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arXiv 2022年

作者： Eremin, Denis Engel, Dennis Krüger, Dennis Wilczek, Sebastian Berger, Birk Oberberg, Moritz Wölfel, Christian Smolyakov, Andrei Lunze, Jan Awakowicz, Peter Schulze, Julian Brinkmann, Ralf Peter Institute of Theoretical Electrical Engineering Ruhr University Bochum Germany Institute of Applied Electromagnetics and Plasma Technology Ruhr University Bochum Germany Institute of Automation and Computer Control Ruhr University Bochum Germany Department of Physics and Engineering Physics University of Saskatchewan Saskatoon Canada

The electron dynamics and the mechanisms of power absorption in radio-frequency (RF) driven, magnetically enhanced capacitively coupled plasmas (MECCPs) at low pressure are investigated. The device in focus is a geometrically asymmetric cylindrical magnetron with a radially nonuniform magnetic field in axial direction and an electric field in radial direction. The dynamics is studied analytically using the cold plasma model and a single-particle formalism, and numerically with the inhouse energy and charge conserving particle-in-cell/Monte Carlo collisions code ECCOPIC1S-M. It is found that the dynamics differs significantly from that of an unmagnetized reference discharge. In the magnetized region in front of the powered electrode, an enhanced electric field arises during sheath expansion and a reversed electric field during sheath collapse. Both fields are needed to ensure discharge sustaining electron transport against the confining effect of the magnetic field. The corresponding azimuthal E×B-drift can accelerate electrons into the inelastic energy range which gives rise to a new mechanism of RF power dissipation. It is related to the Hall current and is different in nature from Ohmic heating, as which it has been classified in previous literature. The new heating is expected to be dominant in many magnetized capacitively coupled discharges. It is proposed to term it the "µ-mode" to separate it from other heating modes. © 2022, CC BY-NC-ND.

关键词： Dynamics

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Implementation of a Human-Robot Collaboration System Based On Smart Hand Gesture and Speech Recognition

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Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao 2020年第6期41卷 755-762页

作者： Chen, Shang-Liang Huang, Li-Wu Huang, Chung-Chi Lee, Feng-Chi Huang, Ho-Chuan Chen, Chien-Yu Institute of Manufacturing Information and Systems National Cheng Kung University Tainan70101 Taiwan Department of Automation and Control Engineering Far East University Tainan74448 Taiwan Department of Mechatronics Control Industrial Technology Research Institute Hsinchu310 Taiwan Department of Intelligent Commerce National Kaohsiung University of Applied Sciences Kaohsiung824 Taiwan Computer Science and Information Engineering National University of Tainan Tainan70005 Taiwan

With the development of artificial intelligence technology, the robot arm will be applied in more fields, undertake more work, and become an important assistant of humans. Especially in the manufacturing industry, if a perfect human-robot cooperation (HRC) model can be developed, in which the engineer is responsible for complex operations and monitoring the production process and the robot arm is responsible for highly repetitive and tiring work, the flexibility of the manufacturing process can be improved, the risk of personnel injury can be reduced, and the production line productivity can be improved. To realize the human-robot collaboration system that can be applied in various fields, the technologies of robot arm control, human-robot interaction (HRI), Radio Frequency Identification, machine-to-machine communication, and automatic speech recognition (ASR), etc. are combined in this research. Besides, the HRC in intelligent manufacturing is taken as the demonstration result of this research. In this application example, the human-robot interaction function is realized through smart hand gesture and speech recognition technology, which enables the user to communicate commands to the robot arm most intuitively and easily. The robot arm can execute specific actions, provide processing prompts, or read corresponding motion control command macros according to different commands, to realize various processing contents required by users. © 2020, Chinese Mechanical engineering Society. All right reserved.

关键词： Radio frequency identification (RFID)

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Photonic torons, topological phase transition and tunable spin monopoles

arXiv

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arXiv 2024年

作者： Wu, Haijun Mata-Cervera, Nilo Wang, Haiwen Zhu, Zhihan Qiu, Cheng-Wei Shen, Yijie Wang Da-Heng Center Heilongjiang Key Laboratory of Quantum Control Harbin University of Science and Technology Harbin150080 China Centre for Disruptive Photonic Technologies School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Applied Physics Stanford University StanfordCA94305 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Creation and control of topological complex excitations play crucial roles in both fundamental physics and modern information science. Torons are a sophisticated class of 3D chiral polar topological structures with both skyrmionic quasiparticle textures and monopole point defects, so far only observed in liquid crystal nonpolar models. Here, we experimentally construct torons with the photonic spin of vector structured light and demonstrate the topological phase transitions among diverse topological states: torons, hopfions, skyrmioniums and monopole pairs. We can also continually tune the toron’s chirality and the helical spin textures of emerging monopole pairs. The birth of photonic torons and tunable monopoles opens a flexible platform for studying nontrivial light-matter interaction and topological informatics. © 2024, CC0.

