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arXiv 2022年

作者： Szántó, Mátyás Bogár, György Richárd Vajta, László Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 2 Magyar tudosok Blvd. BudapestH-1117 Hungary

In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based Deep Learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and low-latency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control. © 2022, CC BY.

关键词： Autonomous vehicles

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Comparison of game theoretical strategy and reinforcement learning in traffic light control

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Periodica Polytechnica Transportation engineering 2020年第4期48卷 313-319页

作者： Guo, Jian Harmati, István Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics H-1521 P. O. B. 91 Budapest Hungary

Many traffic models and control methods have already been utilized in the public transportation system due to the increasing traffic congestion. Thus, an intelligent traffic model is formalized and presented to control multiple traffic light simultaneously and efficiently according to the distribution of vehicles from each incoming link (i.e. sections) in this paper. Compared with constant strategy, two methods are proposed for traffic light control, i.e., game theoretical strategy and reinforcement learning methods. Game theoretical strategy is generated in a game theoretical framework where incoming links are regarded as players and the combination of the status of traffic lights can be regarded as decisions made by these players. The cost function is evaluated and the strategy is produced with Nash equilibrium for passing maximum vehicles in an intersection. The other one is Single-Agent Reinforcement Learning (SARL), specifically with the Q-learning algorithm in this case, which is usually used in such a dynamic environment to control traffic flow so the traffic problem could be improved. Generally, the intersection is regarded as the centralized agent and controlling signal status is considered as the actions of the agent. The performance of these two methods is compared after simulated and implemented in a junction. © 2020 Budapest University of Technology and Economics. All rights reserved.

关键词： Game theory

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Analysis of a Data Set to Determine the Dependence of Airline Passenger Satisfaction 7th

Analysis of a Data Set to Determine the Dependence of Airlin...

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Tynchenko, V.S. Borodulin Kleshko, I.I. Nelyub, V.A. Rukosueva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia

ISBN: (纸本)9783031535512

This paper presents an analysis of a data set to determine the factors influencing airline passenger satisfaction. The study examines various criteria such as gender, customer type, age, travel type, class, and range to assess their impact on passenger satisfaction. The dataset consists of 25 columns, including attributes like Wi-Fi availability, convenience of online booking, seat comfort, in-flight entertainment, baggage handling, and overall satisfaction. The sample is relatively balanced, with equal representation of men and women, predominantly repeat customers, and a majority flying for business purposes. Key findings include a strong correlation between departure and arrival delays, higher satisfaction among passengers in business class, and positive ratings for Wi-Fi service correlating with overall satisfaction. Correlation analysis reveals interdependencies between different attributes, such as the influence of cleanliness on seat comfort and food and beverage ratings. In addition, a neural network forecasting model is used to estimate the average ratings of passengers, although with low accuracy, which was later excluded. Finally, a decision tree algorithm is utilized to identify the most significant attributes affecting passenger satisfaction words. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Baggage handling

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LMI-Based Non-Iterative Design Strategies for Guaranteed Cost Static Output Feedback control for Linear Systems

LMI-Based Non-Iterative Design Strategies for Guaranteed Cos...

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International Conference on Electrical, control and Instrumentation engineering (ICECIE)

作者： Hidetoshi Oya Takuya Nakagawa Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

This paper proposes a non-iterative LMI-based design strategy for guaranteed cost static output feedback controllers for linear systems. The proposed design approach is based on the well-known necessary and sufficient condition for the existence of the static output feedback stabilizing control for linear systems. In this paper, for linear systems we show the non-iterative LMI-based design procedure for static output feedback controllers which minimize the upper bound of a given quadratic cost function, and the effectiveness of the proposed static output feedback controller with guaranteed cost is presented through a simple numerical example.

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Correlation Analysis and Predictive Factors for Building a Mathematical Model 1

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Nelyub, V.A. Tynchenko, V.S. Gantimurov, A.P. Degtyareva, K.V. Kukartseva, O.I. Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

The study, published in the journal Nature Medicine, looked at data on 1,000 people from China who were tracked over an average period of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution. The study analyzed data on patients with lung cancer, including their age, gender, exposure to air pollution, alcohol consumption, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoking, chest pain, cough, hemoptysis, fatigue, weight loss, shortness of breath, wheezing, difficulty swallowing, nail thickening and snoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Air pollution

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Multistability Analysis and Adaptive Feedback control on a New Financial Risk System

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International Journal of Applied and Computational Mathematics 2023年第5期9卷 1-16页

作者： Johansyah, Muhamad Deni Sambas, Aceng Vaidyanathan, Sundarapandian Benkouider, Khaled Sulaiman, Ibrahim Mohammed Department of Mathematics Universitas Padjadjaran Jatinangor Sumedang 45363 Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu Besut 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Center for Control System Vel Tech University Avaddi Chennai 60062 India Department of Non-Destructive Automatic Testing Laboratory Jijel University Rue Rouikha Mokhta Jijel 1800 Algeria School of Quantitative Science University Utara Malaysia Sintok Kedah Bukit Kayu Hitam 06010 Malaysia

This study presents the findings of a new financial risk system with one absolute function and two quadratic nonlinearities. The rich dynamic behaviors of the new financial risk system are discussed analytically and numerically through stability analysis, phase portrait, bifurcation diagram, Lyapunov exponent, multistability and coexisting attractors. In addition, a new financial risk system with two quadratic nonlinearities and one absolute function shows multistability and coexistence of chaotic attractors. Using adaptive feedback control, we established a new control law for fully synchronising the proposed chaotic financial systems with itself. To conclude, some numerical simulation was presented using MATLAB to illustrate the efficiency of the designed method. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Backstepping control Chaos Financial chaotic system Synchronisation

