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arXiv 2022年

作者： Fu, Zao Cucuzzella, Michele Cenedese, Carlo Yu, Wenwu Scherpen, Jacquelien M.A. School of Cyber Science and Engineering Southeast University Nanjing211189 China Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Physikstrasse 3 Zürich8092 Switzerland

In this paper we propose an original distributed control framework for DC mcirogrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on the dual theory, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC mcirogrid and the designed distributed control system in a power preserving way, we steer the DC microgrid's state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on the singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework. © 2022, CC BY-NC-SA.

关键词： Perturbation techniques

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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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A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

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computer Modeling in engineering & Sciences 2020年第11期125卷 579-596页

作者： Xing Deng Haijian Shao Liang Shi Xia Wang Tongling Xie School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China Department of Electrical and Computer Engineering National University of Singapore117583Singapore The People’s Hospital of RUGAO Nantong226500China

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff *** of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation *** this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of *** benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

关键词： COVID-19 detection deep learning transfer learning pre-trained models

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Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5906797页

作者： Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India School of Computer Science and Applications REVA University Bangalore India Department Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India King Abdulaziz University Jeddah Saudi Arabia Indus Institute of Information and Communication Technology Indus University Gujarat Ahmedabad India Computer Science and Engineering Department University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Technological University of Madhya Pradesh Bhopal462033 India Department of Electrical Engineering Tamale Technical University Tamale Ghana

The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words. © 2022 B. Sumathy et al.

关键词： Decision making

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Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks

Non-Euclidean Monotone Operator Theory with Applications to ...

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IEEE Conference on Decision and control

作者： Alexander Davydov Saber Jafarpour Anton V. Proskurnikov Francesco Bullo Center for Control Dynamical Systems and Computation University of California Santa Barbara USA School of Electrical and Computer Engineering Georgia Institute of Technology Department of Electronics and Telecommunications Politecnico di Torino Turin Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We provide a novel transcription of monotone operator theory to the non-Euclidean finite-dimensional spaces ℓ 1 and ℓ ∞ . We first establish properties of mappings which are monotone with respect to the non-Euclidean norms ℓ 1 or ℓ ∞ . In analogy with their Euclidean counterparts, mappings which are monotone with respect to a non-Euclidean norm are amenable to numerous algorithms for computing their zeros. We demonstrate that several classic iterative methods for computing zeros of monotone operators are directly applicable in the non-Euclidean framework. We present a case-study in the equilibrium computation of recurrent neural networks and demonstrate that casting the computation as a suitable operator splitting problem improves convergence rates.

关键词： Casting Analytical models Recurrent neural networks Machine learning Aerospace electronics control systems Robustness

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Macroproperties vs. Microstates in the Classical Simulation of Critical Phenomena in Quench Dynamics of 1D Ising Models

arXiv

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arXiv 2023年

作者： Mitra, Anupam Albash, Tameem Blocher, Philip Daniel Takahashi, Jun Miyake, Akimasa Biedermann, Grant W. Deutsch, Ivan H. Center for Quantum Information and Control Department of Physics and Astronomy University of New Mexico New Mexico87131 United States Department of Electrical and Computer Engineering University of New Mexico New Mexico87131 United States Center for Quantum Research and Technology Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Oklahoma73019 United States

We study the tractability of classically simulating critical phenomena in the quench dynamics of one-dimensional transverse field Ising models (TFIMs) using highly truncated matrix product states (MPS). We focus on two paradigmatic examples: a dynamical quantum phase transition (DQPT) that occurs in nonintegrable long-range TFIMs, and the infinite-time correlation length of the integrable nearest-neighbor TFIM when quenched to the critical point, where the quantities of interest involve equal time one- and two- point correlation functions, which we associate with macroproperties. For the DQPT, we show that the order parameters can be efficiently simulated with heavy truncation of the MPS bond dimension. This can be used to reliably extract critical properties of the phase transition, including critical exponents, even when the full many-body state is not simulated with high fidelity. The long-time correlation length near the critical point is more sensitive to the full many-body state fidelity, and generally requires a large bond dimension MPS. Nonetheless, this can still be efficiently simulated with strongly truncated MPS because it can be extracted from the short-time behavior of the dynamics where entanglement is low. Our results provide illustrations of scenarios where accurate calculation of the full many-body state (microstate) is intractable due to the volume-law growth of entanglement, yet a precise specification of an exact microstate may not be required when simulating macroproperties that play a role in phases of matter of many-body systems. We also study the tractability of simulation using truncated MPS based on quantum chaos and equilibration in the models. We find a counterintuitive inverse relationship, whereby local expectation values are most easily approximated for chaotic systems whose exact many-body state is most intractable. Copyright © 2023, The Authors. All rights reserved.

