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Aerodynamics-Based Collision-Free Control of Connected Drones in Complex Urban Low-Altitude Airspace Using Distributional Reinforcement Learning

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IEEE Transactions on Vehicular technology 2024年第7期73卷 9763-9775页

作者： Liao, Bing-Hao Lee, Chao-Yang Wang, Li-Chun National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu City30010 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin County64002 Taiwan

Recent growth in the number of drones has made traffic management unworkable, particularly in urban areas. The safe operation and optimized navigation of drone swarms are now growing concerns. In this article, we use distributional reinforcement learning to prevent drone collisions via aerodynamic acceleration and deceleration. Although the instantaneous acceleration or deceleration of drones during an encounter influences drone kinematics, we note that drone kinematics itself can prevent collisions via instantaneous acceleration or deceleration. This article's central question, then, is how to take drone kinematics into account to improve air traffic intersection flow performance. First, the intercommunication between drone swarms is established via an Internet of drones to send and receive the current state of each drone in the swarm and detect potential collisions. In the presence of obstacles, we first use distributional reinforcement learning to find the inevitable change in kinematic height and guide descents and ascents through air traffic intersections. Here, the drone's energy consumption during instantaneous acceleration or deceleration cannot be ignored. A properly designed speed control is crucial to the power management of the drone. Distributional reinforcement learning is used to control drone acceleration and deceleration to avoid collision risk while minimizing energy consumption according to the reference energy recorded by each drone. We flew drones to gather energy consumption data and, to validate our approach, we subsequently conducted simulations in which travel time and energy consumption were measured. We demonstrate that the proposed kinematics-based drone collision avoidance method exhibits excellent performance in terms of travel time and power management. © 1967-2012 IEEE.

关键词： Kinematics

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Enhancing Vehicle Routing with Time Windows: A Machine Learning-Driven Ant Colony Optimization Approach

Enhancing Vehicle Routing with Time Windows: A Machine Learn...

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2024 International Conference on Frontiers of Information technology, FIT 2024

作者： Tariq, Khizer Safdar, Ali Awais Zaman, Mubashir Shah, Syed Usman Ali School of Electrical Engineering and Computer Science National University of Sciences & Technology Islamabad Pakistan

ISBN: (纸本)9798331510503

The Vehicle Routing Problem with Time Windows (VRPTW) is a challenging optimization problem in logistics and transportation, where the objective is to efficiently plan routes for vehicles to service a set of customers within specified time windows while minimizing costs. This paper introduces a novel approach to solving VRPTW using a Machine Learning-Driven Ant Colony Optimization (ML-ACO) algorithm, referred to as Machine Learning-Driven Ant Colony Optimization for Vehicle Routing with Time Windows (ML-ACO-VRPTW). The proposed algorithm enhances traditional ACO by integrating a machine learning model, to predict heuristic values that capture the urgency of time windows and vehicle capacity constraints. The algorithm begins with initializing pheromone levels and parameters, followed by constructing routes based on probabilistic path selection influenced by pheromone intensity and machine learning-predicted heuristic values. Key innovations include dynamic pheromone updates that adapt to the algorithm’s progress and the incorporation of penalties for time window violations and vehicle capacity exceedances. The pheromones are used to maintain a memory of the paths, and the machine learning model ensures adaptive and accurate heuristic predictions. © 2024 IEEE.

关键词： Ant colony optimization

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Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Han, Seungwoo Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Japan

ISBN: (纸本)9798350386226

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification. © 2024 IEEE.

关键词： Graph neural networks

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Parallel Implementation of the CCSDS Turbo Decoder on GPU

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China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

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Distributed field mapping for mobile sensor teams using a derivative‐free optimisation algorithm

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IET Cyber-Systems and Robotics 2024年第2期6卷 20-34页

作者： Tony X.Lin Jia Guo Said Al-Abri Fumin Zhang School of Electrical&Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA College of Engineering Sultan Qaboos UniversityMuscatOman Department of Electronic&Computer Engineering Hong Kong University of Science and TechnologyHong KongChina

The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration objectives between areas of high error and high *** computing high error regions is impossible since the scalar field is unknown,a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP *** approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the *** approach is validated in simulations and experiments using 2D wheeled robots and 2D flying mini-ature autonomous blimps.