关键词： Topology

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Modeling and Simulation of Upstream and Downstream Processes for Monoclonal Antibody Production

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IFAC-PapersOnLine 2022年第7期55卷 685-690页

作者： Morten Ryberg Wahlgreen Kristian Meyer Tobias K.S. Ritschel Allan Peter Engsig-Karup Krist V. Gernaey John Bagterp Jørgensen Department of Applied Mathematics and Computer Science Technical University of Denmark DK-2800 Kgs. Lyngby Denmark Department of Chemical and Biochemical Engineering PROSYS Technical University of Denmark DK-2800 Kgs. Lyngby Denmark MCT Bioseparation ApS DK-2800 Kgs. Lyngby Denmark 2-control ApS DK-7400 Herning Denmark

We present a numerical case study for modeling and simulation of upstream and downstream processes for monoclonal antibody (mAb) production. We apply a systematic and intuitive modeling methodology for an existing upstream process and downstream process. The resulting models are based on differential mass balances and kinetic expressions for the reactions and adsorption. Mass balances for the fedbatch reactor yield a model consisting of five ordinary differential equations (ODEs). The downstream process is conducted batchwise in a chromatographic column for capture of mAbs. Mass balances of the chromatographic column yield a system of partial differential equations (PDEs). The chromatographic model applies the nonlinear shrinking core adsorption isotherm model for transition between the mobile phase and the stationary phase. We apply a high-order spectral nodal continuous Galerkin scheme for spatial discretization of the chromatographic column, which result in a semi-discrete ODE formulation. The resulting simulation model, coupling the upstream and downstream processes in batchwise mAb production, can be used as a benchmark for numerical estimation, control and optimization studies.

关键词： Monoclonal antibody production fedbatch reactor capture chromatography shrinking core adsorption isotherm process modeling

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Bridging research gaps in breast cancer detection: An ensemble approach informed by bibliometric analysis

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Biomedical Signal Processing and control 2025年 109卷

作者： ZainEldin, Hanaa Bamaqa, Amna Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa A. Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

One of the leading causes of cancer-related death for women is still breast cancer, which highlights the importance of early and precise diagnosis techniques. Despite medical imaging and deep learning advances, current models fail to combine innovative object identification and classification methods, limiting their diagnostic performance. A bibliometric study of 1145 research articles identified key gaps, including the lack of integration between advanced object detection models like YOLOv9 and YOLOv10 with high-performance classification architectures such as EfficientNet B7, as well as the limited utilization of diverse imaging modalities. This study proposes a novel ensemble model for Breast Cancer Classification and Detection by combining YOLOv9, YOLOv10, and EfficientNet B7 (BC2D-Y/EN) to address these gaps. The proposed model combines the advanced object detection capabilities of YOLOv9 and YOLOv10 with the high-performance classification power of EfficientNet B7. This method aims to improve detection accuracy and computational efficiency in breast cancer identification. The proposed approach is validated using four publicly available datasets: CBIS-DDSM, CBIS-DDSM RGB breast cancer dataset, The Breast1 MRI dataset, and the Breast Ultrasound Tumor Object Detection dataset, all containing expert-annotated cases of benign and malignant tumors. The experimental findings indicate that the suggested model surpasses other models, achieving precision rates of 98.56% with EfficientNet B7, 98.3% with YOLOv9, and 100% with YOLOv9. These findings highlight the advantages of advanced models and diverse imaging methodologies to enhance diagnostic accuracy and effectiveness in breast cancer detection. © 2025 Elsevier Ltd

关键词： Deep learning

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Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions

arXiv

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arXiv 2024年

作者： Katharria, Aashu Rajwar, Kanchan Pant, Millie Velásquez, Juan D. Snášel, Václav Deep, Kusum Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Statistical Quality Control and Operations Research Unit Indian Statistical Institute Hyderabad India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Department of Industrial Engineering University of Chile Santiago Chile Instituto Sistemas Complejos de Ingeniería Santiago Chile Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Mathematics Indian Institute of Technology Roorkee Roorkee India

Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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An optimal power supervising strategy for a smart home assessed based on BPMN Framework

An optimal power supervising strategy for a smart home asses...

