of Information Industry The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The 54th Research Institute of China Electronics Technology Group Corporation Hebei Shijiazhuang050081 China The School of Electronic and Information Engineering Beihang University Beijing100191 China Shenzhen Institution of Beihang University Shenzhen518063 China The Baicells Technologies Co. Ltd Beijing100095 China

This paper investigates the application of a unified non-orthogonal multiple access framework in beam hopping (U-NOMA-BH) based satellite communication systems. More specifically, the proposed U-NOMA-BH framework can be applied to code-domain NOMA based BH (CD-NOMA-BH) and power-domain NOMA based BH (PD-NOMA-BH) systems. To satisfy dynamic-uneven traffic demands, we formulate the optimization problem to minimize the square of discrete difference by jointly optimizing power allocation, carrier assignment and beam scheduling. The non-convexity of the objective function and the constraint condition is solved through Dinkelbach’s transform and variable relaxation. As a further development, the closed-from and asymptotic expressions of outage probability are derived for CD/PD-NOMA-BH systems. Based on approximated results, the diversity orders of a pair of users are obtained in detail. In addition, the system throughput of U-NOMA-BH is discussed in delay-limited transmission mode. Numerical results verify that: i) The gap between traffic requests of CD/PD-NOMA-BH systems appears to be more closely compared with orthogonal multiple access based BH (OMA-BH);ii) The CD-NOMA-BH system is capable of providing the enhanced traffic request and capacity provision;and iii) The outage behaviors of CD/PD-NOMA-BH are better than that of OMA-BH. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite communication systems

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Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints

arXiv

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arXiv 2022年

作者： Wang, Zi Chen, Minglin Guo, Yulan Li, Zhang Yu, Qifeng The College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation Changsha410073 China The School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen518000 China The College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models. To this end, we propose a self-training framework based on the domain-agnostic geometrical constraints. Specifically, we train a neural network to predict the 2D keypoints of a satellite and then use PnP to estimate the pose. The poses of target samples are regarded as latent variables to formulate the task as a minimization problem. Furthermore, we leverage fine-grained segmentation to tackle the information loss issue caused by abstracting the satellite as sparse keypoints. Finally, we iteratively solve the minimization problem in two steps: pseudo-label generation and network training. Experimental results show that our method adapts well to the target domain. Moreover, our method won the 1st place on the sunlamp task of the second international Satellite Pose Estimation Competition. Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

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