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Robust Optimal Higher-order-observer-based Dynamic Sliding Mode Control for VTOL Unmanned Aerial Vehicles

International Journal of Automation and computing 2021年第5期18卷 802-813页

作者： Yashar Mousavi Amin Zarei Arash Mousavi Mohsen Biari Department of Applied Science School of ComputingEngineering and Built EnvironmentGlasgow Caledonian UniversityGlasgow G4 OBAUK Department of Electrical and Computer Engineering University of Sistan and BaluchestanZahedan ***Iran Department Electrical Engineering and Applied Sciences Paradise Research CenterJahrom ***Iran Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood ***Iran

This paper investigates the precise trajectory tracking of unmanned aerial vehicles(UAV) capable of vertical take-off and landing(VTOL) subjected to external disturbances. For this reason, a robust higher-order-observer-based dynamic sliding mode controller(HOB-DSMC) is developed and optimized using the fractional-order firefly algorithm(FOFA). In the proposed scheme, the sliding surface is defined as a function of output variables, and the higher-order observer is utilized to estimate the unmeasured variables,which effectively alleviate the undesirable effects of the chattering phenomenon. A neighboring point close to the sliding surface is considered, and as the tracking error approaches this point, the second control is activated to reduce the control input. The stability analysis of the closed-loop system is studied based on Lyapunov stability theorem. For a better study of the proposed scheme, various trajectory tracking tests are provided, where accurate tracking and strong robustness can be simultaneously ensured. Comparative simulation results validate the proposed control strategy′s effectiveness and its superiorities over conventional sliding mode controller(SMC) and integral SMC approaches.

关键词： Unmanned aerial vehicle dynamic sliding mode trajectory tracking fractional firefly algorithm vertical take-off and landing system

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Machine Learning and Network Traffic to Distinguish Between Malware and Benign Applications 1st

Machine Learning and Network Traffic to Distinguish Between ...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Abualigah, Laith Abualigah, Sayel Almahmoud, Mothanna Forestiero, Agostino Sachdeva, Gagan Hanandeh, Essam S. Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Department of Computer Information Systems Jordan University of Science and Technology Ar-Ramtha Jordan Performance Computing and Networking National Research Council of Italy Rende87036 Italy First Abu Dhabi Bank Abu Dhabi United Arab Emirates Department of Computer Information System Zarqa University Zarqa Jordan

ISBN: (纸本)9783031314681

Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail to detect new Malware. The more advanced approach relies on predicting malicious behavior with dynamic analysis of the process executed. This paper presents a new method for detecting malware using machine learning algorithms applied to data obtained from the Cuckoo sandbox. The Cuckoo sandbox isolates the file being analyzed, providing detailed dynamic analysis reports. The machine learning algorithms were compared and the most important features were identified. The results were obtained using six popular classifiers, including SVM, Random Forest, and LightGBM, and the XGBOOST algorithm had the highest accuracy, at an average of 97%. However, the research on machine learning-based malware analysis is limited in terms of computational complexity and detection accuracy. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Software reliability

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Dynamic properties and chaotic behaviors of pure-cubic complex Ginzburg–Landau equation with different nonlinearities

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Results in Physics 2024年 64卷

作者： Wang, Yining Yin, Zhixiang Lu, Lin Kai, Yue School of Mathematics Physics and Statistics Center of Intelligent Computing and Applied Statistics Shanghai University of Engineering Science Shanghai Xinhao Information Technology Co. Ltd.

This paper investigates the pure-cubic complex Ginzburg–Landau equation (PC-CGLE) with different nonlinearities such as Kerr law, power law and so on. We get the dynamic systems and show that solitons and periodic solutions exist through the complete discrimination system for the polynomial method (CDSPM). To verify these conclusions, we construct the traveling wave solution via the CDSPM, and some new solutions are also built. The soliton stability and modulation instability with two types of nonlinearities are discussed. Finally, by adding perturbed terms to the dynamic system, we obtain the largest Lyapunov exponents and the phase diagrams of the equation, which proves there are chaotic behaviors in PC-CGLE. The results such as Gaussian soliton solutions and chaotic behavior for PC-CGLE are initially discovered in the present paper. © 2024 The Authors

关键词： Chaotic behavior Complete discrimination system for polynomial method Qualitative analysis Traveling wave solution

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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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Boosting generation rate of squeezed single photon states by generalized photon subtraction

