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Nonfragile Observer-Based Event-Triggered Fuzzy Tracking Control for Fast-Sampling Singularly Perturbed Systems with Dual-Layer Switching Mechanism and Cyber-Attacks

SSRN

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SSRN 2023年

作者： Guo, Fang Luo, Mengzhuo Cheng, Jun Katib, Iyad Shi, Kaibo College of Science Guilin University of Technology Guangxi Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Applied Statistics Guilin541004 China College of Mathematics and Statistics Guangxi Normal University Guangxi Guilin541006 China Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Information Science and Engineering Chengdu University Sichuan Chengdu610106 China

This paper investigates the problem of nonfragile observer-based tracking control for a class of fuzzy fast-sampling singularly perturbed systems (SPSs) with sensor saturation, which subject to event-triggered scheme and random cyber-attacks. The switching topology of the fast-sampling SPSs is dominated by a dual-layer switching mechanism, in this case, the Markov process in the underlying system is nonhomogeneous and its time-varying transition probabilities (TPs) are governed by a persistent dwell-time switching signal. First, in order to avoid asynchronous phenomenon of premise variable between the fuzzy fast-sampling SPSs and the fuzzy controller under network environments, the non-parallel distributed compensation (non-PDC) technique is applied to improve the design flexibility and reduce the conservativeness of the results. Furthermore, a novel fuzzy tracking control design scheme is proposed, in which integrating the fuzzy nonfragile observer and reference model signal into the synthetic design of the tracking controller can be expected to reduce the tracking error. Second, by constructing a well-designed time-delay and mode-dependent Lyapunov function, some sufficient conditions are derived such that the closed-loop system is mean-square exponential stable and with extended dissipative tracking performance. Finally, the effectiveness and applicability of the proposed control protocol are illustrated by two simulation examples. © 2023, The Authors. All rights reserved.

关键词： Closed loop systems

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EHands: Quantum Protocol for Polynomial Computation on Real-Valued Encoded States

arXiv

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arXiv 2025年

作者： Balewski, Jan Amankwah, Mercy G. Bethel, E. Wes Perciano, Talita Van Beeumen, Roel National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Computer Science Department San Francisco State University San FranciscoCA94132 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The novel constructive EHands protocol defines a universal set of quantum operations for multivariable polynomial transformations on quantum processors by introducing four basic subcircuits—multiplication, addition, negation, and parity flip—and by using expectation-value encoding (EVEN) to represent real numbers in quantum states. These elementary arithmetic operations can be systematically composed to compute degree-d polynomials, Pd(x), on a QPU. The resulting quantum circuit structure closely mirrors the stepwise evaluation of polynomials on a classical calculator, providing an intuitive and efficient approach to polynomial computation on quantum hardware. By enabling direct, predictable polynomial and nonlinear data transformations on a QPU, our method reduces dependence on classical post-processing in hybrid quantum-classical algorithms, enabling advancements in many quantum algorithms. The EHands quantum circuits are compact enough to deliver meaningful and accurate results on today’s noisy quantum processors. We present a detailed implementation of P4(x) and report experimental results for polynomial approximations of common functions, obtained using IBM’s Heron-class quantum processors and an ideal Qiskit simulator. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomial approximation

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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Artificial Intelligence-Driven Lung Cancer Detection Model Using Chest Radiography Images

SSRN

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SSRN 2023年

作者： Malebary, Sharaf J. Abulfaraj, Anas W. Dutta, Ashit Kumar Sait, Abdul Rahaman Wahab Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Archives and Communication King Faisal University Al Ahsa Hofuf31982 Saudi Arabia

In recent times, medical imaging techniques are widely applied for detecting lung cancer (LC) in the earlier stages. However, there is a lack of an effective LC detection model using chest radiography (CXR) images. The developers face challenges in extracting key features from the CXR images due to their complexities. There is a demand for a framework for handling the complexities of CXR images and generating insight to assist clinicians and radiologists to make a decision. Therefore, the researchers intend to build an LC detection framework using the CXR images. This study proposes an artificial intelligence-driven LC detection model using improved crow search optimization (ICSO) feature selection and the ResNest model. A Cauchy mutation is introduced in the ICSO for updating the position of the image features. The ResNest model’s hyper-parameters are tuned using the Harris Hawks optimization algorithm. To evaluate the proposed model’s performance, the researcher obtained normal and abnormal CXR images from seven healthcare centers across India. The dataset comprises 789 normal and 843 abnormal CXR images. In addition, the researchers employed baseline models for the comparative evaluation. The comparative analysis findings highlighted that the proposed framework obtained an average accuracy, F1-measure, MCC, and Kappa of 98.3, 97.9, 96.4, and 95.6. The area under the receiver operating characteristics and precision-recall curves of 0.99 and 0.98 outlines the significance of the proposed model in detecting LC from the CXR images. The model can assist clinicians and radiologists in making effective decisions and identifying the LC in the earlier stages. © 2023, The Authors. All rights reserved.

