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IFAC-PapersOnLine 2024年第15期58卷 330-335页

作者： Jiabao He Cristian R. Rojas Håkan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology 100 44 Stockholm Sweden

Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them involve a common intermediate step, that is to estimate the range space of the extended observability matrix. In this contribution, an optimized version of the parallel and parsimonious SIM (PARSIM), PARSIM opt , is proposed by using weighted least-squares. It not only inherits all the benefits of PARSIM but also attains the best linear unbiased estimator for the above intermediate step. Furthermore, inspired by SIMs based on the predictor form, consistent estimates of the optimal weighting matrix for weighted least-squares are derived. Essential similarities, differences and simulated comparisons of some key SIMs related to our method are also presented.

关键词： Subspace identification ARX model Markov parameters weighted least-squares

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A network-based bioinformatic analysis for identifying potential repurposable active molecules in different types of human cancers

A network-based bioinformatic analysis for identifying poten...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Brunetti, Martina Paci, Paola Fiscon, Giulia Sapienza University of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Karolinska Institutet Stockholm Sweden National Research Council of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' Rome Italy

ISBN: (纸本)9798350337488

Drug repurposing, also known as drug repositioning, is the process of identifying novel therapeutic indications for existing drugs, offering a cost-effective and time-efficient strategy to drug discovery. In this context, we developed a network-based algorithm, named SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by accounting for the interaction between the drug targets and disease-associated genes in the human interactome, implementing a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Following its successful applications to different disorders (such as viral infections and neurological diseases), in this study, we applied SAveRUNNER on a panel of 13 types of cancers using both disease-associated genes downloaded from widely-used databases and from gene expression data. © 2023 IEEE.

关键词： Drug Repurposing Network Medicine Network Theory

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Atmosphere-dependent combustion of Ganoderma lucidum biomass toward its enhanced transformability into green energy

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Environmental Science and Pollution Research 2024年第29期31卷 42372-42387页

作者： Cao, Hanlin Zhan, Hongda Qi, Jianying Lin, Sen Ren, Mingzhong Liang, Jiayu Evrendilek, Fatih He, Yao Liu, Jingyong Technical Centre for Soil Agriculture and Rural Ecology and Environment Ministry of Ecology and Environment Beijing100012 China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and Engineering Guangdong University of Technology Guangzhou510006 China Ministry of Ecology and Environment South China Institute of Environmental Sciences Guangzhou510275 China Department of Electrical and Computer Engineering College of Engineering Boston University BostonMA02215 United States

Globally, the circular efficiency of biomass resources has become a priority due to the depletion and negative environmental impacts of fossil fuels. This study aimed to quantify the atmosphere-dependent combustion of Ganoderma lucidum (GL) biomass and its thermodynamic and kinetic parameters toward enhancing its circularity and transformability characteristics. The GL combustion occurred in the three stages of moisture removal, volatile release, and coke combustion. Combustion performance characteristics were more favorable in the N2/O2 atmosphere than in the CO2/O2 atmosphere under the same heating rates. The rising heating rate facilitated the release of volatiles. According to the model-free methods of Ozawa-Flynn-Wall and Kissinger–Akahira–Sunose, the activation energies essential for the primary reaction were 283.09 kJ/mol and 288.28 kJ/mol in the N2/O2 atmosphere and 233.09 kJ/mol and 235.64 kJ/mol in the CO2/O2 atmosphere. The gaseous products of the GL combustion included CH4, H2O, C = O, CO, CO2, NH3, C = C, and C–O(H). Ash prepared in both atmospheres exhibited a tendency for slag formation, with oxy-fuel combustion lowering its risk. This study thus provides a theoretical and practical basis for transforming GL residues into a sustainable energy source. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Combustion

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Hidden Markov Models:Inverse Filtering,Belief Estimation and Privacy Protection

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Journal of Systems Science & Complexity 2021年第5期34卷 1801-1820页

作者： LOURENCO Ines MATTILA Robert ROJAS Cristian R. HU Xiaoming WAHLBERG Bo Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Division of Optimizations and Systems Theory School of Engineering SciencesKTH Royal Institute of TechnologyStockholmSweden

A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy *** paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from actions,and privacy enforcement in such a ***,the authors discuss how HMM parameters and sensor measurements can be reconstructed from posterior distributions of an HMM ***,the authors consider a rational decision-maker that forms a private belief(posterior distribution)on the state of the world by filtering private *** authors show how to estimate such posterior distributions from observed optimal actions taken by the *** the setting of adversarial systems,the authors finally show how the decision-maker can protect its private belief by confusing the adversary using slightly sub-optimal *** range from financial portfolio investments to life science decision systems.

关键词： Belief estimation counter-adversarial systems hidden Markov models inverse decision making inverse filtering

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Cascaded Design for Finite-Time Trajectory Tracking control of an Underactuated Surface Vehicle 8

Cascaded Design for Finite-Time Trajectory Tracking Control ...

