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2017 IEEE 2nd Information Technology, Networking, Electronic and automation Control Conference (ITNEC 2017)

作者： Jie Duan Maoli Wang Yan Jiang Jingbo Zhao Yongwei Tang College of Automation Engineering Qingdao University of Technology Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)

ISBN: (纸本)9781509064151;9781509064144

In this paper, an adaptive fuzzy control algorithm based on genetic algorithm is proposed. The membership function of fuzzy controller and the optimization of fuzzy rules are carried out simultaneously. Through the data fusion method, the lateral deviation and the heading deviation two sensors are fused into a data, and through the control of this parameter in the allowed range change, realizes the agricultural machinery to move along the desired path. The last is to determine the control method of automatic navigation of agricultural machinery according to the operation characteristics, simulation results show that the optimization of the parameters of the fuzzy controller strategy, the system has good dynamic and static characteristics, the control effect is very good.

关键词： Genetic Algorithm Adaptive Fuzzy Control Automatic Navigation

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Based on self-learning dictionary Circuit Board Fault Diagnosis Device 2

Based on self-learning dictionary Circuit Board Fault Diagno...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Yongwei Tang Chunmei Wang Maoli Wang Huijuan Hao Jingbo Zhao Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) School of Automation Engineering Qingdao University of Technology

In the classical fault dictionary fault detection method based on the law, a microcontroller-based self-learning fault dictionary method for the circuit board fault detection. At the same time designe

ISBN: (纸本)9781467389808

关键词： Circuit faults Printed circuits Dictionaries Signal detection Power supplies Instruments Rectifiers

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Adaptive-Learning-Based H∞ Optimal Tracking Control Design for a Class of Constrained Nonlinear Systems

Adaptive-Learning-Based H∞ Optimal Tracking Control Design ...

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第37届中国控制会议

作者： Jiaxu Hou Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Automation Guangdong University of Technology

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time H∞ tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

关键词： Adaptive dynamic programming H∞ tracking control constraints convergence proof

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Design of stable and new polysaccharide nanoparticles composite and their interaction with solid cellulose surfaces

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Nano-Structures and Nano-Objects 2020年 24卷

作者： Czabany, Ivana Hribernik, Silvo Bračič, Matej Kurečič, Manja Thomas, Sabu Stana Kleinschek, Karin Mohan, Tamilselvan Institute of Chemistry University of Graz Heinrichstrasse 28 Graz 8010 Austria Institute of Automation Faculty of Electrical Engineering and Computer Science University of Maribor Koroska cesta 46 Maribor 2000 Slovenia Laboratory for Characterisation and Processing of Polymers Faculty of Mechanical Engineering University of Maribor Smetanova 17 Maribor 2000 Slovenia School of Energy Materials International and Inter University Centre for Nanoscience and Nanotechnology School of Chemical Sciences Mahatma Gandhi University Priyadarshini Hills P.O. Kottayam 686560 Kerala India Institute for Chemistry and Technology of Biobased Systems (IBioSys) Graz University of Technology Stremayrgasse 9 Graz 8010 Austria

This study presents a simple and straightforward method for the preparation of aqueous-based stable polysaccharide-metal oxide nanoparticles composites — composed of carboxymethyl cellulose (CMC) and titanium dioxide nanoparticles (TiO₂ NPs), and their interaction with the cellulose surface, is presented. Using a combination of anionic polysaccharide CMC as a stabilizer agent and ultrasound probe sonication technique as an agitation tool, a highly stable dispersion of CMC/TiO₂ NPs composites is prepared in water, with and without the presence of calcium chloride electrolyte, at various pH values. The results of the dynamic light scattering and the zeta-potential measurements showed that the quality, the stability of the dispersions and the hydrodynamic radius of the particles (ca, 200–300 nm) remain well-preserved for several weeks under normal storage conditions. The transmission electron microscope revealed that the morphology and size of the dry particles size (ca. 25–50 nm) remain unchanged and are not influenced by the addition of the stabilizer CMC, the electrolyte or the pH value of the solution. The presence of electrolyte and the low pH of the dispersions strongly favored an irreversible and enhanced deposition of CMC/TiO₂ NPs on cellulose surface as showed by a quartz crystal microbalance with dissipation. A linear correlation between the concentration of CMC and the adsorbed mass of polymer and NPs, and the water content of the adsorbed layers is observed. X-ray photoelectron spectroscopy and atomic force microscopy are combined to prove the successful immobilization of polymer and NPs on the cellulose surface. The knowledge gained from this study can be extended not only for the stabilization of various metal oxide nanoparticles but also for the development of green polymer/NPs composites, that may have the potential to be used as functional coatings in biomedical (e.g. wound dressing) applications. © 2020

关键词： Adsorption Carboxmethyl cellulose QCM-D Thin films Titanium dioxide nanoparticles Water content Zeta potential

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Aggregation Signature for Small Object Tracking

arXiv

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arXiv 2019年

作者： Liu, Chunlei Ding, Wenrui Yang, Jinyu Murino, Vittorio Zhang, Baochang Han, Jungong Guo, Guodong School of Electrical and Information Engineering Beihang University Beijing China Unmanned System Research Institute Beihang University Beijing China School of Computer Science University of Birmingham British United Kingdom University of Verona Verona Italy Pattern Analysis and Computer Vision department Istituto Italiano di Tecnologia Genoa Italy School of Automation Science and Electrical Engineering Beihang University Beijing China Shenzhen Academy of Aerospace Technology Shenzhen China WMG Data Science Group University of Warwick CoventryCV4 7AL United Kingdom Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application

—Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://***/bczhangbczhang/. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

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Cell Genomics 2025年第5期5卷 100845-100845页

作者： Huang, Yu-Ning Munteanu, Viorel Love, Michael I. Ronkowski, Cynthia Flaire Deshpande, Dhrithi Wong-Beringer, Annie Corbett-Detig, Russell Dimian, Mihai Moore, Jason H. Garmire, Lana X. Reddy, T.B.K. Butte, Atul J. Robinson, Mark D. Eskin, Eleazar Abedalthagafi, Malak S. Mangul, Serghei Department of Clinical Pharmacy Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles 90089 CA United States Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau 2045 Moldova Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Genetics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biomolecular Engineering University of California Santa Cruz Santa Cruz 95064 CA United States Genomics Institute University of California Santa Cruz Santa Cruz 95064 CA United States Department of Computers Electronics and Automation Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles 90069 CA United States Department of Computational Medicine and Bioinformatics University of Michigan Medical School Ann Arbor 48105 MI United States US Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley 94720 CA United States Bakar Computational Health Sciences Institute University of California San Francisco (UCSF) San Francisco 94143 CA United States Center for Data-Driven Insights and Innovation University of California Oakland Oakland 94607 CA United States SIB Swiss Institute of Bioinformatics and Department of Molecular Life Sciences University of Zurich Zurich 8057 Switzerland Department of Computer Science University of California Los Angeles Los Angeles CA United States Department of Computational Medicine University of California Los Angeles Los Angeles CA United States Department of Human Genetics University of California Los Angeles Los Angeles CA United Stat

Metadata, or “data about data,” is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integration, and interpretation, enabling reproducibility, reusability, and secondary analysis. However, metadata sharing remains hindered by perceptual and technical barriers, including the lack of uniform standards, privacy concerns, study design limitations, insufficient incentives, inadequate infrastructure, and a shortage of trained personnel. These challenges compromise data reliability and obstruct integrative meta-analyses. Addressing these issues requires standardization, education, stronger roles for journals and funding agencies, and improved incentives and infrastructure. Looking ahead, emerging technologies such as artificial intelligence and machine learning may offer promising solutions to automate metadata processes, increasing accuracy and scalability. Fostering a collaborative culture of metadata sharing will maximize the value of omics data, accelerating innovation and scientific discovery. © 2025 The Authors

关键词： barriers in metadata sharing practices data metadata metadata completeness

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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An invisible DWT watermarking algorithm using noise removal with application to dermoscopic images

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Journal of Physics: Conference Series 2021年第1期1730卷

作者： S Moldovanu F A (Damian) Michis L Moraru Dunarea de Jos University of Galati Department of Computer Science and Information Technology Faculty of Automation Computers Sciences Electronics and Electrical Engineering 2 Stiintei Street RO-800210 Galati Romania Dunarea de Jos University of Galati Modelling & Simulation Laboratory Galati Romania Dunarea de Jos University of Galati Faculty of Science and Environment 111 Domneasca Street RO-800201 Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street 800008 Galati Romania

A new approach for the digital watermarking process is proposed to be part of the pre-processing stage of a computer-aided diagnosis system. We propose to embed a denoised image acting as the watermark image in the original host image with the final goal of improving the quality of demoscopic images for further image processing operation related to CAD. The proposed algorithm uses Discrete Wavelet Transform (DWT) corroborated with some basic properties of Human Visual System such as Contrast Sensitive Function (CSF) and Noise Visibility Function (NVF) with the goal of correlating the texture properties and noise. This approach hides the watermark (i.e. denoised version of the host image) in high-pass subbands that are focused on image features. The main concern is to evaluate the distortion produced to the host image by watermarking and an objective quality measure function, i.e. Weighted Peak Signal-to-Noise Ratio (WPSNR), is used to evaluate the existing differences between the original and watermarked images. The proposed approach is tested using the available skin lesion images from the digital image archive of the Department of Dermatology of the University Medical Center Groningen. The experiment results show the improved performance of the proposed scheme against a 3 × 3 median filtering attack in comparison with the a 5 × 5 median filtering attack.

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Simulating evolutional symport/antiport by evolution-communication and vice versa in tissue p systems with parallel communication 18th

Simulating evolutional symport/antiport by evolution-communi...

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18th International Conference on Membrane Computing, CMC 1018

作者： Adorna, Henry Alhazov, Artiom Pan, Linqiang Song, Bosheng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China University of the Philippines Diliman Quezon City1101 Philippines Institute of Mathematics and Computer Science Academy of Science of Moldova Academiei 5 Chişinău2028 Moldova School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul... 详细信息

ISBN: (纸本)9783319733586

关键词： Tissue

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Research on Agricultural Environment Prediction Based on Deep Learning

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Procedia computer science 2018年 139卷 33-40页

作者： Shuchang Chen Bingchan Li Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

The environmental security of agriculture is closely related to human beings. Analytical training of agricultural environmental data, forecasting its development trend, has positive significance for the protection of the safety of agricultural products. This paper proposes an agricultural environment prediction model based on deep learning LSTM (Long Short-Term Memory). By analyzing the agricultural environment parameters of the current period, the environmental parameters of the next moment can be predicted to achieve the purpose of early warning. The experimental results show that the model’s prediction results have little deviation from the actual values; on this basis, the LSTM model is optimized to replace LSTM with GRU(Gated Recurrent Unit), and the model is more effective.

关键词： Deep learning Agricultural environment LSTM GRU

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