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Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 441-454页

作者： Yongliang Yang Zhijie Liu Qing Li Donald C.Wunsch the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China the Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear *** asymmetric output constraints and input saturation are *** asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear *** avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each *** deal with the actuator saturation,an additional auxiliary dynamical system is *** is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately *** simulation examples are conducted to verify the presented adaptive fuzzy controller design.

关键词： Asymmetric barrier Lyapunov function input saturation Nussbaum function time-varying prescribed performance

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Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport

arXiv

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arXiv 2025年

作者： Liu, Yonghao Giunchiglia, Fausto Li, Ximing Huang, Lan Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education China

Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph few-shot learning models have exhibited superior performance across diverse applications. Despite their successes, several limitations still exist. First, existing models in the meta-training phase predominantly focus on instance-level features within tasks, neglecting crucial set-level features essential for distinguishing between different categories. Second, these models often utilize query sets directly on classifiers trained with support sets containing only a few labeled examples, overlooking potential distribution shifts between these sets and leading to suboptimal performance. Finally, previous models typically require necessitate abundant labeled data from base classes to extract transferable knowledge, which is typically infeasible in real-world scenarios. To address these issues, we propose a novel model named STAR, which leverages Set funcTions and optimAl tRansport for enhancing unsupervised graph few-shot learning. Specifically, STAR utilizes expressive set functions to obtain set-level features in an unsupervised manner and employs optimal transport principles to align the distributions of support and query sets, thereby mitigating distribution shift effects. Theoretical analysis demonstrates that STAR can capture more task-relevant information and enhance generalization capabilities. Empirically, extensive experiments across multiple datasets validate the effectiveness of STAR. Our code can be found here. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on knowledge and Data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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Multi-Layered Ontology Models for Dynamic knowledge Representation in Social Network Analysis 2

Multi-Layered Ontology Models for Dynamic Knowledge Represen...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Reddy, Y Manohar Raj, Vijilius Helena Bansal, Saloni Thethi, H Pal Kalra, Ravi Khalid, Mohammed Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

In the rapidly evolving landscape of social networks, understanding the multifaceted nature of human interactions and the dissemination of information is pivotal. This paper introduces a novel approach to dynamic knowledge representation through the development of Multi-Layered Ontology Models tailored for Social Network Analysis (SNA). The core of this research lies in architecting a robust, scalable, and semantically rich framework that leverages the principles of data mining and web technologies to dissect and interpret the complex structure of social networks. The ontology layers are intricately designed to represent various dimensions of social interactions, including temporal dynamics, user behavior, and network evolution. Each layer is interlinked yet distinct, providing a granular view of the social fabric and enabling precise inference and prediction of patterns. The methodology adopts an innovative blend of semantic web technologies for ontology construction, coupled with advanced data mining techniques to efficiently process and analyze large volumes of social data. The result is a dynamic model that adapts to the ever-changing social context, offering valuable insights into trends, community structures, and influence dissemination. The implications of this research are vast, impacting areas ranging from targeted marketing to public opinion analysis. This paper sets a new precedent for interdisciplinary research in SNA, opening avenues for further exploration and refinement. © 2024 IEEE.

关键词： Latent semantic analysis

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Advanced Simulation and Optimization Strategies in Thermo-Fluid Dynamics: A Deep Learning Approach to Enhancing Heat Transfer in Evaporative Cooling Systems

Advanced Simulation and Optimization Strategies in Thermo-Fl...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Rajalakshmi, B. Alekhya, V. Lakhanpal, Sorabh Khan, Irfan Munshid, Maha Chasib Maranan, Ramya New Horizon College of Engineering Department of Computer Science Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350373783

