检索结果-内蒙古大学图书馆

Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

Bulletin of the Polish Academy of sciences: Technical sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

来源：评论

学校读者我要写书评

暂无评论

CRISPR-Cas12a-Empowered Electrochemical Biosensor for Rapid and Ultrasensitive Detection of SARS-CoV-2 Delta Variant

引用

Nano-Micro Letters 2022年第10期14卷 83-94页

作者： Chenshuo Wu Zhi Chen Chaozhou Li Yabin Hao Yuxuan Tang Yuxuan Yuan Luxiao Chai Taojian Fan Jiangtian Yu Xiaopeng Ma Omar AAl-Hartomy SWageh Abdullah GAl-Sehemi Zhiguang Luo Yaqing He Jingfeng Li Zhongjian Xie Han Zhang International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of EducationInstitute of Microscale OptoelectronicsCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060People’s Republic of China Shenzhen International Institute for Biomedical Research Shenzhen 518116People’s Republic of China Department of Respiratory Shenzhen Children’s HospitalShenzhen 518038People’s Republic of China Institute of Pediatrics Shenzhen Children’s HospitalShenzhen 518038People’s Republic of China Shenzhen Han’s Tech Limited Company Shenzhen 518000People’s Republic of China Hospital of Guangzhou Medical University Qingyuan city People’s HospitalQingyuan 511518People’s Republic of China Department of Physics Faculty of ScienceKing Abdulaziz UniversityJeddah 21589Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Department of Chemistry College of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Zhongmin(Shenzhen)Intelligent Ecology Co. LtdShenzhen 518055People’s Republic of China Shenzhen Center for Disease Control and Prevention Shenzhen 518055People’s Republic of China

Coronavirus disease 2019(COVID-19)is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).The gold standard method for the diagnosis of SARS-CoV-2 depends on quantitative reverse transcription-polymerase chain reaction till now,which is time-consuming and requires expensive instrumentation,and the confirmation of variants relies on further sequencing ***,we first proposed a robust technique-methodology of electrochemical CRISPR sensing with the advantages of rapid,highly sensitivity and specificity for the detection of SARS-CoV-2 *** enhance the sensing capability,gold electrodes are uniformly decorated with electro-deposited gold *** DNA template identical to SARS-CoV-2 Delta spike gene sequence as model,our biosensor exhibits excellent analytical detection limit(50 fM)and high linearity(R2=0.987)over six orders of magnitude dynamic range from 100 fM to 10 nM without any nucleic-acid-amplification *** detection can be completed within 1 h with high stability and specificity which benefits from the CRISPR-Cas ***,based on the wireless micro-electrochemical platform,the proposed biosensor reveals promising application ability in point-of-care testing.

关键词： SARS-COV-2 variant Methodology of electrochemical CRISPR sensing(MOECS) Gold nanoparticles(AuNPs) Point-of-care testing(POCT)

来源：评论

学校读者我要写书评

暂无评论

M-CNN-RF: A hybrid deep learning model for accurate pediatric skeletal age estimation using hand bone radiographs

引用

Alexandria engineering Journal 2025年 129卷 289-301页

作者： Hazrat Bilal Yibin Tian Inam Ullah Sahil Garg Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea École de Technologie Supérieure Montreal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Precise and accurate skeletal age estimation using medical imaging is a pivotal and challenging task in the healthcare sector, particularly for identifying potential bone growth issues in infants and newborns. Therefore, this study addresses the pervasive challenges associated with assessing bone abnormalities in pediatric patients, including injuries and infections. Given the importance of early and precise detection of skeletal development, a novel hybrid model is proposed that integrates a modified convolutional neural network (M-CNN) with a robust machine learning (ML) model, specifically random forest (RF), resulting in the M-CNN-RF framework. This model is designed to enhance pediatric bone health assessment by providing an effective method for skeletal age estimation. The M-CNN-RF model is tailored to accurately evaluate hand bone maturation, overcoming the inherent difficulties in skeletal age assessment. The model utilizes the bone age dataset from the Radiological Society of North America that includes 14,236 left-hand radiological images, focusing on the development of a robust model for a precise evaluation based on hand skeleton guidelines. In addition, to enhance the prediction and generalization of the model, data augmentation techniques were employed to increase the size of the dataset. The M-CNN-RF exhibits exceptional performance using numerous performance measures, achieving an accuracy of 97% and precision and recall exceeding 94%. In addition, the model reaches an F1 score of 97%, highlighting the ability of the model to ensure a balance between precision and recall. Furthermore, low mean absolute error (MAE) and mean square error (MSE) values of 0.0141 and 0.0327, respectively, were computed for the proposed model, which demonstrates its notable efficacy in predicting skeletal age. The findings of this study not only contribute valuable information for clinical applications but also underscore the potential of the adopted approach to address th

关键词： Machine learning Pediatric bone health assessment Bone X-ray images Modified-CNN model Hybrid M-CNN-RF model Skeletal age estimation

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

An adaptive scaling cuckoo search algorithm 4

An adaptive scaling cuckoo search algorithm

引用

4th International Conference on advanced Electronic Materials, computers and Software engineering, AEMCSE 2021

作者： Song, Zhenyu Song, Shuangyu Zhang, Bin Qian, Jin Tang, Cheng Ji, Junkai Taizhou University College of Computer Science and Technology Taizhou China Jiangsu University of Technology School of Computer Engineering Changzhou China University of Toyama Faculty of Engineering Toyama Japan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781665415965

