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arXiv 2025年

作者： Yu, Zhenyu Idris, Mohd Yamani Idna Wang, Pei Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming210098 China

Quantitative remote sensing inversion plays a critical role in environmental monitoring, enabling the estimation of key ecological variables such as vegetation indices, canopy structure, and carbon stock. Although vision foundation models have achieved remarkable progress in classification and segmentation tasks, their application to physically interpretable regression remains largely unexplored. Furthermore, the multi-spectral nature and geospatial heterogeneity of remote sensing data pose significant challenges for generalization and transferability. To address these issues, we introduce SatelliteCalculator, the first vision foundation model tailored for quantitative remote sensing inversion. By leveraging physically defined index formulas, we automatically construct a large-scale dataset of over one million paired samples across eight core ecological indicators. The model integrates a frozen Swin Transformer backbone with a prompt-guided architecture, featuring cross-attentive adapters and lightweight task-specific MLP decoders. Experiments on the Open-Canopy benchmark demonstrate that SatelliteCalculator achieves competitive accuracy across all tasks while significantly reducing inference cost. Our results validate the feasibility of applying foundation models to quantitative inversion, and provide a scalable framework for task-adaptive remote sensing estimation. The code and dataset are available at: https://***/YuZhenyuLindy/SatelliteCalculator Copyright © 2025, The Authors. All rights reserved.

关键词： Environmental monitoring

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Intelligent Control for Wind Turbines Connected to Utility Grid using MPC 23

Intelligent Control for Wind Turbines Connected to Utility G...

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23rd International Middle East Power Systems Conference, MEPCON 2022

作者： Al-Gabalawy, Mostafa Salama, Mohamed M. M. Abdel-Sattar, Ibrahim A. Darwish, Mohamed M. F. El-Safwa High Institute of Engineering Department of Electronic Communication and Computer Engineering Cairo Egypt Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Cairo Egypt Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9781665463638

Traditional methods for controlling wind turbines often rely on accurate modelling techniques to ensure reliable and productive interaction between the wind farms and the primary electricity grids in both grid-connected and autonomous modes. Additionally, because of the unpredictable and irregular wind speed, its natural output power behaves significantly erratically. Furthermore, to avoid the negative effects of excessive fluctuation in wind turbine output power in the wind farm besides the power grid, it is essential to properly estimate, plan, and manage the wind farm output power. In addition, the transmission of the control signal can leave a gap between itself and the wind energy conversion systems (WECS) due to the communication channels' time delay, which makes the WECS operation unstable. Specifically, when running over the permitted wind speed, wind energy conversion devices frequently pitch, which reduces the mechanical power to the rated level at the price of the blade, and drives shaft loads. In this regard, this paper suggests an intelligent control system for the synchronous generator wind turbines using the model predictive controller (MPC). The results highlighted the suggested controller's greater performance against changes in wind speed rather than PI controller. Further, the coordination of pitch/torque sharing depend on the analysis performed, both generator power output quality, in addition load circumstances are maximized in a variable-weight MPC technique. © 2022 IEEE.

关键词： Model predictive control

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Linguistic feature template integration for Chinese-Vietnamese neural machine translation

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Frontiers of computer Science 2022年第4期16卷 223-225页

作者： Zhiqiang YU Yantuan XIAN Zhengtao YU Yuxin HUANG Junjun GUO Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of Mathematics and Computer Science Yunnan Minzu UniversityKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

1 Introduction and main contributions Template-based approaches have achieved significant progress in low-resource neural machine translation(NMT)recently[1],such as the efficient works,NMT-GTM[2],SoftPrototype[3],***,most previous works only retrieve target sentence as template to generate translation,neglecting the utilization of linguistic feature that contained in the source sentence and template.

关键词： translation template such

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Energy efficient indoor localisation for narrowband internet of things

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1150-1163页

作者： Ismail Keshta Mukesh Soni Mohammed Wasim Bhatt Azeem Irshad Ali Rizwan Shakir Khan Renato RMaaliw III Arsalan Muhammad Soomar Mohammad Shabaz Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia Department of CSE University Centre for Research and Development Chandigarh UniversityMohaliPunjabIndia Department of Computer Science and Engineering National Institute of TechnologySrinagarIndia Asghar Mall College Rawalpindi Higher Education Department(HED)Govt.of the PunjabRawalpindiPakistan Department of Industrial Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Automation Electronics and Electrical EngineeringGdansk University of TechnologyGdańskPoland Arba Minch University Arba MinchEthiopia

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops *** high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these *** maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a *** on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot *** a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is *** investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT *** current work primarily considers the NOMA Narrow Band IoT network under a single EH ***,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance *** simulation,the proposed approach is successfully *** improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.

