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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET Control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET Control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Closed loop systems

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TLEF: Two-layer Evolutionary Framework for t-Closeness Anonymization 24th

TLEF: Two-Layer Evolutionary Framework for t-Closeness Anony...

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： You, Mingshan Ge, Yong-Feng Wang, Kate Wang, Hua Cao, Jinli Kambourakis, Georgios Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne Australia School of Health and Biomedical Sciences RMIT University Melbourne Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Information and Communication Systems Engineering University of the Aegean Karlovasi Greece

ISBN: (纸本)9789819972531

Data anonymization is a fundamental and practical privacy-preserving data publication (PPDP) method, while searching for the optimal anonymization scheme using traditional methods has been proven to be NP-hard. Some recent studies have introduced genetic algorithms (GA) into data anonymization to address this issue, revealing potential solutions. However, the discussions are restricted to a few privacy protection models and evolutionary algorithms (EAs). This paper extends this field by introducing differential evolution (DE) for the first time to optimize data anonymization schemes under the constraints of the t-closeness privacy model. To further enhance the algorithm’s performance, this paper designs a two-layer evolutionary framework (TLEF) that effectively explores optimal solutions by leveraging the unique properties of both GA and DE in a balanced manner. Experimental evaluations conducted on 16 test datasets highlight the advantages of DE in addressing optimal t-closeness anonymization and validate the effectiveness of the TLEF. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Local Enumeration: The Not-All-Equal Case 42

Local Enumeration: The Not-All-Equal Case

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42nd International Symposium on Theoretical Aspects of computer science, STACS 2025

作者： Gurumukhani, Mohit Paturi, Ramamohan Saks, Michael Talebanfard, Navid Cornell University IthacaNY United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Department of Mathematics Rutgers University PiscatawayNJ United States University of Sheffield United Kingdom

ISBN: (纸本)9783959773652

Gurumukhani et al. (CCC’24) proposed the local enumeration problem Enum(k, t) as an approach to break the Super Strong Exponential Time Hypothesis (SSETH): for a natural number k and a parameter t, given an n-variate k-CNF with no satisfying assignment of Hamming weight less than t(n), enumerate all satisfying assignments of Hamming weight exactly t(n). Furthermore, they gave a randomized algorithm for Enum(k, t) and employed new ideas to analyze the first non-trivial case, namely k = 3. In particular, they solved Enum(3, n2 ) in expected 1.598n time. A simple construction shows a lower bound of 6 n4 ≈ 1.565n. In this paper, we show that to break SSETH, it is sufficient to consider a simpler local enumeration problem NAE-Enum(k, t): for a natural number k and a parameter t, given an n-variate k-CNF with no satisfying assignment of Hamming weight less than t(n), enumerate all Not-All-Equal (NAE) solutions of Hamming weight exactly t(n), i.e., those that satisfy and falsify some literal in every clause. We refine the algorithm of Gurumukhani et al. and show that it optimally solves NAE-Enum(3, n2 ), namely, in expected time poly(n) · 6 n4 © Mohit Gurumukhani, Ramamohan Paturi, Michael Saks, and Navid Talebanfard.

关键词： Hamming distance

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6D Pose Estimation and Correction for Fully Occluded 3D Model X-Ray Source Using Deeplabv3 Utilizing Mobilenetv2 Backbone and Dense Fusion Algorithm

6D Pose Estimation and Correction for Fully Occluded 3D Mode...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Rogelio, Jayson P. Dadios, Elmer Jose P. Bandala, Argel Naguib, Raouf Vicerra, Ryan Rhay Sybingco, Edwin Lim, laurence Gan Metals Industry Research and Development Center De La Salle University Department of Science and Technology Philippines De La Salle University College of Manufacturing and Management Manila Philippines De La Salle University College of Electronics and Communications Engineering Manila Philippines Liverpool Hope University Mathematics Computer Science and Engineering Liverpool United Kingdom De La Salle University College of Mechanical Engineering Manila Philippines

ISBN: (纸本)9798350364910

Recent advancements in computer vision, notably in 6D pose estimation with methods like Deeplabv3 and Dense Fusion, show promising results. However, a substantial research gap exists in addressing the limitations of these methods in occluded scenarios, which in need further research to enhance their effectiveness and adaptability in situations involving hidden or partially obscured objects. This study introduces a novel approach employing 6D object pose estimation and pose correction by integrating DeeplabV3 and modified dense fusion. The study exhibits enhanced accuracy in pose estimation and correction for occluded 3D object model x-ray source. Evaluation metrics, including intersection-over-union and mean average, demonstrate high accuracy percentages for detecting the body (98.91%), handle (96.57%), and aperture (89.54%). The mean IoU for each part of the 3D model portable X-ray source ranges from 65.69% to 76.42%. Pose estimation accuracy, assessed through the Average Distance Difference (ADD) metric, indicates superior performance for static pose estimation closer to the camera. Dynamic pose estimation exhibits higher average ADD metrics in scenes with total occlusion. The robustness metric reveals lower lost tracking counts in scenes without occlusion, emphasizing the algorithm's challenges in fully occluded scenarios. © 2024 IEEE.

