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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Multiple valence states of Fe boosting SERS activity of Fe_(3)O_(4) nanoparticles and enabling effective SERS-MRI bimodal cancer imaging

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Fundamental Research 2024年第4期4卷 858-867页

作者： Jie Lin Xuehua Ma Anran Li Ozioma Udochukwu Akakuru Chunshu Pan Meng He Chenyang Yao Wenzhi Ren Yanying Li Dinghu Zhang Yi Cao Tianxiang Chen Aiguo Wu Cixi Institute of Biomedical Engineering International Cooperation Base of Biomedical Materials Technology and ApplicationChinese Academy of Science Key Laboratory of Magnetic Materials and DevicesZhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringCASNingbo315201China School of Engineering Medicine Key Laboratory of Big Data-Based Precision Medicine(Beihang University)Ministry of Industry and Information TechnologyBeihang UniversityBeijing100191China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China University of Chinese Academy of Sciences Bejing 100049China

Developing novel nanoparticle-based bioprobes utilized in clinical settings with imaging resolutions ranging from cell to tissue levels is a major challenge for tumor diagnosis and ***,an optimized strategy for designing a Fe_(3)O_(4)-based bioprobe for dual-modal cancer imaging based on surface-enhanced Raman scattering(SERS)and magnetic resonance imaging(MRI)is *** SERS activity of ultrasmall Fe_(3)O_(4) nanoparticles(NPs)was discovered,and a 5×10^(-9)M limit of detection for crystal violet molecules was successfully *** high-efficiency interfacial photon-induced charge transfer in Fe_(3)O_(4) NPs was promoted by multiple electronic energy levels ascribed to the multiple valence states of Fe,which was observed using ultraviolet-visible diffuse reflectance *** functional theory calculations were utilized to reveal that the narrow band gap and high electron density of states of ultrasmall Fe_(3)O_(4) NPs significantly boosted the vibronic coupling resonances in the SERS system upon *** subtypes of cancer cells were accurately recognized via high-resolution SERS imaging in vitro using the prepared Feg Og-based bioprobe with high sensitivity and good ***,Fe_(3)O_(4)-based bioprobes simultaneously exhibited T,-weighted MRI contrast enhancement with an active targeting capability for tumors in *** the best of our knowledge,this is the first report on the use of pure semiconductor-based SERS-MRI dual-modal nanoprobes in tumor imaging in vivo and in vitro,which has been previously realized only using semiconductor-metal complex *** non-metallic materials with SERS-MRI dual-modal imaging established in this report are a promising cancer diagnostic platform,which not only showed excellent performance in early tumor diagnosis but also possesses great potential for image-guided tumor treatment.

关键词： Fe_(3)O_(4)NPs SERS-MRI dual-modal nanoprobes PICT Multiple valence states Cancer diagnosis

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A Time-Domain Carrier Frequency Offset Estimation Algorithm Based on the Power of Zero-Subcarriers for CO-OFDM Systems

A Time-Domain Carrier Frequency Offset Estimation Algorithm ...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Yan, Jiaxin Jin, Taowei Du, Xinwei Zhang, Jing Yang, Qi Qiu, Kun Key Lab. of Optical Fiber Sensing and Communications University of Electronic Science and Technology of China Chengdu China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China

We propose a simple, full-range, and low-latency frequency offset estimation method based on the moving average filter bank in the time domain for CO-OFDM transmission systems. Simulation results validate its feasibil... 详细信息

ISBN: (纸本)9798350312614

关键词： Carrier frequency offset orthogonal frequency division multiplexing

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Research on Freshwater Fish Recognition Based on Improved Random Forest

Research on Freshwater Fish Recognition Based on Improved Ra...