Boosting generation rate of squeezed single photon states by...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Tomoda, Hiroko Machinaga, Akihiro Takase, Kan Harada, Jun Kashiwazaki, Takahiro Umeki, Takeshi Miki, Shigehito Terai, Hirotaka Yabuno, Masahiro China, Fumihiro Okuno, Daichi Takeda, Shuntaro Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokko-dai Kobe Nada657-0013 Japan

We theoretically and experimentally proved that the generation rate of squeezed single photon states can be increased by generalized photon subtraction method. Our work will encourage universal and fault-tolerant cont... 详细信息

ISBN: (纸本)9781957171395

关键词： Squeezed light

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Automating Weed Detection Through Hyper Spectral Image Analysis

Automating Weed Detection Through Hyper Spectral Image Analy...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Shambhu Bharadwaj Prabhu A Vipin Solanki Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Applied Science Sanskriti University Mathura Uttar Pradesh India

Weed detection is an essential assignment in agricultural settings. Negative environmental results, crop yield loss, and mechanical weeding hard work prices related to weed management have necessitated the improvement of automation solutions to discover and treat weeds. Hyperspectral imaging (HSI) is a promising method that can produce abundant statistics about the spectral residences of flowers. This generation has been used in weed detection packages to classify weeds from crops, and these days deep mastering has been used to provide high accuracy prices in this discipline. In this abstract, we explore the utility of HSI for weed detection. We define current demanding situations that require further research earlier than automatic weed detection structures using HSI grow to be widely available. In particular, the presently available algorithms lack robustness and scalability, and further improvements in gadget-gaining knowledge of algorithms and techniques are wished to conquer those constraints, in addition to advancing the computational capabilities of these structures. Moreover, we discuss the potential of HSI as a weed detection answer in various contexts, including agroforestry and precision farming. In conclusion, we advise that the software of HSI for automatic weed detection has the massive capability to reduce labor fees related to weed manipulation, improve farming performance, and in the end, boom crop yields. Weed detection is a first-rate challenge inside the agricultural enterprise, as guide weed control is costly, time-consuming, and the correct identity of weeds is rigid. Hyper Spectral photo evaluation (HSIA) offers an alternative to guide weed detection, considering the rapid and effective mapping of weed-infested land without the need for guide labor. HSIA may be used to routinely detect the spectral signature of weed species, allowing for correct identity and brief remedy. This method uses hyperspectral scanners to gather spectral records, whic

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applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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Intelligent Automation & Soft computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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Generation of Over-8-dB Squeezed Light by a Broadband Waveguide Optical Parametric Amplifier Toward Fault-Tolerant Quantum Computers

Generation of Over-8-dB Squeezed Light by a Broadband Wavegu...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Takahiro Kashiwazaki Taichi Yamashima Koji Enbutsu Takushi Kazama Asuka Inoue Kosuke Fukui Mamoru Endo Takeshi Umeki Akira Furusawa NTT Device Technology Labs NTT Corporation Atsugi Kanagawa Japan Department of Applied Physics School of Engineering The University of Tokyo Tokyo Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing Wako Saitama Japan

ISBN: (纸本)9798350345995

Measurement-based optical quantum computers (MBOQCs) using continuous-variables (CVs) of traveling wave of light have a potential for ultra-fast fault-tolerant quantum information processing [1]. In CV MBOQCs, broadband high-level squeezed light is the most essential quantum resource. Broad bandwidth not only enables high-clock frequency computation, but also contributes to the realization of large-scale quantum computers using time-domain-multiplexed quantum entanglement. This is because squeezed light with a broader bandwidth enables us to define many more quantum wave packets in a narrow time range. On the other hand, high-level squeezing contributes to high-fidelity quantum operation and suppression of quantum errors. For example, over-8.3-dB squeezing enables us to achieve a fault-tolerant CV MBOQC with Gottesman-Kitaev-Preskill (GKP) coding [2].

关键词： Information processing Optical amplifiers Optical computing Photonic entanglement Quantum information Scanning electron microscopy

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Development of Automated Techniques for Object-Oriented Image Analysis in Hyper Spectral Images

Development of Automated Techniques for Object-Oriented Imag...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Monika Abrol Rajendra P. Pandey Rahul Pawar Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes; however, using a combination of those strategies has been visible to enhance the classification performance.

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