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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 66-78页

作者： Liu, Siwen Zuo, Yi Li, Tieshan Wang, Huanqing Gao, Xiaoyang Xiao, Yang Navigation College Dalian Maritime University Dalian116026 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Yangtze Delta Region Institute University of Electronic Science and Technology of China Huzhou313000 China Bohai University College of Mathematical Sciences Jinzhou121000 China The University of Alabama Department of Computer Science TuscaloosaAL35487-0290 United States

In the article, an adaptive fixed-time reinforcement learning (RL) optimized control policy is given for nonlinear systems. Radial basis function neural networks (RBFNNs) are exploited to fit uncertain nonlinearities appeared in the considered systems and RL is applied under the critic-actor architecture by using RBFNNs. Specifically, a novel fixed-time smooth estimation system is proposed to improve the estimating performance of RBFNNs. The introduction of the hyperbolic tangent function effectively avoids the singularity problem of the derivative of the virtual controller. The stability analysis shows that the tracking error inclines to an adjustable region near the origin in a fixed-time interval and the boundedness of all signals is obtained. Finally, the intelligent ship autopilot is simulated to prove the utilizability of the obtained control way. © 2024 IEEE.

关键词： Radial basis function networks

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MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems 32

MAX CUT in Weighted Random Intersection Graphs and Discrepan...

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nikoletseas, Sotiris Raptopoulos, Christoforos Spirakis, Paul Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Greece Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959772143

Let V be a set of n vertices, M a set of m labels, and let R be an m × n matrix of independent Bernoulli random variables with probability of success p;columns of R are incidence vectors of label sets assigned to vertices. A random instance G(V, E, RT R) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely [RT R]v,u) between any two vertices u, v for which this weight is strictly larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given G(V, E, RT R) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of G(V, E, RT R) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, m = nα, α Θ(1)n), we show that with high probability, a majority type randomized algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we formally prove a connection between the computational problem of finding a (weighted) maximum cut in G(V, E, RT R) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system Σ with incidence matrix R (i.e. minimum imbalance over all sets in Σ). We exploit this connection by proposing a (weak) bipartization algorithm for the case m = n, p = Θ(1)n that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in a set system with incidence matrix R. In fact, with high probability, the latter 2-coloring corresponds to a bipartition with maximum cut-weight in G(V, E, RT R). Finally, we prove that our (weak) bipartization algorithm terminates in polynomial time, with high probability, at least w

关键词： Graph theory

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Dopamiini: Real-Time Interaction to Combating Loneliness with Smart Wheelchair Integrated Chatbot Support Model

Dopamiini: Real-Time Interaction to Combating Loneliness wit...

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International Conference on Innovations in Science, engineering and Technology (ICISET)

作者： Moslema Hoque Oeishee Munyem Ahammad Muyeed Mishkat Alvi Electrical and Electronic Engineering Department Brac University Dhaka Bangladesh Computer Science and Engineering Department United International University Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

The “Dopamiini” wheelchair, which integrates four control systems and a personalized AI chatbot to help older people overcome their obstacles and loneliness, was developed in this paper. This prototype was constructed using the GPT-3 API for chatbot functionality and the Python library pyttsx3 for offline text-to-speech conversion. When it comes to obstacle recognition, our proposed wheelchair shows an error of no more than ± 0.4 cm up to a distance of 400 cm. It takes an average of 5.6 seconds to reach the maximum speed. In order to monitor and regulate its performance in real time, this is equipped with a dashboard and a GPS navigation system. To make the prototype portable and effective, its weight is close to 2.7 kg.

关键词： Navigation Wheelchairs Prototypes Mental health Chatbots Real-time systems Artificial intelligence Older adults Monitoring Global Positioning System

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Global Graph Clustering and Local Graph Exploration for Community Detection in Twitter 7

Global Graph Clustering and Local Graph Exploration for Comm...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Karras, Christos Karras, Aristeidis Giannoukou, Ioanna Giotopoulos, Konstantinos C. Tsolis, Dimitrios Sioutas, Spyros University of Patras Decentralized Systems Computing Group Computer Engineering and Informatics Department Greece University of Patras Department of Management Science and Technology Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In this paper, the concepts and techniques for global graph clustering are examined, or the process of locating related clusters of vertices within a graph. We introduce the construction of a graph clustering technique based on an eigenvector embedding and a local graph clustering method based on stochastic exploration of the graph. Then, the developed implementations of both methods are presented and assessed in terms of performance. In addition, the difficulties associated with assessing clusterings and benchmarking cluster algorithms are explored where PageRank and EigEmbed algorithms are utilized. The experiments show that the EigEmbed outperformed PageRank across all experiments as it detected more communities with the same number of clusters. Ultimately, we apply both algorithms to a real-world graph representing Twitter network and the followers and tweets therein. © 2022 IEEE.