关键词： Ising model

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Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices

arXiv

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arXiv 2022年

作者： Burrello, Alessio Pagliari, Daniele Jahier Risso, Matteo Benatti, Simone Macii, Enrico Benini, Luca Poncino, Massimo The Department of Electrical Electronic and Information Engineering University of Bologna Bologna40136 Italy The Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy The Department of Information Technology and Electrical Engineering The ETH Zurich Zurich8092 Switzerland The Department of Control and Computer Engineering Politecnico di Torino Turin Italy The Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino Turin Italy

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial sensor. Unfortunately, most standard approaches of this kind rely on hand-tuned parameters, which impair their generalization capabilities and their applicability to real data in the field. In contrast, methods based on deep learning, despite their better generalization, are considered to be too complex to deploy on wearable devices. In this work, we tackle these limitations, proposing a design space exploration methodology to automatically generate a rich family of deep Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a single "seed" model. Our flow involves a cascade of two Neural Architecture Search (NAS) tools and a hardware-friendly quantizer, whose combination yields both highly accurate and extremely lightweight models. When tested on the PPG-Dalia dataset, our most accurate model sets a new state-of-the-art in Mean Absolute Error. Furthermore, we deploy our TCNs on an embedded platform featuring a STM32WB55 microcontroller, demonstrating their suitability for real-time execution. Our most accurate quantized network achieves 4.41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47.65 mJ and a memory footprint of 412 kB. At the same time, the smallest network that obtains a MAE © 2022, CC BY-SA.

关键词： Deep neural networks

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Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

arXiv

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arXiv 2023年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Li, Li Gong, Wei Jin, Jian Jiao, Zhenzhen The School of Informatics Xiamen University Fujian China The Department of Electrical and Computer Engineering Western University ON Canada The Department of Control Science and Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The Research Institute of Industrial Internet of Things CAICT China The iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China

Cloud-aided mobile edge networks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore an overbooking-driven aforehand cross-layer matching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services;and an onsite cross-layer matching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Multi-Exposure Image Fusion Using Edge-Aware Network

Multi-Exposure Image Fusion Using Edge-Aware Network

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International Conference on Emerging Technologies, ICET

作者： Ghazala Aslam Muhammad Imran Bushra Haq Anayat Ullah Ersin Elbasi Department of Computer Science BUITEMS Quetta Pakistan Department of Electrical Engineering FICT BUITEMS Quetta Pakistan Control Automotive and Robotics Lab NCRA Rawalpindi Pakistan Department of Computer Science FICT BUITEMS Quetta Pakistan Department of Electronic Engineering FICT BUITEMS Quetta Pakistan College of Engineering & Technology American University of Middle East Egaila Kuwait

ISBN: (纸本)9781665459938

The natural dynamic range is much broader than the dynamic range a digital camera can capture. As a result, the images captured by ordinary cameras suffer from visibility issues; some images look overly bright, while others look overly dark. Therefore, in the research article, we propose a solution for image fusion, where multiple images can be combined to produce a single comprehensive image with better perceptibly. To achieve that, we designed a feature extraction block, which can extract features of an image at different levels and combines them to get more refined and detailed feature maps. In addition to that, we also designed a custom layer to extract edges, and those edges later are combined with the feature maps to give a fused image. We evaluated and compared our proposed architecture with some of the latest architecture, designed for the same reason, in terms of Image Structure Similarity and Human Perception-Based metrics. Experimental results corroborate that the proposed scheme outperforms the contemporary techniques for image fusion.

关键词： Image edge detection Lighting Deep architecture Dynamic range Feature extraction Digital cameras Image fusion

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