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An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

computers, Materials & Continua 2021年第11期69卷 2475-2491页

作者： Saddam Bekhet Monagi H.Alkinani Reinel Tabares-Soto M.Hassaballah Faculty of Commerce South Valley UniversityQenaEgypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Electronics and Automation Universidad Autónoma de ManizalesManizales170001Colombia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and *** situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily *** hospitals possess X-ray or CT imaging equipments that can be used for early detection of *** by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT ***,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several *** paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN *** proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are *** outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.

关键词： Artificial intelligence COVID-19 chest CT chest X-ray deep learning

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Self-adaptive image codec for intelligent vision sensor networks

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International Journal of Information Technology (Singapore) 2022年第5期14卷 2533-2540页

作者： Lin, Ming-Yi Chen, Ching-Han Chen, Shou-Cheng Liu, Chao-Tsu Department of Computer Science and Information Engineering Taipei City University of Science and Technology No.2 Xueyuan Rd. Beitou District Taipei 11202 Taiwan Machine Intelligence and Automation Technology (MIAT) Laboratory Department of Computer Science and Information Engineering National Central University No. 300 Jhongda Rd. Jhongli District Taoyuan 32001 Taiwan

Wireless sensor networks (WSNs) play a crucial role in various multimedia services and applications. The main challenge in designing WSNs is coping with hardware resources constraints of vision sensors that involve limited energy, insufficient memory, limited computational capability, and low bandwidth in sensor nodes. In this manuscript, first, WSNs based on self-adaptive protocol was proposed. Next, an embedded processor with hardware resource constrained is applied to make a Joint Photographic Experts Group (JPEG) codec allowing a dynamic range compression ratio for the bandwidth adaptation design of wireless image transmission, which can be used in an un-reliable communication environment. Finally, the system prototype with proposed self-adaptive protocol solution can improve the reliability of real-time image transmission. Experimental results demonstrate our image codec that is more efficient than SPIHT algorithm (Ammah in Inform Med Unlocked 15:100183, 2019). © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Hardware resource-constrained Intelligent vision sensor networks JPEG codec Self-adaptive protocol

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Dynamic Cascaded Flow-Shop Scheduling Using an Evolutionary Greedy Algorithm

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IEEE Transactions on Evolutionary Computation 2025年

作者： Li, Qiu-Ying Pan, Quan-Ke Wang, Ling Gao, Liang Li, Wei-Min Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Tsinghua University Department of Automation Beijing100084 China Huazhong University of Science and Technology State Key Lab of Intelligent Manufacturing Equipment and Technology Wuhan430074 China Shanghai University School of Computer Engineering and Science Shanghai200000 China

Production processes are inherently complex, often involving multiple production phases from raw materials to finished products, making joint scheduling problems a focal point of research. This paper addresses the dynamic cascaded flowshop joint scheduling problem, which integrates a distributed permutation flowshop in Phase 1 and a hybrid flowshop in Phase 2. The challenge involves both initial scheduling and dynamic response mechanisms for new job insertions. We propose an evolutionary greedy algorithm (EGA) aimed at minimizing total flowtime. The EGA employs a multi-start cooperative framework tailored to problem characteristics, alternating between a population-based evolutionary greedy algorithm for Phase 1 and an elitist-based greedy algorithm for Phase 2 to generate a robust and complete schedule. Upon new job insertions, three heuristic-driven response strategies enhance solution stability and adaptability. Additionally, phase-specific hybrid local search operators and an adaptive insertion strategy, leveraging knowledge-based problem properties, further improve solution quality and search efficiency. The experimental results indicate that the EGA outperforms five state-of-the-art algorithms, achieving an average improvement of 39% in RPI values across 480 instances. Moreover, the proposed local search mechanisms and dynamic response strategies significantly enhance its performance. Thus, the EGA is well-suited for addressing the studied problem. © 1997-2012 IEEE.

关键词： Job shop scheduling

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Multi-resolution Small Sample Face Recognition Based on Coupled Dictionary Learning 8

Multi-resolution Small Sample Face Recognition Based on Coup...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Fan, Xiaojin Zou, Weidong Wang, Changzhi School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Automation Beijing Institute of Technology Beijing China Fudan University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9781665450515

Multi-resolution small sample face recognition has always been a challenge in the field of face recognition. This paper proposes a multi-resolution small sample face recognition method based on coupled dictionary learning (MCDL). To expose the relationship between the representation coefficients corresponding to dictionary atoms and samples at different resolutions, MCDL introduces an analysis dictionary and jointly learns it with the synthesis dictionary. Besides, various coding coefficients are applied to each sample with varied resolutions throughout the multi-dictionary learning process so that the learnt dictionary is more realistic. Furthermore, we introduce the fractional norm into the model to remove the redundant information. Experimental results show that MCDL achieves good results and its recognition rate is higher than that of many algorithms. © 2022 IEEE.