关键词： Random processes

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Loaded N-port Structure for Reconfigurable Compact MIMO Antenna Design Invited Paper

Loaded N-port Structure for Reconfigurable Compact MIMO Ante...

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2023 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2023

作者： Han, Zixiang Wang, Sen Jin, Jing Wang, Qixing Shen, Shanpu Murch, Ross China Mobile Research Institute Future Research Laboratory Beijing100053 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350308761

A systematic method for designing reconfigurable compact multiple-input multiple-output (MIMO) antennas is described. This method is based on the analysis of a general loaded N - port structure in beamspace domain and is use-ful in optimizing various compact MIMO antennas for next-generation wireless communication. In this method, multiple streams of symbols are mapped onto a set of orthogonal far-field radiation patterns. Then by finding the required loads, the desired radiation patterns can be excited to transmit symbols. Leveraging this method, in this paper, we focus on two MIMO antenna configurations, including single-radio-frequency (RF) electronically steerable parasitic array radiator (ESPAR) and multi-RF compact antenna, and provide efficient optimization methods to find the optimal MIMO antenna design with load values. Simulation results show that by using the proposed method, the single-RF ESPAR can significantly improve the energy efficiency (EE) while achieving close spectral efficiency (SE) when compared with conventional MIMO antennas. In addition for the multi-RF compact antenna, the mutual coupling can be efficiently suppressed by generating orthogonal radiation patterns at multiple ports and the resulting SE can achieve the channel capacity bound. © 2023 IEEE.

关键词： 6G beamspace compact antenna ESPAR MIMO reconfigurable

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Rumor Detection Model with Deep Cross Fusion of Text and Propagation Graph Structures 3

Rumor Detection Model with Deep Cross Fusion of Text and Pro...

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3rd International Conference on computer Science, electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Xu, Kai He, Junfei School of Computer Engineering Guilin University of Electronic Technology Beihai China Chengdu Cloud Power Technology Co. Ltd Department Research and Development Chengdu China

ISBN: (纸本)9798350372052

The generation and spread of rumors are phenomenon that cannot be eradicated in human society. However, the wanton spread of rumors will harm the development of society, especially in the current era of information. The existing rumor detection models or frameworks are basically based on deep neural networks, which can extract text and propagation features and other information aiding rumor assessment, and then combine these features to filter rumors. Although these methods are effective, it is difficult to distinguish rumors in complex contexts or instances where the signs of forgery are not so obvious. Therefore, based on the interactive characteristics of text content and Propagation graph structure, this research proposes a rumor detection model with deep cross fusion. First, the text content and the information of propagation graph structures are encoded while ensuring that they are complete and independent, thereby extracting important information;Then, the separately encoded text content is deeply cross fused with the propagation graph structures, so that the information can penetrate and complement each other, and at the same time, a self-attention operation is performed after the cross fusion of each layer. Finally, the encoding from both components is fully integrated, then a classification feature vector is introduced into the output result of each part, and the classification result is output after multiple layers of fully connected layers. The experiments on three classical rumor detection datasets show that the model has a significant improvement in both accuracy and specified delay time, and the maximum accuracy is improved by 1.21%. © 2023 IEEE.

关键词： Graphic methods

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New In-Air Signature Datasets

New In-Air Signature Datasets

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Alobaidi, Abdulazeez R. Dhieb, Thameur Nuimi, Zeina N. Hamdani, Tarek M. Wali, Ali Alimi, Adel M. University of Sfax REGIM-Lab.: REsearch Groups in Intelligent Machines Enis Tunisia Imam ja'Afar Al-Sadiq University Faculty of Information Technology Department of Computer Techniques Engineering Iraq Higher Institute of Business Administration University of Gafsa Tunisia College of Engineering Department of Electrical Engineering University of Misan Iraq Higher Institute of Computer Science Mahdia University of Monastir Tunisia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Electronic Engineering Science South Africa