关键词： X ray apparatus

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Constructing Multi-constrained Cognitive Diagnostic Tests: An Improved Ant Colony Optimization Algorithm 12th

Constructing Multi-constrained Cognitive Diagnostic Tests: A...

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12th International Conference on Health Information science, HIS 2023

作者： Cao, Xi Ge, Yong-Feng Lin, Ying Department of Computer Science and Information Technology La Trobe University Melbourne3086 Australia Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne3011 Australia Department of Psychology Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9789819971077

Most application scenarios of cognitive diagnostic tests (CDTs) are confronted with multiple constraints. Some existing well-developed algorithms for constructing CDTs transforms the test-construction problem into an optimization problem to improve the statistical performance of tests. However, tackling optimization problems with multiple constraints, particularly nonlinear ones, through previous optimization methods may not yield satisfactory outcomes. To address the multi-constrained CDT construction with greater efficiency, this paper introduces a method based on ant colony optimization (ACO-mTC). The method uses pheromone and heuristic information to guide the test-construction optimization. Pheromone signifies past construction experiences, while heuristic information integrates item discrimination indices and test constraint information. To facilitate the search to approximate feasible optima, each construction iteration is evaluated by item discrimination indices and test constraint information collectively. The effectiveness of the proposed method is demonstrated through a series of simulation experiments under various conditions. Comparative analysis with the random algorithm illustrates that ACO-mTC is more proficient in producing feasible tests with increased speed and enhanced statistical performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Ant colony optimization

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Novel Human-Posture Recognition System Based on Advanced Graph Convolutional Network Using Skeletal Data

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 224-236页

作者： Liu, Guannan Xie, Rende Fang, Shih-Hau Wu, Hsiao-Chun Yan, Kun San Jose State University Department of Applied Data Science San JoseCA95112-3613 United States National Taiwan Normal University Department of Electrical Engineering Taipei106 Taiwan Yuan Ze University Department of Electrical Engineering Taoyuan320315 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology School of Information and Communication Guilin541004 China

Automatic human-posture or human-activity recognition is a very important research problem nowadays. In this work, we propose a novel human-posture recognition approach using the 3-D skeletal data acquired by the Kinect V2 sensor. The acquired skeletal data are first segmented using our recently proposed automatic-segmentation technique and each segment can be labeled with a particular kind of human-posture. We propose four different types of node feature matrices extracted from the segmented skeletal data, which can serve as the input features to the advanced graph convolutional network for multiclassification. The realworld experimental results demonstrate that our proposed novel human-posture recognition system can reach a very high average classification-accuracy of 91.56%. In addition, the ablation study of the effect of skeletal-graph variations on the recognition performance is also presented. The average classification-accuracy further reaches up to 92.33% when four confusing joint-nodes are removed from the skeletal graph. Our proposed novel human-posture recognition approach can be very useful for practical applications, such as human-computer interface, intelligent healthcare, robotics, etc. © 2024 IEEE.

关键词： Sensor nodes

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Quantum Face Recognition With Multigate Quantum Convolutional Neural Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6330-6341页

作者： Zhu, Yijie Bouridane, Ahmed Celebi, M. Emre Konar, Debanjan Angelov, Plamen Ni, Qiang Jiang, Richard LIRA Center Lancaster University LancasterLA1 4YW United Kingdom University of Sharjah Sharjah27272 United Arab Emirates The Department of Computer Science University of Central Arkansas ConwayAR72035 United States School of Industrial Engineering Purdue University West LafayetteIN47906 United States

In the last decade, quantum computing has showcased its unique mechanism across diverse fields, highlighting significant potential for data-driven applications requiring substantial computational resources. Within this landscape, quantum machine learning emerges as a promising frontier, poised to harness the unique advantages of quantum computing for machine learning tasks. Nonetheless, the current generation of quantum hardware, typified by noisy intermediate-scale quantum (NISQ) devices, grapples with severe resource constraints, particularly in terms of qubit availability. While quantum computing offers tantalizing capabilities such as superposition and entanglement, which can be strategically leveraged to optimize the performance of quantum neural networks, the challenge remains in mitigating the resource limitations while upholding high recognition accuracy. To address this imperative, we introduce a pioneering face recognition method christened the multigate quantum convolutional neural network (MG-QCNN). This innovation is engineered to surmount the resource bottleneck endemic to NISQ devices while preserving exceptional recognition accuracy. Our empirical investigations conducted on benchmark datasets, including the Yale face dataset and the ORL face database, illuminate the remarkable potential of this approach. Specifically, our proposed variational quantum circuit architecture consistently achieves an impressive average accuracy of 96%, which is better than the 95% of the classic CNN. Our model underscores the efficacy of quantum convolution operations in the extraction of feature maps, exhibiting a transformative stride toward unlocking the full potential of quantum-enhanced face recognition, and compared with other quantum models, our method has more advantages in accuracy and efficiency. harnessed the unique capabilities of quantum computing to overcome resource limitations and achieve an astounding 96% average accuracy on face recognition tasks. This

关键词： Face recognition

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Enhancing Classification Accuracy and Speed with ML-DSP: A Game-Changer in Genomic Sequence Analysis 3