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DeepHealth: Geospatial and ML-Based Approach to Identify Health Disparities and Determinants for Improving Pandemic Health Care

DeepHealth: Geospatial and ML-Based Approach to Identify Hea...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Jinwei Liu Rui Gong Long Cheng Richard A. Alo Department of Computer and Information Sciences Florida A&M University Tallahassee FL USA Department of Informatics and Mathematics Mercer University Macon GA USA School of Control and Computer Engineering North China Electric Power University Beijing China

The COVID-19 pandemic has exacerbated existing health disparities, and its impact has fallen disproportionately on disadvantaged and vulnerable communities. Racial and ethnic minorities such as Black Americans who are at a particular disadvantage are more likely to be the potential target of COVID-19 infection and are dying at alarmingly high rates. Despite a promising solution of the COVID-19 vaccination offers hope, equitable access to COVID-19 vaccines remains a challenge in the US, which has compounded the existing disparities in cases, hospitalizations, and deaths among racial and ethnic minority groups. The deep and pervasive history of medical racism in the US has led to the vaccine hesitancy in racial and ethnic minorities, and thereby caused the disparities. Although some studies examine determinants of health disparities (e.g., social health determinants), there is a shortage of studies examining the social, structural and constructural health determinants, either alone or in tandem with other determinants. Little research paid attention to leveraging geographic information to trace the social, structural and constructural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepHealth, a geospatial and ML-based (machine learning based) approach to identify diverse determinants (including the social, structural, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities based on multiple COVID-19 datasets and examine the social, structural, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in incidence and mortality rates due to COVID-19 pandemic. Extensive experimental results show the effectiveness of our approach. This research provides new strategies for health disparity identification and deter

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Comparative Analysis of YOLO-based Models for Vocal Cord Segmentation in Laryngoscopic Images

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Procedia Computer Science 2024年 246卷 4998-5006页

作者： Jakub Steinbach Jan Vrba Tomáš Jirsa Matouš Cejnek Zuzana Urbániová Department of Mathematics Informatics and Chemistry Faculty of Chemical Engineering University of Chemistry and Technology in Prague Technická 5 166 28 Praha 6 - Dejvice Prague Czech Republic Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University in Prague Technická 1902/4 160 00 Praha 6 - Dejvice Prague Czech Republic Department of Otorhinolaryngology 3rd Faculty of Medicine and Faculty Hospital Královské Vinohrady Charles University Ruská 87 100 00 Praha 10 Prague Czech Republic

This study presents a comparative analysis of segmentation models based on the YOLO (You Only Look Once) architecture for the task of vocal cord detection in laryngoscopic images. The yolov5, yolov8, and yolov9 architectures were evaluated using images obtained from laryngoscopic videos recorded during standard examinations at ORL clinics. The primary objective was to assess the efficiency of different model sizes and architectures in accurately identifying the position of vocal cords within the images. Our findings reveal that all evaluated architectures demonstrate proficiency in vocal cord detection, with comparable results across the models. However, there is a discernible difference in mean Average Precision (mAP) metrics (at IoU thresholds ranging from 0.5 to 0.95). Notably, yolov8 exhibits the highest mAP scores, followed by yolov5 and yolov9, indicating superior performance in identifying vocal cord regions. This comparative analysis provides valuable insights into the effectiveness of YOLO-based segmentation models for vocal cord detection, highlighting the importance of model size and architecture selection in medical image analysis applications.

关键词： Voice pathology detection image segmentation YOLO deep learning machine learning vocal folds vocal cords laryngoscopic imaging ORL otorhinolaryngology

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Raman microscopic measurement of materials used on banknotes

Raman microscopic measurement of materials used on banknotes

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Circuits, Systems, Communications and Computers (CSCC), International Conference on

作者： Hana Tomášková Vladimír Vašek Department of electronic and measurements Faculty of Applied informatics Tomas Bata University in Zlin Zlín Czech Republic Department of automation and control engineering Faculty of Applied informatics Tomas Bata University in Zlin Zlín Czech Republic

ISBN: (纸本)9781665481878

The successful verification of the authenticity of banknotes, or the detection of counterfeits, is based on the combination of an effective measuring system and computer technology. In this paper, a modern diagnostic method Raman microscopy together with an exploratory tool for processing and interpreting multi-dimensional datasets, and cluster analysis, are used to study structural differences between paper and orange ink used on genuine banknotes. Six currencies of European countries were selected for this study: Croatian kuna, Czech crown, euro, Great British Pound, Polish zloty, and Swiss franc. Raman microscopy offers non-destructive, rapid analysis without any sample preparation and pre-processing, with the potential to recognize structural modification of substances, together with the advantages of optical microscopy. Cotton fibres, $\text{TiO}_{2}$ in two different modifications, and other additives are successfully identified within the spectra. The patterns found in mathematically processed Raman data, especially between the paper, indicate both, the similarities and differences across the various banknotes, even of the same currency. The results serve as a base of the spectral database for authenticity control of banknotes and classification of the materials used.

关键词： Optical fibers Optical microscopy Databases Statistical analysis Microscopy Process control Ink

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