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Data-driven output regulation control for constrained linear systems

science China(information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Improving Next Tokens via Second-to-Last Predictions with Generate and Refine 23rd

Improving Next Tokens via Second-to-Last Predictions with ...

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23rd International Symposium on Intelligent Data Analysis, IDA 2025

作者： Schneider, Johannes Department of Computer Science and Information Systems University of Liechtenstein Vaduz Liechtenstein

ISBN: (纸本)9783031913976

Autoregressive language models like GPT aim to predict next tokens, while autoencoding models such as BERT are trained on tasks such as predicting masked tokens. We train a decoder-only architecture for predicting the second to last token for a sequence of tokens. Our approach yields higher computational training efficiency than BERT-style models by employing a structured deterministic approach to masking tokens. We use our model to improve the next token predictions of a standard GPT by combining both predictions in a "generate-then-refine" approach. We demonstrate on different variants of GPT-2 and different datasets that (not unexpectedly) second to last token predictions are much more accurate, i.e., more than 15% higher accuracy than standard next token predictions. The "generate-then-refine" approach also demonstrates notable improvements in next-token predictions, yielding smaller yet consistent and significant gains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Bidirectional decoder-only model Generate-then-refine Large Language Models

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Recognition techniques of medicinal plants: A review

Recognition techniques of medicinal plants: A review

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Tiwari, Nidhi Gupta, Bineet Kumar Sharma, Rajat Department of Computer Science and Information Systems SRMU-Barabanki India

ISBN: (纸本)9781032669663

Pharmaceutical companies are gaining interest in medicinal plants due to their lower costs and fewer side effects as compared to modern drugs. These facts have led to many researchers expressing an interest in the study of automatic medicinal plant recognition. As a result of the development of strong classifiers that can categorize medicinal plants reliably in real time, several avenues for progress are available. This study focuses on the effectiveness and reliability of various machine learning techniques used in recent years for classifying plants using leaf pictures. In the review, several machine learning classifiers are discussed for detecting leaves and extracting significant leaf features. In this study, we investigate how these classifiers perform when classifying leaf ismages according to standard features such as shape, veins, texture, and a combination of these factors. A discussion of some of the most important ongoing studies and potential for improvement in this area wraps up our discussion of the leaf databases that is publicly available for pre-programmed plant recognition. © 2024 The Author(s).

关键词： Textures

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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on computer Vision Techniques

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computer Modeling in Engineering & sciences 2024年第4期139卷 759-782页

作者： Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

关键词： computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations

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Machine Learning Models with Neutrosophic Numbers for Cloud Security Analysis and Smart Grid Control of Renewable Energy

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Neutrosophic Sets and systems 2025年 85卷 960-973页

作者： Fakhry, Ahmed E. Gomaa, Mamdouh Metwaly, Ahmed A. Abdelhafeez, Ahmed Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Department of Computer Science Faculty of Information Technology Amman Arab University Amman11953 Jordan Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt

Machine learning (ML) enables difficult tasks to be completed independently. In a smart grid (SG), computers and mobile devices may make it easier to monitor security, control interior temperature, and perform routine maintenance. The ability of smart grids to identify cyberattacks is essential for assessing the operation's reliability and availability. This essay emphasizes the integrity of cyberattacks using fake data in the physical layers of smart grids (SGs). This paper analyzes data transmission security and proposes a novel approach to smart grid energy management. Here, renewable energy sources are used to assess the smart grid network's energy efficiency, and the network has been monitored for cloud computing security assessments. We use an interval trapezoidal neutrosophic number model to deal with uncertainty information and rank the best ML model under different evaluation matrices. We use the k-nearest neighbor with k= 3 to 12. The CoCoSo method is used to rank ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Learning to rank

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Detection and Classification of Hemorrhages in Retinal Images

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computer systems science & Engineering 2023年第2期44卷 1601-1616页

作者： Ghassan Ahmed Ali Thamer Mitib Ahmad Al Sariera Muhammad Akram Adel Sulaiman Fekry Olayah College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Studies in Computer Science University of MysoreMysoreIndia

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy(DR).Hemorrhages is theﬁrst clinically visible symptoms of *** paper presents a new technique to extract and classify the hemorrhages in fundus *** normal objects such as blood vessels,fovea and optic disc inside retinal images are masked to distinguish them from *** masking blood vessels,thresholding that separates blood vessels and background intensity followed by a newﬁlter to extract the border of vessels based on orienta-tions of vessels are *** masking optic disc,the image is divided into sub-images then the brightest window with maximum variance in intensity is *** the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological *** are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet *** different types of Support Vector Machine(SVM),Linear SVM,Quadratic SVM and Cubic SVM classiﬁer are applied to classify the candidate dark regions as either hemor-rhages or *** efﬁcacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in *** performance of the method is measured based on average sensitivity,speciﬁcity,F-score and *** results show the Linear SVM classiﬁer gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to speciﬁcity Quadratic SVM gives better result as compared to other SVMs.