关键词： Diseases

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Optimizing Handwritten Numeral Recognition for English and Devanagari Using MNIST and CPAR Data

Optimizing Handwritten Numeral Recognition for English and D...

引用

Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Kamred Udham Singh Ankit Kumar Gaurav Kumar Teekam Singh Tanupriya Choudhury Ketan Kotecha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University Mathura UP India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

In this paper, a technique for the categorization of offline handwritten digits in the Devanagari script as well as the Roman script (English numbers) is presented. Finding out how to get the best possible recognition results while concurrently working with several scripts is the primary purpose of this study. The approach that has been described makes use of an easy profiling method for the extraction of features, together with an innovative implementation of Linear Discriminant Analysis (LDA), and a neural network architecture that has been adapted specifically for numeral classification. An exhaustive series of trials including 36,000 examples of handwritten numerals were carried out as part of the evaluation of the effectiveness of this approach. These samples were chosen at random from the CPAR datasets, with 22,000 being put to use in the construction of the training set and the remaining 14,000 being assigned to the role of the test set. In addition, the well-known MNIST database was used, which provided 60,000 samples for training purposes and 10,000 samples specifically for testing. It was determined via thorough testing that the LDA classifier produced variable results. These varying results are essential in emphasising the success of the method as well as possible areas for improvement for the method. According to the results, this strategy, which combines profile-based feature extraction with advanced classification algorithms, has the potential to lead to major breakthroughs in the area of handwritten number identification.

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Z-Inspection®: A Process to Assess Trustworthy AI

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2021年第2期2卷 83-97页

作者： Zicari, Roberto V. Brodersen, John Brusseau, James Dudder, Boris Eichhorn, Timo Ivanov, Todor Kararigas, Georgios Kringen, Pedro McCullough, Melissa Moslein, Florian Mushtaq, Naveed Roig, Gemma Sturtz, Norman Tolle, Karsten Tithi, Jesmin Jahan Van Halem, Irmhild Westerlund, Magnus Frankfurt Big Data Lab Goethe University Frankfurt Frankfurt Germany Department of Public Health Section of General Practice and Research Unit for General Practice Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Philosophy Department Pace University New YorkNY United States Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Iceland Institute of the Law and Regulation of Digitalization Philipps-University Marburg Marburg Germany Parallel Computing Labs Intel Labs Santa ClaraCA United States Department of Computer Science Arcada University of Applied Sciences Helsinki Finland Primary Health Care Research Unit Copenhagen1014 Denmark

The ethical and societal implications of artificial intelligence systems raise concerns. In this article, we outline a novel process based on applied ethics, namely, Z-Inspection®, to assess if an AI system is trustworthy. We use the definition of trustworthy AI given by the high-level European Commission's expert group on AI. Z-Inspection® is a general inspection process that can be applied to a variety of domains where AI systems are used, such as business, healthcare, and public sector, among many others. To the best of our knowledge, Z-Inspection® is the first process to assess trustworthy AI in practice. © 2020 IEEE.

关键词： Deep learning

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Automating gamification personalization: To the user and beyond

arXiv

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arXiv 2021年

作者： Rodrigues, Luiz Toda, Armando M. Oliveira, Wilk Palomino, Paula T. Vassileva, Julita Isotani, Seiji Laboratory of Applied Computing to Education and Advanced Social Technology Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil Lab Department of Computer Science University of Saskatchewan Saskatoon Canada

Personalized gamification explores knowledge about the users to tailor gamification designs to improve one-size-fits-all gamification. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several occasions to tailor. Consequently, tools for automating gamification personalization are needed. The problems that emerge are that which of those characteristics are relevant and how to do such tailoring are open questions, and that the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants’ opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through conditional decision trees to address the aforementioned tailoring process. Second, as a product from the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings i) present empirical evidence that LAT, geographic locations, and other user characteristics affect users’ preferences, ii) enable defining gamification designs tailored to user and contextual features simultaneously, and iii) provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system. © 2021, CC BY.