ISBN: (纸本)9798350335590

Compared to traditional biometric systems, in-air signatures are considered more robust and secure than classical pen paper. A few datasets capturing in-air signatures have been introduced, utilizing various devices such as the Leap Motion and the Microsoft Kinect sensor camera. However, these devices are not exempt from shortcomings and exhibit certain limitations. The expenses associated with their implementation and the requirement for technical proficiency in operating them present notable challenges for in-air signature analysis. Additionally, users may encounter difficulties in adapting their finger movements to fit within the device's limited field of view, particularly if they lack familiarity with these devices. To address these concerns, this paper proposes the creation of three in-air signature datasets using solely the camera of a laptop or a smartphone, eliminating the need for any additional specialized equipment. Our datasets were collected in three ways. The first is the In-Air Signature dataset (IAS dataset) and the second is the In-Air Signature dataset using a transparent Glass Plate (IASGP dataset) while the third is the In-Air Signature dataset using Smart Phone (IASSP dataset). Forty volunteers participated in the construction of these datasets. Their ages ranged from 21 to 40 years. Each volunteer signs in the air five signatures and imitates five signatures of five other volunteers. Our in-air signatures datasets are publicly available and can be used for various research tasks like in-air signature verification and identification. © 2023 IEEE.

关键词： Cameras

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The study of Machine Learning Scenarios for the Internet of Arctic Things 3

The study of Machine Learning Scenarios for the Internet of ...

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3rd Moscow Workshop on electronic and Networking Technologies, MWENT 2022

作者： Rolich, Alexey Alexander, Ilyin Voskov, Leonid National Research University Higher School of Economics Computer Engineering Department Moscow Russia National Research University Higher School of Economics Electronic Engineering Department Moscow Russia

ISBN: (纸本)9781665496650

The paper investigated the problem of using IoT data transmission technologies in the absence or underdeveloped network infrastructure. As a result of a study of the technologies used in the IoT for data transmission, the LoRaWAN data transmission network was selected. A model of the functioning of IoT devices of a sensor network and a method for increasing the efficiency of data transmission using machine learning methods on terminal data collection devices to reduce the amount of transmitted data and increase the energy efficiency of systems are proposed. The proposed method was evaluated. The proposed method of using machine learning methods significantly increases the lifetime of terminal devices with certain strategies for collecting and processing data. The method allows to increase the maximum number of simultaneously connected devices, by reducing the use of the radio channel, since only processed information is sent. Processing data on edge devices using machine learning methods increases the autonomy of the IoT system, thereby increasing its reliability and providing increased data protection. With the development of computing systems, the use of machine learning on terminal devices will become more widespread. © 2022 IEEE.

关键词： Internet of things

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Bayesian optimization for the design of deep neural networks 12

Bayesian optimization for the design of deep neural networks

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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Giannakis, Nikolas Gorgolis, Nikolaos Hatzilygeroudis, Ioannis University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9781665400329

The design of deep neural networks (DNNs), which in essence concerns the choice of specific values for their hyperparameters, is a very involved process that provides very big challenges to researchers and designers. The fact that there are strong problem-specific dependencies and intuition/experience has been typically used by the designers, has led to the consideration of it as more of an art issue than a well structured and stardardized procedure. The aim of this work is to introduce some structure on the above design process by considering it as a function to be optimized, which takes as input a specific set of hyperparameter values and returns the accuracy of the designed DNN. The process of Bayesian optimization, using Gaussian processes as modeling functions, is employed to fine tune the hyperparameters of DNNs. We arrived at some very promising results. Comparing the proposed process to the random choice of hyperparameters from a specific set, much better accuracy is achieved at no significant extra time cost. Also, the process produces neural network architectures that mimic very closely the known best performing architectures for specific problem sets within the given constraints. © 2021 IEEE.

关键词： Deep neural networks

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Exploring Bound States in the Continuum in Metasurfaces Using Deep Reinforcement Learning

Exploring Bound States in the Continuum in Metasurfaces Usin...

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Photonics North

作者： Abdullah Bin Shams Abdur Rahman Akib Stewart Aitchison Department of Electrical & Computer Engineering University of Toronto Toronto ON Canada Department of Electrical & Electronic Engineering Islamic University of Technology Gazipur Bangladesh

ISBN: (数字)9798350366365

ISBN: (纸本)9798350366372

An inverse design approach to find Bound States in the Continuum (BIC), at a specified wavelength, in all-Dielectric metasurfaces is proposed using Deep Reinforcement Learning (Deep-RL) algorithm. The method is time efficient, and a muti-objective optimization is presented. To demonstrate, a Si split ring is designed in the Fiber-optic communication band that shows a symmetryprotected BIC at a wavelength around 1580 nm.

关键词： Adaptation models Transfer learning Optical fiber networks Metasurfaces Deep reinforcement learning Silicon Nanoscale devices

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