关键词： environment sensing multi-robot systems

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Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

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computers, Materials & Continua 2023年第5期75卷 2491-2508页

作者： Ashir Javeed Ana Luiza Dallora Johan Sanmartin Berglund Arif Ali Peter Anderberg Liaqat Ali Aging Research Center Karolinska InstitutetStockholm17165Sweden Department of Health Blekinge Institute of TechnologyKarlskrona37141Sweden School of Health Sciences University of SkövdeSkövdeSE 54128Sweden Department of Computer Science University of Science and Technology BannuBannu28100Pakistan Department of Electrical Engineering University of Science and Technology BannuBannu28100Pakistan

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity,mortality,and *** there is a consensus that dementia is a multifactorial disorder,which portrays changes in the brain of the affected individual as early as 15 years before its onset,prediction models that aim at its early detection and risk identification should consider these *** study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data,which comprised 75 *** are two automated diagnostic systems developed that use genetic algorithms for feature selection,while artificial neural network and deep neural network are used for dementia *** proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%,sensitivity of 93.15%,specificity of 91.59%,MCC of 0.4788,and performed superior to other 11 machine learning techniques which were presented in the past for dementia *** identified best predictors were:age,past smoking habit,history of infarct,depression,hip fracture,single leg standing test with right leg,score in the physical component summary and history of TIA/*** identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.

关键词： Dementia prediction feature selection genetic algorithm neural networks

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An Adaptive Online Few-shot Network Intrusion Detection System Based on Meta-Learning 12

An Adaptive Online Few-shot Network Intrusion Detection Syst...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Ding, Shiru Wang, Zhen Lu, Yifei School of Electrical and Automation Engineering Nanjing Normal University China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798331511074

Previous deep learning-based Network Intrusion Detection Systems (NIDS) require a sufficient number of labeled samples to train deep neural network models. However, in certain scenarios of the Internet of Things (IoT), such as zero-day attacks, abnormal data is scarce and cannot meet the training conditions for neural network models. Thus, there is a need for a NIDS capable of few-shot learning. Additionally, previous online network intrusion detection systems did not comprehensively consider the significant impact of data feature drift and catastrophic forgetting on the performance of online models. In order to solve these two problems, we designed an adaptive online few-shot network intrusion detection system based on meta-learning. We evaluated the proposed system using the public datasets CIC-IDS2017, and the results show that our system can effectively handle the challenge of small samples while continuously adapting to new data with minimal catastrophic forgetting. © 2024 IEEE.

关键词： Zero-shot learning

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Design of a Model-Free Controller based on Predictive Functional Control

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2024年第1期144卷 15-20页

作者： Ashida, Yoichiro Katayama, Masaru Department of Electrical Engineering and Computer Science National Institute of Technology Matsue College 14-4 Nishi-Ikuma Shimane Matsue690-8518 Japan

Intelligent-PID (i-PID) control proposed by Fliess is a simple control algorithm. The controller is designed based on ultra-local model, and consisted of PID type controller and derivatives of reference signal and controlled variables. Authors have considered discrete i-PID controller and its properties, and one of the result was that PD controller was sufficient in discrete time domain. In discrete i-PID controller, PD type controller is still required to ensure convergence of controlled error to zero, and PD type parameters affects convergence property, fast-response property and stability. In this paper, predictive functional control (PFC) based on an ultra-local model is proposed. By introducing of PFC, controlled variable converge to reference value without PD type controller. User-specified parameters of the proposed controller is smaller than the conventional i-PID controller. Characteristics of the proposed controller are compared with discrete i-PID controller by numerical examples. © 2024 The Institute of electrical Engineers of Japan.

关键词： Controllers

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Impact of UV content of illumination on acceptability of colour reproduction 12

Impact of UV content of illumination on acceptability of col...

引用

12th Colour and Visual Computing Symposium, CVCS 2024

作者： Nussbaum, Peter Gudzenchuk, Anastasiia Green, Phil Faculty of Information Technology and Electrical Engineering Department of Computer Science NTNU Norway Department of Science and Technology Linköping University Sweden

Paper and board for printing commonly contains optical brightening agents that fluoresce in the presence of UV radiation, making the prints appear brighter and bluer. If the relative amount of optical brightening agents (OBAs) is not consistent between proof and print, and the UV content of measurement and the viewing illumination differs, there will be a colorimetric difference between proof and print. Colour management workflows are normally media-relative, and visual adaptation to the media white can conflict with the goals of colorimetric matching. The impact of UV content in measurement and viewing illumination on the visual acceptability of colour reproductions was investigated in a series of psychophysical experiments, using substrates with varying OBA concentrations and a media-relative colour reproduction workflow. The results showed that visual acceptability was largely unaffected by UV content of measurement or viewing illumination, suggesting that visual adaptation to media white discounts the effect of fluorescence. © 2024 Copyright for this paper by its authors.

关键词： Colorimetry

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