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2023 International Conference on Intelligent Management and Software Engineering, IMSE 2023

作者： Zhang, Xiaorui Zhang, Longqing Chen, Hongming Chen, Qian Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China Guangdong University of Science and Technology Dongguan523083 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350394146

In this paper, the performance of models based on decision trees and improved random forest is explored for the fish classification problem. Through comparative experiments, the differences in performance in terms of accuracy between these two models are studied. The experiments utilize fish datasets from different ecological environments and geographic regions, containing rich morphological feature information. Firstly, we construct a fish classification model based on decision trees and train and test it on the dataset. This model demonstrates a decent performance in terms of accuracy, achieving a classification accuracy of 79.16%. However, to further enhance classification performance, we introduce the random forest model, which integrates predictions from multiple decision trees. Through cross-validation and parameter tuning, an improved random forest classifier is built. The experimental results illustrate a significant improvement in fish classification tasks with the improved random forest model. Its accuracy reaches 91.88%, showing a 7.5-percentage-point increase compared to the random forest model. Furthermore, we test the generalization performance of the model and find that the improved random forest model also exhibits good classification ability on unseen samples. In summary, this research, by contrasting and analyzing the performance of decision tree models and the improved random forest model, validates the effectiveness and superiority of the random forest in the fish classification problem. This provides essential insights for fields such as ecological environment monitoring and fish resource management, while also offering inspiration for further exploration of the application of other machine learning methods to similar problems. ©2023 IEEE.

关键词： Decision trees

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Enhancing Statistical-Based Remote Sensing Image Classification Algorithms: An Optimization Study 8th

Enhancing Statistical-Based Remote Sensing Image Classificat...

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Yu, Tianyi Liu, L. Faculty of Science and Technology BNU-HKBU United International College Zhuhai519087 China Centre for Evaluation Studies BNU-HKBU United International College Zhuhai519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China

ISBN: (纸本)9789819777099

This study presents an optimization approach for statistically based remote sensing image classification algorithms. Traditional image classification methods suffer from subjectivity and inefficiency due to manual annotation. Taking advantage of advances in artificial intelligence and computer vision, this study focuses on content-based image classification methods. Specifically, the study focuses on optimizing statistically based algorithms for classifying remote sensing images, which are known for their complexity. The methodology includes efficient image feature extraction using visual saliency features based on regional covariance, followed by an optimized statistical-based classification algorithm using extended sparse coding and an empty spectrum dictionary model. Experimental evaluation conducted on remotely sensed images of Hong Kong demonstrates the superior performance of the proposed method compared to existing approaches, with an average accuracy rate of 99.41%. A comprehensive error analysis highlights the robustness and consistency of the proposed approach, confirming its effectiveness in addressing challenges of remote sensing image classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Algorithm analysis Image classification Optimization Remote sensing Statistical-based

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Embodied Intelligence in Mining: Leveraging Multi-modal Large Language Model for Autonomous Driving in Mines

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-4页

作者： Li, Luxi Li, Yuchen Zhang, Xiaotong He, Yuhang Yang, Jianjian Tian, Bin Ai, Yunfeng Li, Lingxi Nuchter, Andreas Xuanyuan, Zhe Faculty of Science and Technology BNU-HKBU United International College Zhuhai China Institution of Automation Chinese Academy of Sciences Beijing China University of Oxford Oxford U.K Beijing China Purdue University West Lafayette IN USA Würzburg University Würzburg Germany BNU-HKBU United International College Zhuhai Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science China

With computer technology advancing in both software and hardware, the benefits of embodied intelligence are becoming increasingly evident. This robust interactive learning model enables artificial intelligence (AI) to be more flexibly deployed across diverse fields. In recent years, the development of multi-modal large language models (LLMs) has further accelerated the progress of AI, prompting extensive research on how to leverage these advancements to enhance the field of autonomous driving. This perspective believes that embodied intelligence can significantly enhance the application of LLMs, analyzing the new opportunities brought to the mining industry, and emphasizing the potential of their integration to revolutionize various aspects of the field. Meanwhile, This perspective also examines the challenges of deploying embodied agents in mining, while emphasizing their promising future and offering insights into potential research and development avenues. IEEE

关键词： data mining

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Research on Ornamental Fish Recognition Base on Improved Shufflenet V2 Network 4

Research on Ornamental Fish Recognition Base on Improved Shu...

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4th International Conference on Big data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Zhang, Xingyun Chen, Hongming Zhang, Longqing Chen, Qian Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Guangdong University of Science and Technology School of Computer Science Dongguan523083 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350329490

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. Based on the ShuffleNet V2 network, we incorporate Transfer Learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. The final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome. © 2023 IEEE.