关键词： Diabetic retinopathy hemorrhages adaptive thresholding support vector machine

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Exploring the Effectiveness of Graph-based Computational Models in COVID-19 Research

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Operations Research Forum 2024年第3期5卷 80页

作者： Boadu, Dennis Opoku Appati, Justice Kwame Mensah, Joseph Agyapong Department of Computer Science University of Ghana Accra Ghana Department of Computer Science and Information Systems Ashesi University Berekuso Ghana

The world has witnessed various scientific disciplines’ rapid growth and advancement, leading to groundbreaking discoveries and advances in multiple fields in recent years. One such field that has gained significant attention, particularly during the COVID-19 pandemic, is the application of graph theory techniques in studying the spread and mitigation of the virus. In this paper, we delve into the intricacies of graph theory and its utilization in analyzing COVID-19, shedding light on the innovative approaches researchers worldwide employ. Also, the study evaluates the various implementation of graph theories in spreading and controlling the virus using diverse datasets. The researchers retrieved several works in the COVID-19 and graph theory field from digital databases. However, studies deducted that GT approaches, algorithms and techniques offer insights into transmission hotspots, spread dynamics in social, control and mobility networking, vaccination optimization, evaluation of interventions and epidemic prediction, among other valuable findings. Limitations and future directions were also directed in the study. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Control and mobility networks COVID-19 Edges Graph Theory (GT) Nodes Shortest path (Djikstra) Social

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A multi-modal deep learning system for Arabic emotion recognition

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International Journal of Speech Technology 2023年第1期26卷 123-139页

作者： Abu Shaqra, Ftoon Duwairi, Rehab Al-Ayyoub, Mahmoud Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

Emotion analysis is divided into emotion detection, where the system detects if there is an emotional state, and emotion recognition where the system identifies the label of the emotion. In this paper, we provide a multimodal system for emotion detection and recognition using Arabic dataset. We evaluated the performance of both audio and visual data as a unimodal system, then, we exposed the impact of integrating the information sources into one model. We examined the effect of gender identification on the performance. Our results show that identifying speaker’s gender beforehand increases the performance of emotion recognition especially for the models that rely on audio data. Comparing the audio-based system with the visual-based system demonstrates that each model performs better for a specific emotional label. 70% of the angry labels were predicted correctly using the audio model while this percentage was less using the visual model (63%). The accuracy obtained for the surprise class was (40.6%) using the audio model while it was (56.2%) using the visual model. The combination of both modalities improves accuracy. Our final result for the multimodal system was (75%) for the emotion detection task and (60.11%) for emotion recognition task and these results are among the top results achieved in this field and the first which focus on Arabic content. Specifically, the novelty of this work is expressed by exploiting deep learning and multimodal models in emotion analysis and applying it on a natural audio and video dataset for Arabic speaking persons. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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WifiGO: Assessment of PUV Viability for Opportunistic Coverage Mapping of WiFi Networks

WifiGO: Assessment of PUV Viability for Opportunistic Covera...

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2024 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2024

作者： Pecson, Jeff Nereo G. Tan, Wilson M. Festin, Cedric Angelo M. Dulva, Philip Angelo Y. Networks and Distributed Systems Group Department of Computer Science University of the Philippines - Diliman Metro Manila Philippines

ISBN: (纸本)9798331521219

Current methods in mapping the availability of WiFi networks, such as crowdsourcing platforms (e.g. Project BASS and CoverageMap) and dedicated wardriving, face limitations in terms of data recency, volume, and cost-effectiveness. Due to the downsides of both these methods, opportunistic wardriving is proposed which utilizes public utility vehicles (PUVs). This study investigates the feasibility of PUVs as potential wardriving vehicles for opportunistic coverage mapping. This method is cost-efficient and removes the problem of data recency because of its continuous daily trips. The findings suggest that opportunistic wardriving with PUVs is viable for wardriving with its capability to detect a high volume of access points (APs). Multiple runs within the University of the Philippines Diliman campus showed the PUVs’ effectiveness in detecting a significant number of APs, with higher success rates at closer distances. Comparatively, warwalking detected fewer APs, but there was a significant overlap between the methods. The study also highlighted significant differences in detected AP types, with wardriving finding more mobile hotspots and miscellaneous devices than warwalking. Overall, the results underline the effectiveness of PUVs in providing extensive WiFi coverage data and further enhancements to the detection system could optimize the approach. ©2024 IEEE.

关键词： Wireless local area networks (WLAN)

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