关键词： artificial inteligence detection of moving objects internet of things

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Research on intrusion detection algorithm based on dilated convolution and bidirectional gating cyclic unit

Research on intrusion detection algorithm based on dilated c...

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2022 International Conference on Algorithms, Microchips and Network Applications

作者： Liu, Xingyuan Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Laboratory of Yunnan Province Kunming650500 China

ISBN: (纸本)9781510653290

Intrusion detection based on machine learning plays an important role in the security protection of the network environment. In order to improve the accuracy of network intrusion detection, intrusion detection algorithm based on dilated convolution and a two-way gating unit is proposed. Firstly, dilated convolution (DCNN) is used to increase the receptive field of information and extract spatial features. Secondly, the bidirectional gated cyclic unit (bigru) model is used to capture the temporal features of data, and the attention mechanism is introduced to increase the calculation of feature importance. Compared with the classical machine learning classifier, this model has a high detection rate. The multi-classification experiments on the famous UNSW-NB15 data set show that the model has leading performance, and its classification accuracy can reach 97.14%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Intrusion detection

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Attention Predictions Related to Cinematic Montage Using Biometric Markers with Application in ADHD Disorder 10

Attention Predictions Related to Cinematic Montage Using Bio...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Motrescu, Emanuela Geman, Oana "Babes Bolyai" University Cluj Romania "Stefan cel Mare" University Faculty of Electric Engineering and Computer Science Department of Electronic Computers and Automation NeuroaestheticsLab Suceava Romania

ISBN: (纸本)9781665485579

The following case study targets the live deconstruction of a visual experience correlating attention-related body sensations that emerge with external visual stimuli resulting the improvement of attention. The attention deficit and hyperactivity disorder (ADHD) is one of the most common mental health disorders among children. It includes symptoms such as difficulty paying attention, hyperactivity and impulsive behavior. The disease appears before age of seven and lasts till adulthood. In contrast with traditional treatment (medication) our work focuses on the usage of biometric marker such as heart rate variability, applied in recovery through other treatments such as art therapy based on cinematic language techniques. We will be able to create a database that can be used to forecast pathologies within the autism spectrum and ADHD using various machine learning algorithm, by expanding the pilot project and including more participants. © 2022 IEEE.

关键词： Biometrics

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Grey Box Non-Recursive System Identification for a Third Order Electro-Hydraulic Actuator System: From Simulation to Practical Approaches 20

Grey Box Non-Recursive System Identification for a Third Ord...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Soon, Chong Chee Ghazali, Rozaimi Ghani, Muhamad Fadli Liew, Siaw-Hong Teng, Howe Cheng Zulkipli, Nur Farhanah Beh, Chong You Hanaffi, Mohd Saidi Department Of Engineering And Built Environment Penang Branch Campus Pulau Pinang Malaysia Centre For Robotics And Industrial Automation Fakulti Teknologi Dan Kejuruteraan Elektrik Melaka Malaysia Universiti Kuala Lumpur Perak Malaysia Faculty Of Computer Science And Information Technology Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9798350382310

engineering systems heavily rely on mathematical modeling, significantly influencing both system performance and the design of control systems. This paper thoroughly investigates the establishment of a third-order mathematical model for heavy machinery systems, with a specific emphasis on the ElectroHydraulic Actuator (EHA) system. The formulation of a double-acting EHA system model using a grey box system identification approach is outlined, encompassing the system's dynamics and behavior. In addition, the details of the EHA system experimental setup that is used for system identification are described. The identification process demonstrates promising performance, achieving an impressive accuracy of 89.1%, as highlighted in the results and discussion sections. © 2024 IEEE.

关键词： Hydraulic actuators

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Reduced CAD system for classifications of cancer types based on microarray gene expression data 9

Reduced CAD system for classifications of cancer types based...

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9th IEEE International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Bacha, Sawssen Taouali, Okba Liouane, Noureddine National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Image Monastir5019 Tunisia University of Tabuk Faculty of Computers and Information Technology Department of Computer Engineering Tabuk Saudi Arabia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Cancer is one of the deadliest diseases for human health. The classification of cancers poses many challenges in biomedical research because it allows an accurate and effective diagnosis and guarantees the success of medical treatments. In this paper, a new reduced computer-Aided Diagnosis (CAD) system is implemented under the MATLAB (version R2016a) environment to classifying four cancer subtypes. The results of the experiment are carried out with four sets of baseline data on the expression of cancer genes. To validate the proposed CAD system, different performance metrics such as sensitivity, specificity, accuracy, and F-Score are measured. The experimental analysis justifies the effectiveness of the proposed model and, therefore, this model can be considered as an effective tool to help radiologists for a better diagnosis. © 2022 IEEE.

关键词： Gene expression

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