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International Conference on Sciences and Techniques of Automatic control and computer engineering (STA)

作者： Leila Lanouar Mouna Rekik Olfa Bouchaala Lotfi Krichen Multimédia & Web Department University of Gabes Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Gabes Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax University of Gabes National Engineering School of Sfax (ENIS) Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Sfax Gabes Tunis Department of Computer Engineering and Applied Mathematics Research Laboratory on Development and Control and Distributed Applications (ReDCAD) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis

ISBN: (纸本)9781665482622

For each smart home, the need of energy consumption supervision is necessary, which plays an important role to ensure the highest power quality and to enhance the stability of the whole grid. The current document implements a smart home supply strategy based on endless resources to reduce the electricity bill and confirm the energy balance. In this context, a proposed supervision algorithm operates in eight cases to reach optimal energy flow between renewable generators, home battery and grid in a smart home concept is presented. The system is evaluated using the framework “Business Process Model and Notation” (BPMN) Camunda basing on information stored in Firebase Cloud and results are presented in order to manifest the efficiency of this control strategy.

关键词： Renewable energy sources Energy consumption Power quality Process control Smart homes Power system stability Stability analysis

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Learning nonlinear feedforward: a Gaussian Process Approach applied to a Printer with Friction

arXiv

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arXiv 2021年

作者： van Meer, Max Poot, Maurice Portegies, Jim Oomen, Tom The Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands The Applied Analysis Group Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper is to develop an identification strategy for inverse models of systems with nonlinear dynamics of unknown structure using input-output data, which directly delivers feedforward signals for a-priori unknown tasks. To this end, inverse systems are regarded as noncausal nonlinear finite impulse response (NFIR) systems, and modeled as a Gaussian Process with a stationary kernel function that imposes properties such as smoothness and periodicity. The approach is validated experimentally on a consumer printer with friction and shown to lead to improved tracking performance with respect to linear feedforward. Copyright © 2021, The Authors. All rights reserved.

关键词： Friction

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Development of a rapid aptamer-chemiluminescence sensor for detecting glyphosate pesticide residue in soybeans

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Talanta 2024年 272卷 125741页

作者： Weng, Rui-Cian Tsou, Min-Cheng Lee, Jyun-Lin Tseng, Chao-Ming Huang, Yu-Fen Xiao, Yu-Lin Lu, Yen-Pei Chou, Wei-Chun Chang, Ruey-Feng Chuang, Chun-Yu Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Residue Control Division Agricultural Chemicals and Toxic Substances Research Institute Council of Agriculture Executive Yuan Taichung Taiwan Institute of Analytical and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Instrument Research Institute National Applied Research Laboratories Hsinchu Taiwan Department of Environmental Sciences College of Natural and Agricultural Sciences University of California RiversideCA United States Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Glyphosate (GLY) is a widely used herbicide worldwide, particularly in cultivating genetically modified soybeans resistant to GLY. However, routine multi-residue analysis does not include GLY due to the complexity of soybean matrix components that can interfere with the analysis. This study presented the development of an aptamer-based chemiluminescence (Apt-CL) sensor for rapidly screening GLY pesticide residue in soybeans. The GLY-binding aptamer (GBA) was developed to bind to GLY specifically, and the remaining unbound aptamers were adsorbed onto gold nanoparticles (AuNPs). The signal was in the form of luminol-H2O2 emission, catalyzed by the aggregation of AuNPs in a chemiluminescent reaction arising from the GLY-GBA complex. The outcomes demonstrated a robust linear relationship between the CL intensity of GLY-GBA and the GLY concentration. In the specificity test of the GBA, only GLY and Profenofos were distinguished among the fifteen tested pesticides. Furthermore, the Apt-CL sensor was conducted to determine GLY residue in organic soybeans immersed in GLY as a real sample, and an optimal linear concentration range for detection after extraction was found to be between 0.001 and 10 mg/L. The Apt-CL sensor exploits the feasibility of real-time pesticide screening in food safety. © 2024 Elsevier B.V.

关键词： Chemiluminescence

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Improved Method of Segmentation of Images From Space-Based Optoelectronic Observation Systems Based on Otsu's Algorithm with Global and Adaptive Thresholds

Improved Method of Segmentation of Images From Space-Based O...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Hennadii Khudov Temir Kalimulin Rostyslav Khudov Ihor Butko Viktoriia Burtseva Valeriy Burtsev Department of Radar Troops Tactic Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Theoretical and Applied Informatics V.N.Karazin Kharkiv National University Kharkiv Ukraine Department of Computer Sciences and Software Engineering Academician Y. Bugay International Scientific and Technical University Kyiv Ukraine Scientific Research Department of Military Standards Metrology Center of Military Standards Kharkiv Ukraine Department of Control Systems of Antiaircraft Missile Troops Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

An analysis of optoelectronic image, received from space-based observation system has been carried out. It has been found out, that this image has a complex structure and a unimodal intensity histogram. Otsu method is chosen as to segment the image. This method is easy and relatively quick, when talking of its realization. Methods of global and adaptive thresholding have been researched to perform image segmentation. Results of experimental research of segmentation of optoelectronic image from space-based observation system with use of Otsu method are developed. Ways for future research in this area are defined.

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