This article describes a novel technique to study and improve evaporative cooling systems to move heat better. The system uses the GA, CNN, LSTM, and FVM algorithms. Each thermofluid dynamics study approach provides something unique. The CNN approach independently acquires spatial information from raw modeling data to study fluid movement. Temporal linkages in sequential data reveal heat's movement over space and time via LSTM. GAN adds continuing antagonistic processes to the dataset, strengthening the model. GA adjusts GAN settings to improve data, whereas FVM simulates realistic fluid dynamics to improve heat transport. Ablation studies demonstrate each algorithm's distinct contributions and importance. Performance comparisons reveal that the recommended framework outperforms others in accuracy, processing time, memory utilization, convergence rate, resilience, and adaptability. Bar charts, line charts, pie charts, and histograms show how the recommended method compares to others. The framework is precise, efficient, and versatile, making it ideal for improving thermo-fluid dynamics models in evaporative cooling systems. Researchers and engineers seeking to improve heat transfer in evaporative cooling systems can utilize it. The proposed architecture is full and adaptable to thermal-fluid utilization issues. © 2024 IEEE.

关键词： Evaporative cooling systems

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Towards High-Accuracy 3D Creation of Aviation Tools via Analysis-by-Synthesis

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IEEE Access 2025年

作者： Hassan, Gundu Mohamed Kim, Yoo Sung Jo, Geun Sik Inha University Knowledge Management System Laboratory Department of Artificial Intelligence Engineering Incheon22212 Korea Republic of Inha University Artificial Intelligence Laboratory Department of Computer Science Engineering Incheon22212 Korea Republic of Augmented Knowledge Corporation Incheon22212 Korea Republic of

The field of education is rapidly shifting toward digital platforms;however, aviation education continues to rely heavily on traditional paper-based manuals. This persistence is largely due to the inherent complexity, scale, and high costs associated with aviation equipment, making digital transformation particularly challenging. In this paper, we utilizing the analysis-by-synthesis method is presented for multi-view 3D reconstruction of solid objects, specifically focusing on aviation tools. To facilitate this research, a custom dataset was generated using Unity, enabling controlled experimentation and performance evaluation. Comparative analysis with existing benchmarks demonstrates that, despite the intricate nature of aviation tools compared to simpler objects, the proposed architecture effectively reconstructs detailed and accurate digital replicas. This advancement has the potential to enhance aviation education by providing immersive, interactive 3D content, thereby bridging the gap between traditional learning materials and modern digital education tools. © 2013 IEEE.

关键词： 3D reconstruction

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Innovative feature selection and classification model for heart disease prediction

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Journal of Reliable Intelligent Environments 2022年第4期8卷 333-343页

作者： Nagarajan, Senthil Murugan Muthukumaran, V. Murugesan, R. Joseph, Rose Bindu Meram, Munirathanam Prathik, A. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Mathematics School of Advance Sciences REVA University Bangalore India Department of Mathematics Christ Academy Institute for Advance Studies Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R. K. Valley Andhra Pradesh Nuzividu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

Heart disease is a complex disease that affects a large number of people worldwide. The timely and accurate detection of heart disease is critical in healthcare, particularly in the field of cardiology. In this article, we proposed a system for diagnosing heart disease that is both efficient and accurate, and it is based on machine-learning techniques. The diagnosis of heart disease is found to be a serious concern, so the diagnosis has to be done remotely and regularly to take the prior action. In the present world, finding the prevalence of heart disease has become a key research area for the researchers and many models have crown proposed in the recent year. The optimization algorithm plays a vital role in heart disease diagnosis with high accuracy. Important goal of this work is to develop a hybrid GCSA which represents a genetic-based crow search algorithm for feature selection and classification using deep convolution neural networks. From the obtained results, the proposed model GCSA shows increase in the classification accuracy by obtaining more than 94% when compared to the other feature selection methods. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Novel Technique for Improving Pedestrian Detection Accuracy and Processing Efficiency 7

Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Nov...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Bhavani, N.P.G. Sudha, R.V. Priya, K.Shanmuga Arun Raj, S.R. Yadav, Vinay Kumar Balaram, Allam Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Electronics And Communication Engineering Tamilnadu India Knowledge Institute of Technology Department of Computer Science And Business Systems Selliampalayam Tamil Nadu Kakapalayam India KSR Institute For Engineering And Technology Department of Information Technology TamilNadu Tiruchengode637215 India U.B.D.T College of Engineering Department of Electronics & Communication Engineering Karnataka Davanagere577004 India Glocal University Glocal School of Science And Technology Uttar Pradesh India Mlr Institute of Technology Department of Computer Science And Engineering Hyderabad500043 India