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8th International Conference on control Science and Systems engineering, ICCSSE 2022

作者： Chen, Ziyin Ge, Shuzhi Sam Beijing Institute of Space Mechanics & Electricity Department of Control Engineering Beijing China National University of Singapore Department of Electrical & Computer Engineering Singapore Singapore

ISBN: (纸本)9781665484138

This paper discusses the cascaded design for finitetime trajectory tracking control of an underactuated unmanned surface vehicle (USV). The tracking error dynamics is established with the guidance of a virtual reference vehicle, and transformed into a new cascaded structure, so the control of the overall system can be equivalent to stabilization of two interconnected subsystems separately, which can facilitate the control design by verifying the properties of the interconnection instead of iterative constructing Lyapunov functions for the whole system. The nonsingular fast terminal sliding mode (NFTSM) method is applied to stabilize the kinematic and dynamic subsystems in the present of model uncertainties, which can drive the states to the origin in finite time and avoid the possible singularity during the control phase. The stability of the cascaded system is guaranteed by verifying the satisfaction of the sufficient conditions. Numerical simulation results are provided to demonstrate the effectiveness of the proposed cascaded design. © 2022 IEEE.

关键词： Unmanned surface vehicles

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Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems 19

Individually-guided Evolutionary Algorithm for Solving Multi...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Wang, XiaoLing Kang, Qi Zhou, MengChu Tongji University Department of Control Science and Engineering Shanghai China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States

ISBN: (纸本)9781665472432

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants. © 2022 IEEE.

关键词： Evolutionary algorithms

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Fuzzy control design for time-delay systems under adaptive event-triggered mechanism

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Neural Computing and Applications 2024年第9期36卷 4727-4739页

作者： Du, Zhenbin Qu, Zifang Kao, Yonggui Wu, Zhaojing School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Mathematics and Information Science Shandong Technology and Business University Shandong Yantai264005 China Department of Mathematics Harbin Institute of Technology Weihai264209 China School of Mathematics and Information Sciences Yantai University Shandong Yantai264005 China

This manuscript studies the fuzzy H∞ control design problem for nonlinear time-varying delay systems in fuzzy form via adaptive event-triggered mechanism. The fuzzy model and an adaptive event-triggered controller are connected in closed-loop form. A looped-functional is established to conduct stability analysis, and the main feature of this functional can fully reflect the sampling feature of control systems. Thus, the achieved results are less conservative. The Wringter inequality is also used to further relax stability analysis. Finally, sufficient conditions are achieved for the existence of event-triggered fuzzy controller, and the resulting closed-loop system reaches an H∞ control performance. Two examples verify the efficiency of the presented control strategy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： Time delay

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Dorsal Hand Vascular Imaging System

Dorsal Hand Vascular Imaging System

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2024 IEEE Ural-Siberian Conference on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2024

作者： Mizinov, Pavel V. Konnova, Natalia S. Kravchenko, Oleg V. Egorov, Dobroslav P. Osipenko, Gavriil V. Bauman Moscow State Technical University Department Of Information Security Moscow Russia Computer Science And Control Of Ras Federal Research Center Moscow Russia Kotel'nikov Institute Of Radio Engineering And Electronics Of Ras Moscow Russia Moscow Aviation Institute Moscow Russia

ISBN: (纸本)9798350362893

Biometric identification methods using vascular modalities (vein pattern of the finger, wrist, ventral and dorsal part of the metacarpal) use near infrared (NIR) illumination to obtain an image of the vein pattern. The 'classic' attack scenario for this type of biometric system involves the use of a photograph of the user's vein pattern, printed on ordinary office paper and obtained under NIR illumination, as a presentation attack tool. This attack scenario has been well studied by researchers and there are several reliable ways to detect such an attack. New biometric attack vectors on these systems involve the use of attack tools created using NIR reflective compounds. In order to develop effective methods to detect and counter this type of attack, it is necessary to study the characteristics of the attack tool in each of the 750-950 nm NIR sub-bands of the venous channel imaging band. In this paper, the authors present the stages of creating a hardware tool for obtaining an image of the vascular pattern to solve the problem at hand. © 2024 IEEE.

关键词： Biometrics

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Cybersecurity of SMART Technologies Using Deep Learning Methods 8

Cybersecurity of SMART Technologies Using Deep Learning Meth...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Milov, Oleksandr Rahimova, Irada Havrylova, Alla Golovashych, Sergii Aksonova, Iryna Pecherytsia, Dmytro National Technical University 'Kharkiv Polytechnic Institute' Department of Cyber Security Kharkiv Ukraine Azerbaijan Technical University Department of 'Computer Technologies' Baku Azerbaijan National Technical University 'Kharkiv Polytechnic Institute' Department of Software Engineering and Intelligent Control Technologies Kharkiv Ukraine

ISBN: (纸本)9798350354423

The concept of 'smart', the concept of smart technology, and its main elements are considered. An analysis of the factors influencing the formation and development of the concept of smart technology is provided. Aspects of the development of smart technology are highlighted. A description of deep learning methods is given and their classification is proposed. The areas of application of the described methods in various cybersecurity applications are identified, including detection of intrusions and malware, analysis of network traffic and some other tasks related to smart technologies. © 2024 IEEE.

关键词： Network intrusion

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