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TechRxiv 2023年

作者： Hamandi, Mahmoud Al-Ali, Ismail Seneviratne, Lakmal Franchi, Antonio Zweiri, Yahya The Department of Aerospace Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates The Department of Mechanical Engineering Khalifa University Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Abu Dhabi United Arab Emirates The Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands The Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy LAAS CNRS Université de Toulouse Toulouse31000 France Khalifa University Abu Dhabi United Arab Emirates Center for Artificial Intelligence and Robotics New York University Abu Dhabi Saadiyat Island Abu Dhabi129188 United Arab Emirates

This paper presents a novel optimization-based full-pose trajectory tracking method to control overactuated multirotor aerial vehicles with limited actuation abilities. The proposed method allocates feasible control inputs to track a reference trajectory, while ensuring the tracking of the reference position, and while tracking the closest feasible attitude. The optimization simultaneously searches for a feasible trajectory and corresponding feasible control inputs from the infinite possible solutions, while ensuring smooth control inputs. The proposed real-time algorithm is tested in extensive simulation on multiple platforms with fixed and actuated propellers. The simulation experiments show the ability of the proposed approach to exploit the complex set of feasible forces and moments of overactuated platforms while allocating smooth feasible control inputs. © 2023, CC BY.

关键词： Trajectories

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer Systems Science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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Online Evaluation of The Distribution System Resiliency Criteria Using Choquet-Based Graph Technique

SSRN

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SSRN 2022年

作者： Ranjbar, Soheil Marzband, Mousa Iqbal, Atif Department of Electrical Engineering Velayat University P.O. Box 99111 Iranshahr31311 Iran Northumbria University Electrical Power and Control Systems Research Group Ellison Place Newcastle upon TyneNE1 8ST United Kingdom Center of research excellence in renewable energy and power systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical Engineering Qatar University Doha Qatar

Nowadays, due to the tremendous power network expansions, system operators are dealing with different natural events resulting system disconnections and loads failures. Resiliency criterion is introduced as the level of distribution network readiness to maintain their interconnections with respect to severe weather conditions. With this regard, there are specific techniques to present secure operating conditions (OCs) for future power grids. An online and non-model-based scheme of evaluating distribution system resiliency is presented in this paper with the aim of supplying critical loads (CLs) within severe weather conditions. Further, the system resiliency is evaluated by developing the system resiliency criteria as a multi-metric decision making problem and then determine the problems using Choquet integral and graph theoretic methodology. In addition, the proposed scheme is a non-model-based technique suitable for all possible distribution system configurations. Specifically, the corresponding planning schemes with proper resiliency criteria are represented by identifying nominated CLs through each OC. The effectiveness of the proposed scheme is observed by two different cases studies consisting of IEEE 123-bus distribution network and two CERTS microgrids. Finally, in the simulation results, a protective switching strategy is represented to design the system resiliency criteria through future power distribution systems equipped with microgrids. © 2022, The Authors. All rights reserved.

关键词： Electric power transmission networks

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A novel deep learning method for textual sentiment analysis

arXiv

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arXiv 2021年

作者： Sadr, Hossein Solimandarabi, Mozhdeh Nazari Pedram, Mir Mohsen Teshnehlab, Mohammad Department of Computer Engineering Rasht Branch Islamic Azad University Rasht Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Industrial Control Center of Excellence Faculty of Electrical and Computer Engineering K. N. Toosi University Tehran Iran

Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the performance is also explored. Based on the empirical results, the proposed model not only has higher classification accuracy and can extract informative words but also applying incremental transfer learning can significantly enhance the classification performance. © 2021, CC BY.