ISBN: (纸本)9798350371406

Pedestrian detection is a critical task in computer vision with the aim of accurately identifying pedestrians in images or video frames. In this work, we tackle the issue of enhancing pedestrian detection precision while reducing processing time. To achieve this, we propose a novel technique called Softsign Gaussian Deep Belief Neural Network (SGDBNN) for pedestrian detection. The proposed SGDBNN technique leverages the power of recurrent deep neural networks to handle the complexities of pedestrian detection. The input layer receives images as input, which are then processed in the hidden layer. Here, a Gaussian activation function is employed to extract relevant features, effectively detecting pedestrian objects in the images. The incorporation of deep learning and recurrent behavior in the neural networks results in reduced incorrect classifications, leading to a lower false positive rate and improved pedestrian detection accuracy. The softsign activation function at the hidden layer allows the proposed technique to efficiently compare extracted features with pre-stored templates, enabling effective pedestrian identification in the images. By effectively utilizing the recurrent behavior and advanced activation functions, the SGDBNN demonstrates superior performance compared to existing methods, such as Deep Convolutional Neural Networks and Extended Deep Model. Experimental evaluations illustrate the effectiveness of the proposed SGDBNN technique. It achieves a significant increase in Positive Detection Accuracy (PDA) of up to 20% and 16%, while simultaneously reducing the False Positive Rate (FPR) by up to 18% and 3% compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Additionally, the proposed SGDBNN reduces training time by up to 30% and 12% and decreases memory requirements by up to 47% and 30% when compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Likewise, the SGDBNN tec

关键词： computer vision

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Architecture of Bluetooth Security for Pairing Key and Better Authentication 5

Architecture of Bluetooth Security for Pairing Key and Bette...

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Mondal, Asif Ikbal Mandal, Bijoy Kumar Dumkal Institute of Engineering Technology Department of Computer Science and Engineering Murshidabad742406 India NSHM Knowledge Campus Department of Computer Science and Engineering Durgapur713212 India

ISBN: (纸本)9781665403412

In this paper, we have deal with the authentication and identification of the device for higher security using key pairing and low computation. Bluetooth when start communication with other device it needs pairing, and then a number of other processes, Profile selection which defines the types of communication. The connection can be compromised and without pairing codes connection can be established. Once the connection is established passing command to the victim's device makes the device to work in favor of the attacker without being noticed by the owner of the device. One such tool is bluesnarfer which is used to pair two devices without being acknowledged by the owner or the victim. This paper is proposed for finding a solution to the same using asymmetric key cryptography and encrypted protocol values to gain access of the different function of the Bluetooth device. The solution is deployed in a number of phases. Public and private key based pairing is used which make the authentication more effective. This is the first phase. In the next second phase there will be we verification protocol in which referential connection is required for making a new connection. In the third phase information regarding profile choice is passed through a tunnel profile, which passed on to the actual profile required. This system contains a threshold function which defines that if at any stage a breach of the system is identified then the system will promote the attacker with improper information to miss lead the attacker so that either the attacker is kept stuck in a miss leading loop or the connection is closed. © 2021 IEEE.

关键词： Bluetooth

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Measuring drug similarity using drug–drug interactions

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Quantitative Biology 2024年第2期12卷 164-172页

作者： Ji Lv Guixia Liu Yuan Ju Houhou Huang Ying Sun College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina Sichuan University Library Sichuan UniversityChengduChina College of Chemistry Jilin UniversityChangchunChina Department of Respiratory Medicine The First Hospital of Jilin UniversityChangchunChina

Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug *** existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of *** this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction *** explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning *** unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group *** addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug *** method exceeds existing approaches in terms of ***,our experiments demonstrate the effectiveness and practicability of the proposed similarity *** the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown *** the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.

关键词： drug similarity drug–drug interactions drug combinations synergy effect clustering semi-supervised learning

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