关键词： Face recognition

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A PID Controller based on ESO and Tuning Method 11

A PID Controller based on ESO and Tuning Method

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Li, Xiangyang Hu, Yu Gao, Zhiqiang Ai, Wei Tian, Senping School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Cleveland State University Department of Electrical and Computer Science ClevelandOH44115 United States

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

PID (proportional-integral-derivative) is a widely used form of industrial control because of its simplicity and effectiveness. Its tuning, however, is usually tedious and experience dependent. In this paper, a more general form of PID, denoted as PID n-1, is proposed as a variation of ADRC (active disturbance rejection control). In addition to dealing with high order dynamics, the PID n-1 has only three parameters b0o and c to tune, all of them have explicit physical meanings. This helps streamlining the controller tuning process and making PID n-1 easy to use in industry. The experiments verify the effectiveness of the proposed PID n-1 solution and the ease of tuning. © 2022 IEEE.

关键词： Controllers

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Interactive AI with Retrieval-Augmented Generation for Next Generation Networking

arXiv

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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Liu, Yinqiu Niyato, Dusit Kang, Jiawen Sun, Sumei Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Institute for Infocomm Research Agency for Science Technology and Research Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) has been introduced, which can interactively understand and respond not only to human user input but also to dynamic system and network conditions. In this article, we explore an integration and enhancement of IAI in networking. We first comprehensively review recent developments and future perspectives of AI and then introduce the technology and components of IAI. We then explore the integration of IAI into the next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. Subsequently, we propose an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. We also design the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. We demonstrate the effectiveness of the framework through case studies. Finally, we discuss potential research directions for IAI-based networks. © 2024, CC BY.

关键词： Decision making

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Implementing Genetic Algorithms for Optimization in Neuro-Cognitive Rehabilitation Robotics 1

Implementing Genetic Algorithms for Optimization in Neuro-Co...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Abhang, L.B. Changala, Ravindra Ghosh, Anudeb Manage, Prabhakar S Rao, Vuda Sreenivasa Kiran Bala, B. Loni Under. SPPU University Pravara Rural Engineering College Automation and Robotics Dept Pune India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Siksha - Bhavana Department of Environmental Studies Visva-Bharati Santiniketan India Kle Technological University's Msscet Belagavi Campus Department of Electronics and Communication Karnataka Belagavi India Education Foundation Department of Computer Science and Engineering Koneru Lakshmaiah A.P Vaddeswaram522302 India K.Ramakrishnan College of Engineering The Department Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350372748

The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power. © 2024 IEEE.

关键词： Genetic algorithms

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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H8Control of Linear Uncertain Systems with Positivity Constraints 13

H8Control of Linear Uncertain Systems with Positivity Constr...

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13th Asian Control Conference, ASCC 2022

作者： Yang, Nachuan Li, Yuzhe Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9788993215236

This paper fills the literature gap by considering the worst-case H8 performance of continuous-time positive linear systems with structured uncertainty. The necessary and sufficient conditions on the positivity of structured uncertain systems are characterized using second-order cone programming (SOCP). More specifically, we reformulate the worst-case H8 control problem as a min-max semidefinite programming (SDP) based on the extended bounded real lemma for positive systems. Then we equivalently transform the min-max SDP into an SDP with linear matrix inequality (LMI) constraints, which turns out to be a convex programming. Finally, we provide several simulations to verify the effectiveness of the proposed results. © 2022 ACA.

关键词： H-infinity positive systems uncertain systems

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Multi-scale visual analysis of cycle characteristics in spatially-embedded graphs

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Visual Informatics 2023年第3期7卷 49-58页

作者： Farhan Rasheed Talha Bin Masood Tejas G.Murthy Vijay Natarajan Ingrid Hotz Department of Science and Technology LinkOing UniversityBredgatan 33NorrkOing60221Sweden Department of Civil Engineering Indian Institute of ScienceBangalore560012India Department of Computer Science and Automation Indian Institute of ScienceBangalore560012India

We present a visual analysis environment based on a multi-scale partitioning of a 2d domain intoregions bounded by cycles in weighted planar embedded *** work has been inspired by anapplication in granular materials research,where the question of scale plays a fundamental role inthe analysis of material *** propose an efficient algorithm to extract the hierarchical cyclestructure using persistent *** core of the algorithm is a filtration on a dual graph exploitingAlexander’s *** resulting partitioning is the basis for the derivation of statistical properties thatcan be explored in a visual *** demonstrate the proposed pipeline on a few syntheticand one real-world dataset.

关键词： Visual data analysis Planar graph Force network Granular materials Persistence homology Force loops Computational geometry

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