关键词： Computer vision

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From GARCH to Neural Network for Volatility Forecast

arXiv

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arXiv 2024年

作者： Zhao, Pengfei Zhu, Haoren Wilfred Siu Hung, N.G. Lee, Dik Lun Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College China Hong Kong University of Science and Technology Hong Kong

Volatility, as a measure of uncertainty, plays a crucial role in numerous financial activities such as risk management. The Econometrics and Machine Learning communities have developed two distinct approaches for financial volatility forecasting: the stochastic approach and the neural network (NN) approach. Despite their individual strengths, these methodologies have conventionally evolved in separate research trajectories with little interaction between them. This study endeavors to bridge this gap by establishing an equivalence relationship between models of the GARCH family and their corresponding NN counterparts. With the equivalence relationship established, we introduce an innovative approach, named GARCH-NN, for constructing NN-based volatility models. It obtains the NN counterparts of GARCH models and integrates them as components into an established NN architecture, thereby seamlessly infusing volatility stylized facts (SFs) inherent in the GARCH models into the neural network. We develop the GARCH-LSTM model to showcase the power of the GARCH-NN approach. Experiment results validate that amalgamating the NN counterparts of the GARCH family models into established NN models leads to enhanced outcomes compared to employing the stochastic and NN models in isolation. © 2024, CC BY.

关键词： Stochastic systems

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PAMPHLET:PAM Prediction HomoLogous-Enhancement Toolkit for precise PAM prediction in CRISPR-Cas systems

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Journal of Genetics and Genomics 2025年第2期52卷 258-268页

作者： Chen Qi Xuechun Shen Baitao Li Chuan Liu Lei Huang Hongxia Lan Donglong Chen Yuan Jiang Dan Wang Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhaiGuangdong 519087China School of Biology and Biological Engineering South China University of TechnologyGuangzhouGuangdong 510006China BGI Research HangzhouZhejiang 310030China BGI Research ShenzhenGuangdong 518083China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Laboratory of Integrative Biomedicine Department of BiologyFaculty of ScienceUniversity of CopenhagenCopenhagenDenmark Qingdao-Europe Advanced Institute for Life Sciences BGI ResearchQingdaoShandong 266555China School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiang 310024China STOmics Americas Ltd. 2904 Orchard PkwySan Jose CA 95134USA

CRISPR-Cas technology has revolutionized our ability to understand and engineer organisms,evolving from a singular Cas9 model to a diverse CRISPR toolbox.A critical bottleneck in developing new Cas proteins is identifying protospacer adjacent motif(PAM)*** to the limitations of experimental methods,bioinformatics approaches have become ***,existing PAM prediction programs are limited by the small number of spacers in CRISPR-Cas systems,resulting in low *** address this,we develop PAMPHLET,a pipeline that uses homology searches to identify additional spacers,significantly increasing the number of spacers up to *** is validated on 20 CRISPR-Cas systems and successfully predicts PAM sequences for 18 *** predictions are further validated using the DocMF platform,which characterizes protein-DNA recognition patterns via next-generation *** high consistency between PAMPHLET predictions and DocMF results for Cas proteins demonstrates the potential of PAMPHLET to enhance PAM sequence prediction accuracy,expedite the discovery process,and accelerate the development of CRISPR tools.

关键词： CRISPR-Cas Protospacer adjacentmotif Genome editing PAM prediction Computational framework

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Kernel-Based Enhanced Oversampling Method for Imbalanced Classification

arXiv

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arXiv 2025年

作者： Li, Wenjie Zhu, Sibo Li, Zhijian Wang, Hanlin Faculty of Science and Technology BNU-HKBU United International College Zhuhai China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method enhances the traditional SMOTE algorithm by incorporating convex combination and kernel-based weighting to generate synthetic samples that better represent the minority class. Through experiments on multiple real-world datasets, we demonstrate that the new technique outperforms existing methods in terms of F1-score, G-mean, and AUC, providing a robust solution for handling imbalanced datasets in classification tasks. Copyright © 2025, The Authors. All rights reserved.

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