The cuckoo search algorithm (CSA) is a natureinspired metaheuristic algorithm based on the parasitic behaviour of the cuckoo species, and it has received much attention for solving optimization problems. Nevertheless, the CSA still has some limitations when applied to optimization problems, such as easily falling into local optima and converging slowly. In this paper, we propose a modified local search method with an adaptive boundary scaling mechanism using a chaotic map, which calculates the search radius depending on the position of the current population. Thus, the search ability of the traditional CSA is improved for optimization purposes. To further verify the stability and effectiveness of the proposed adaptive boundary scaling algorithm (ACSA), we compare it with the traditional CSA and nonmodified chaos-embedded CSA (CCSA) on 25 test benchmark functions. The experimental and statistical test results show that the ACSA outperforms its competitors, which means that the proposed algorithm can perform better than these other approaches in terms of their search capabilities for optimization problems. © 2021 IEEE.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Efficient Security Interface for High-Performance Ceph Storage System

SSRN

引用

SSRN 2024年

作者： Parast, Fatemeh Khoda Wang, Yang Kelly, Brett Damghani, Seyed Alireza Kent, Kenneth B. Faculty of Computer Science University of New Brunswick Canada 45 Drives Inc. Canada Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Ceph portrays a resilient clustered storage solution, supporting object, block, and file storage capabilities with no single point of failure. Despite these qualifications, data confidentiality defines a concern in the system, as authentication and access control are the only data protection security services in Ceph. CephArmor was proposed as a third-party security interface to protect data confidentiality by adding an extra protection layer to data at rest. Despite the additional layer, the original API design required further efficiency improvements and expanded security services to address security and performance concerns concurrently. In this study, we propose a new architectural design to address the associated issues with the preliminary prototype. Comprehensive performance and security analysis verify the improvement of the proposed method compared to the initial approach. The benchmark result has indicated a 37% improvement on average in IOPS, elapsed time, and bandwidth for the write benchmark compared to the initial model. © 2024, The Authors. All rights reserved.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

ML-based Expert Products Scoring System

ML-based Expert Products Scoring System

引用

Progress in Applied Electrical engineering (PAEE)

作者： Patryk Mendoń Mariusz Pelc Aleksandra Kawala-Sterniuk Mariusz Gola Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Mathematics Physics and Computer Science University of Opole Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

In this paper the authors propose a system architecture which allows product scoring based on sentiment analysis and utilising Machine Learning (ML) and Expert systems technologies. This solution presented in this paper allows decoupling the AI-based part, which is very capable in sentiment analysis but is lacking some flexibility to actually put the sentiment analysis results in a wider context to make a better sense as to what the results actually mean. This task in our solution is performed by the Expert system where the expert may provide some decision making rules in order to determine the final system *** results confirm discrepancy between reviews and star ratings.

关键词： Knowledge engineering Fuzzy logic Sentiment analysis Reviews Systems architecture Stars Machine learning Cognition Expert systems System analysis and design

来源：评论

学校读者我要写书评

暂无评论

A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks

引用

IEEE Transactions on Mobile Computing 2025年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin Beihang University School of Electronic and Information Engineering Beijing100191 China Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks' demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. © 2002-2012 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Enabling technology and core theory of synthetic biology

引用

science China(Life sciences) 2023年第8期66卷 1742-1785页

作者： Xian-En Zhang Chenli Liu Junbiao Dai Yingjin Yuan Caixia Gao Yan Feng Bian Wu Ping Wei Chun You Xiaowo Wang Tong Si Faculty of Synthetic Biology Shenzhen Institute of Advanced TechnologyShenzhen 518055China Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China State Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Metabolism Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Tianjin Institute of Industrial Biotechnology Chinese Academy of SciencesTianjin 300308China Ministry of Education Key Laboratory of Bioinformatics Center for Synthetic and Systems BiologyBioinformatics DivisionBeijing National Research Center for Information Science and TechnologyDepartment of AutomationTsinghua UniversityBeijing 100084China

Synthetic biology provides a new paradigm for life science research(“build to learn”)and opens the future journey of biotechnology(“build to use”).Here,we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology,including synthesis and assembly of a genome,DNA storage,gene editing,molecular evolution and de novo design of function proteins,cell and gene circuit engineering,cell-free synthetic biology,artificial intelligence(AI)-aided synthetic biology,as well as *** also introduce the concept of quantitative synthetic biology,which is guiding synthetic biology towards increased accuracy and predictability or the real rational *** conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

关键词： synthetic biology quantitative synthetic biology genome synthesis and assembly DNA storage molecular evolution de novo design computer-aided design cell engineering gene circuit chassis cell artificial intelligent(AI) biofoundry

来源：评论

学校读者我要写书评

暂无评论

DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal

DocDeshadower: Frequency-Aware Transformer for Document Shad...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ziyang Zhou Yingtie Lei Xuhang Chen Shenghong Luo Wenjun Zhang Chi-Man Pun Zhen Wang School of Computer Science and Engineering Huizhou University Huizhou China Faculty of Science and Technology University of Macau Macau China Tp-Link International Shenzhen Co. Ltd. Shenzhen China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Shadows in scanned documents pose significant challenges for document analysis and recognition tasks due to their negative impact on visual quality and readability. Current shadow removal techniques, including traditional methods and deep learning approaches, face limitations in handling varying shadow intensities and preserving document details. To address these issues, we propose DocDeshadower, a novel multi-frequency Transformer-based model built upon the Laplacian Pyramid. By decomposing the shadow image into multiple frequency bands and employing two critical modules: the Attention-Aggregation Network for low-frequency shadow removal and the Gated Multi-scale Fusion Transformer for global refinement. DocDeshadower effectively removes shadows at different scales while preserving document content. Extensive experiments demonstrate DocDe-shadower's superior performance compared to state-of-the-art methods, highlighting its potential to significantly improve document shadow removal techniques. The code is available at https://***/leiyingtie/DocDeshadower.

关键词： Deep learning Visualization Text analysis Laplace equations Codes Image color analysis Face recognition Logic gates Transformers Cybernetics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：