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robustness evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

China Communications 2020年第10期17卷 218-228页

作者： Yiping Duan Xiaoming Tao Xijia Liu Ning Ge Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Beijing 100084China

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

关键词： remote-sensing TH data set image feature robustness evaluation

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A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

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Computers, Materials & Continua 2023年第10期77卷 911-940页

作者： Peiyuan Jiang Weijun Pan Jian Zhang Teng Wang Junxiang Huang College of Air Traffic Management Civil Aviation Flight University of ChinaDeyang618307China East China Air Traffic Management Bureau Xiamen Air Traffic Management StationXiamen361015China

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments

关键词： Air traffic control automatic speech recognition conformer robustness evaluation T5 error correction model

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An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry

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Journal of Pharmaceutical Analysis 2021年第1期11卷 88-95页

作者： Zhenhao Li Xiaohui Zhang Jie Liao Xiaohui Fan Yiyu Cheng Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China

Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine.However,this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness.In this work,we proposed a multiple reaction monitoring(MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment.The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards,based on which the large-scale fingerprinting method was efficiently established.This approach was exemplified on QiShenYiQi Pill(QSYQ),a traditional Chinese medicine-derived drug product,and its robustness was systematically evaluated by four indices:clustering analysis by principal component analysis,similarity analysis by the congruence coefficient,the number of separated peaks,and the peak area proportion of separated peaks.Compared with conventional ultraviolet-based fingerprints,the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples,but also higher robustness under different chromatographic conditions(i.e.,flow rate,apparent pH,column temperature,and column).The result also showed for such large-scale fingerprints including a large number of peaks,the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm.This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive,robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.

关键词： Multiple reaction monitoring Mass spectrometry-based fingerprinting Quality assessment Traditional Chinese medicine robustness evaluation

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