关键词： Recommender systems

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Mach–Zehnder-like interferometry with graphene nanoribbon networks

arXiv

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arXiv 2023年

作者： Sanz, Sofia Papior, Nick Giedke, Géza Sánchez-Portal, Daniel Brandbyge, Mads Frederiksen, Thomas Donostia-San SebastiánE-20018 Spain DTU Computing Center Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. LyngbyDK-2800 Denmark IKERBASQUE Basque Foundation for Science BilbaoE-48013 Spain CSIC-UPV/EHU Donostia-San SebastiánE-20018 Spain Department of Physics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark

We study theoretically electron interference in a Mach–Zehnder-like geometry formed by four zigzag graphene nanoribbons (ZGNRs) arranged in parallel pairs, one on top of the other, such that they form intersection angles of 60◦. Depending on the interribbon separation, each intersection can be tuned to act either as an electron beam splitter or as a mirror, enabling tuneable circuitry with interfering pathways. Based on the mean-field Hubbard model and Green’s function techniques, we evaluate the electron transport properties of such 8-terminal devices and identify pairs of terminals that are subject to self-interference. We further show that the scattering matrix formalism in the approximation of independent scattering at the four individual junctions provides accurate results as compared with the Green’s function description, allowing for a simple interpretation of the interference process between two dominant pathways. This enables us to characterize the device sensitivity to phase shifts from an external magnetic flux according to the Aharonov–Bohm effect as well as from small geometric variations in the two path lengths. The proposed devices could find applications as magnetic field sensors and as detectors of phase shifts induced by local scatterers on the different segments, such as adsorbates, impurities or defects. The setup could also be used to create and study quantum entanglement. Copyright © 2023, The Authors. All rights reserved.

关键词： Interferometry

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ADiff4TPP: Asynchronous Diffusion Models for Temporal Point Processes

arXiv

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arXiv 2025年

作者： Mukherjee, Amartya Deng, Ruizhi Zhao, He Mao, Yuzhen Sigal, Leonid Tung, Frederick RBC Borealis Canada RBC Borealis Canada Department of Applied Mathematics University of Waterloo Canada School of Computing Science Simon Fraser University Canada Department of Computer Science University of British Columbia Canada

This work introduces a novel approach to modeling temporal point processes using diffusion models with an asynchronous noise schedule. At each step of the diffusion process, the noise schedule injects noise of varying scales into different parts of the data. With a careful design of the noise schedules, earlier events are generated faster than later ones, thus providing stronger conditioning for forecasting the more distant future. We derive an objective to effectively train these models for a general family of noise schedules based on conditional flow matching. Our method models the joint distribution of the latent representations of events in a sequence and achieves state-of-the-art results in predicting both the next inter-event time and event type on benchmark datasets. Additionally, it flexibly accommodates varying lengths of observation and prediction windows in different forecasting settings by adjusting the starting and ending points of the generation process. Finally, our method shows superior performance in long-horizon prediction tasks, outperforming existing baseline methods. The implementation of our models can be found at Github repository. © 2025, CC BY-SA.

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Real-Time Data Collection of Wsn Based on Trajectory Optimization of Laser-Charged Uav

SSRN

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SSRN 2023年

作者： Luo, Chuanwen Hong, Yi Hou, Yunan Zhu, Yuqing Li, Deying Yu, Dongxiao Wu, Weili School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China Research Center of Applied Mathematics and Machine Intelligence Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los AngelesCA90032 United States School of Information Renmin University of China Beijing100872 China School of Computer Science and Technology Institute of Intelligent Computing Shandong University Qingdao266510 China Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, this paper study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is used to collect data from a given WSN and replenished energy by Laser Beam Directors (LBDs). The problem aims at optimizing the flight trajectory of UAV for ensuring the real-time performance of data, which is to design the detailed hovering and traveling plans of UAV for collecting data from sensors and charged by LBDs such that all sensory data of WSN are collected by UAV and transported to the base *** prove that the RDCL problem is NP-hard. To solve the problem, we first study two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Afterwards, we propose an approximation algorithm to solve the RDCL problem based on TODC and CTO problems. Finally, the proposed algorithm is verified by extensive simulations. © 2023, The Authors. All rights reserved.

关键词： Wireless sensor networks

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