Enhancing Classification Accuracy and Speed with ML-DSP: A G...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Rakesh, M.D. Madhura, R. Shashidhar, R. Krishnappa, Krutthika Hirebasur JSS Science and Trechnology University Department of Electronics and Communication Engineering Mysuru India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Southern University and AM College Department of Computer Science Baton RougeLA United States

ISBN: (纸本)9798350340358

Even with a wide range of software tools at our disposal for comparing, analyzing, identifying, and categorizing genomic sequences, the complexity of the task remains, the task of classifying these sequences remains challenging. This difficulty is attributed to the vast datasets and inherent issues associated with classification. To overcome the limitations of existing alignment-centric and alignment-independent methods, a novel approach and software tool are required. In this research, we introduce ML-DSP, an innovative fusion of guided Machine Learning and processing of digital signals. This hybrid technique is developed to facilitate rapid, precise, and scalable classification of genetics across various taxonomic levels. The evaluation of ML-DSP involves the classification of 7396 complete mitochondrial genetics, resulting in an average accuracy of over 97% from kingdom to genus levels. Through a thorough comparison with classification software tools, using both small standard datasets and a larger dataset containing 4322 vertebrate mtDNA genetics, we demonstrate that machine learning digital signal processing beats the alignment-centric software MEGA7 (employing MUSCLE or CLUSTALW for alignment) into processing speed, during achieving kindred or even superior sorting accuracy across the data inventory. In contrast to the alignment-independent software FFP, Machine learning processing exhibits enhanced sorting accuracy and faster aggregate performance. Furthermore, starting experiments underscore the potential of ML-DSP in diverse datasets, achieving perfect accuracy in categorizing 4271 entire dengue virus genetic information into variants and 95.5% accuracy in classifying 4,710 bacterial genetic information into phyla. © 2023 IEEE.

关键词： Genes

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Comments on "Variations of Joint Integrated Data Association with Radar and Target-Provided Measurements"

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Journal of Advances in Information Fusion 2023年第2期18卷 93-101页

作者： Gaglione, Domenic Braca, Paolo Soldi, Giovanni Meyer, Florian Hem, Audun G. Brekke, Edmund F. Hlawatsch, Franz La Spezia19126 Italy Department of Electrical and Computer Engineering the Scripps Institution of Oceanography University of California San Diego San DiegoCA92093 United States Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim7034 Norway Institute of Telecommunications TU Wien Vienna1040 Austria

Recently, a method for including target-provided measurements within a joint integrated probabilistic data association (JIPDA) filter was presented and compared with a belief propagation (BP)based multitarget tracking method. While the JIPDA-based approach uses multiple kinematic models within an interacting multiple model framework, the BP-based approach uses only a single kinematic model. Here, we present and analyze the results of similar experiments conducted on both simulated and real data. Our results show that the JIPDA-based method tends to outperform the BP-based method when the targets are well-separated and perform sharp maneuvers, whereas the BP-based method outperforms the JIPDA-based method when the targets are closely spaced. © 2023 International Society of Information Fusion. All rights reserved.

关键词： Belief propagation

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On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

arXiv

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arXiv 2024年

作者： Zhu, Shengkun Zeng, Jinshan Wang, Sheng Sun, Yuan Li, Xiaodong Yao, Yuan Peng, Zhiyong The School of Computer Science Wuhan University China The School of Computer and Information Engineering Jiangxi Normal University China La Trobe Business School La Trobe University Australia The School of Computing Technologies RMIT University Australia Hong Kong University of Science and Technology Hong Kong

Statistical heterogeneity is a root cause of tension among accuracy, fairness, and robustness of federated learning (FL), and is key in paving a path forward. Personalized federated learning (PFL) is an approach that aims to reduce the impact of statistical heterogeneity by developing personalized models for individual users, while also inherently providing benefits in terms of fairness and robustness. However, existing PFL frameworks focus on improving the performance of personalized models while neglecting the global model. This results in PFL suffering from lower solution accuracy when clients have different kinds of heterogeneous data. Moreover, these frameworks typically achieve sublinear convergence rates and rely on strong assumptions. In this paper, we employ the Moreau envelope as a regularized loss function and propose FlaME, an optimization framework by utilizing the alternating direction method of multipliers (ADMM) to train personalized and global models. Due to the gradient-free nature of ADMM, FlaME alleviates the need for tuning the learning rate during training of the global model. We demonstrate that FlaME can generalize to the existing PFL and FL frameworks. Moreover, we propose a model selection strategy to improve performance in situations where clients have different types of heterogeneous data. Our theoretical analysis establishes the global convergence and two kinds of convergence rates for FlaME under mild assumptions. Specifically, under the assumption of gradient Lipschitz continuity, we obtain a sublinear convergence rate. Further assuming the loss function is lower semicontinuous, coercive, and either real analytic or semialgebraic, we can obtain constant, linear, and sublinear convergence rates under different conditions. We also theoretically demonstrate that FlaME is more robust and fair than the state-of-the-art methods on a class of linear problems. We thoroughly conduct experiments by utilizing six schemes to partition non-i.i.d. d

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