关键词： Sentiment analysis

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Robustcontainment control of heterogeneous multi-agent systems with structured uncertainty and external disturbances

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International Journal of Robust and Nonlinear control 2021年第2期32卷

作者： Hajar Atrianfar Abolghasem Karimi Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

This article considers the robust containment control problem of heterogeneous linear multi-agent systems subject to structured uncertainties and external disturbances. By introducing a new neighborhood error of follower agents, the containment control problem can be formulated into output regulation problem. By employing a distributed compensator and robust control approach, sufficient conditions in terms of linear matrix inequalities are derived to ensure that all followers asymptotically converge to the convex hull spanned by the leaders with the prescribed performance under fixed topology. A numerical example is provided to verify the effectiveness of the proposed method.

关键词： external disturbances controller heterogeneous linear multi-agent system linear fractional transformation robust containment control structured uncertainties

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Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

Enhanced Subspace Distribution Matching for Fast Visual Doma...

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作者： Kang, Qi Yao, Siya Zhou, Mengchu Zhang, Kai Abusorrah, Abdullah Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering Faculty of Engineering and Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21481 Saudi Arabia Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions. © 2014 IEEE.

关键词： Image classification

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Radiomics Model Based on Computed Tomography Images for Prediction of Radiation-Induced Optic Neuropathy Following Radiotherapy of Brain and Head and Neck Tumors

SSRN

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SSRN 2024年

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Noraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Computer Science University of Copenhagen Copenhagen Denmark Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and Methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. The radiomics features were extracted from the area segmented by Swin UNETR version 2, which included the right and left optic nerves and chiasm. We ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31% of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97% and 95% respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision can predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Coordination control of Double-Integrator Systems with Time-Varying Weighted Inputs

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IFAC-PapersOnLine 2020年第2期53卷 2489-2494页

作者： Marcus Greiff Zhiyong Sun Anders Robertsson The LCCC Linnaeus Center and the ELLIIT Excellence Center at Lund University Sweden Department of Automatic Control Lund University Sweden Department of Electrical Engineering Eindhoven University of Technology the Netherlands

This paper considers coordination control of double-integrator systems and proposes general control laws involving time-varying inputs. The nominal control input is weighted by time-varying (time-dependent or state-dependent) positive definite matrices, providing more freedoms in defining the control tasks. We present sufficient conditions to ensure the asymptotic convergence of double-integrator networked systems in this context, and support the theoretical results by several application examples. This includes distance-based multi-agent formation control and power network systems with unknown inertia matrices.

关键词： Coordination control double integrator weighted input nonlinear control

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Intelligent Fault Tolerant Energy Management System Using First-Price Sealed-Bid Algorithm for Microgrids

SSRN

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SSRN 2022年

作者： Jonban, Mansour Selseleh Romeral, Luis Marzband, Mousa MCIA Center Electronic Engineering Department Universitat Politecnica de Catalunya Terrassa Spain Northumbria University Electrical Power and Control Systems Research Group Ellison Place NE1 8ST Newcastle upon Tyne United Kingdom Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21589 Saudi Arabia

At present context, computer based algorithms lead modern power industry while monitoring and controlling distributed energy resources. In fact, with prevalence of artificial intelligence algorithm, stability and fault tolerant in power or smart control side become a critical consideration to be investigated in avoiding blackouts. This paper presents a fault tolerant multi-agent based framework to control and manage power into a microgrid. In this paper, a tender framework is used to control operation of agents and develop mutual cooperation. Further, agents are considered as bidders with distinctive decision making capabilities that assist them to participate in tender and gain reward against sharing power. The iterative first-price sealed-bid algorithm has implemented to optimum cost and allocate energy among bidders. The proposed framework guarantees system in avoiding blackouts and creates a possibility to have easy redevelopment in MG, along with iterative optimal algorithm minimizes cost in MG and also reduces purchasing power from the grid. Eventually, theoretical analysis and results demonstrate that proposed technique provides a robust and stable control which could guarantee stability, flexibility and fault tolerant in the system. The results of simulation indicate that during a day power sharing by the grid and injected power by MG are 146.92 kW is 214.34 kW, respectively. © 2022, The Authors. All rights reserved.

关键词： Smart power grids

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