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Age of Information Aware Trajectory Planning of UAV

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第6期10卷 2344-2356页

作者： Pan, Junnan Li, Yun Chai, Rong Xia, Shichao Zuo, Linli Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

This paper investigates the planning of Unmanned aerial vehicles (UAVs) trajectory in UAV-assisted Internet of Things (IoT) networks with a massive number of IoT devices (IoTDs). Existing UAV-assisted IoT network data collection schemes mostly focus on optimizing energy consumption and data collection throughput, while neglecting the temporal value of data collection. With the assistance of the age of information (AoI), the average AoI of data collected by the UAV from IoTDs is minimized to enhance information freshness. To strike a balance between trajectory planning and information freshness, a two-stage artificial intelligence (AI) algorithm is proposed in this paper. Firstly, to tackle the issue of prolonged flight time caused by the UAV sequentially collecting data from IoTDs, an improved clustering algorithm is introduced to determine the cluster centers of IoTDs. Secondly, considering that the UAV lacks prior knowledge of the IoT network environment, the AoI minimization problem is reformulated as a Markov decision process (MDP). A neural network algorithm based on twin-delayed deep deterministic policy gradient (TD3) is employed to optimize UAV trajectory. Simulation results show that the proposed algorithm is superior to the benchmark algorithms, particularly in scenarios involving a massive number of IoTDs.

关键词： Autonomous aerial vehicles Trajectory Trajectory planning Internet of Things clustering algorithms Quality of service Real-time systems Unmanned aerial vehicle (UAV) age of information (AoI) Internet of Things (IoT) deep reinforcement learning (DRL) trajectory planning

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Dholes Hunting-A Multi-Local Search Algorithm Using Gradient Approximation and Its Application for Blockchain Consensus Problem

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IEEE ACCESS 2024年 12卷 93333-93349页

作者： Nguyen, Binh Minh Nguyen, Thieu Vu, Quoc-Hien Hung, Tran Huy Hai, Tran Hoang Binh, Huynh Thi Thanh Tran, Van-Dang Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi 100000 Vietnam

This study introduces a brand-new swarm-inspired algorithm dubbed dholes hunting-based optimization (DhoH) based on an animal hunting strategy to solve global optimization problems. The technique is a brilliant idea for simultaneously finding many local minima. The dhole's hunting strategy is coordinated by members of a swarm, clustering and chasing prey. A clustering approach and finding an optimal global algorithm describe primarily based on gradient approximation. We use four benchmark function datasets to evaluate the DhoH's performance. We compare the achieved results with several previous research from various well-known algorithms. The comparisons demonstrate that DhoH is better than other meta-heuristic algorithms in most cases and determines high-quality solutions with fewer control parameters. Besides, we also explore the application of DhoH in optimizing the decentralized level of Meta-heuristic Proof of Criteria consensus protocol (MPoC) in Blockchain Network to further demonstrate its potential in multi-dimensional problems. The results show the superior effectiveness of DhoH in terms of computational burden and solution precision compared with the existing optimization techniques in the literature.

关键词： clustering algorithms Approximation algorithms Metaheuristics Blockchains Search problems Mathematical models Particle swarm optimization Dholes hunting-based optimization nature-inspired computing swarm-inspired meta-heuristics global search optimization MPoC consensus protocol blockchain network

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Interpreting High-Dimensional Projections With Capacity

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第9期30卷 6038-6055页

作者： Zhang, Yang Liu, Jisheng Lai, Chufan Zhou, Yuan Chen, Siming Fudan Univ Sch Data Sci Shanghai 200437 Peoples R China Fudan Univ Sch Math Sci Shanghai Peoples R China Peking Univ Beijing 100871 Peoples R China

Dimensionality reduction (DR) algorithms are diverse and widely used for analyzing high-dimensional data. Various metrics and tools have been proposed to evaluate and interpret the DR results. However, most metrics and methods fail to be well generalized to measure any DR results from the perspective of original distribution fidelity or lack interactive exploration of DR results. There is still a need for more intuitive and quantitative analysis to interactively explore high-dimensional data and improve interpretability. We propose a metric and a generalized algorithm-agnostic approach based on the concept of capacity to evaluate and analyze the DR results. Based on our approach, we develop a visual analytic system HiLow for exploring high-dimensional data and projections. We also propose a mixed-initiative recommendation algorithm that assists users in interactively DR results manipulation. Users can compare the differences in data distribution after the interaction through HiLow. Furthermore, we propose a novel visualization design focusing on quantitative analysis of differences between high and low-dimensional data distributions. Finally, through user study and case studies, we validate the effectiveness of our approach and system in enhancing the interpretability of projections and analyzing the distribution of high and low-dimensional data.

关键词： Distortion Measurement Distortion measurement Data visualization Statistical analysis clustering algorithms Visual analytics Dimensional reduction interactive visual exploration projection algorithm evaluation

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Exploring the time axis within medium-modified jets

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EUROPEAN PHYSICAL JOURNAL C 2024年第7期84卷 672页

作者： Apolinario, Liliana Guerrero-Rodriguez, Pablo Zapp, Korinna LIP Av Prof Gama Pinto 2 P-1649003 Lisbon Portugal Univ Lisbon Inst Super Tecn IST Av Rovisco Pais 1 P-1049001 Lisbon Portugal Lund Univ Dept Phys Box 118 SE-22100 Lund Sweden

In this manuscript, we illustrate how to use the newly proposed tau\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau $$\end{document} re-clustering algorithm to select jets with different degrees of quenching without biasing their initial transverse momentum spectrum. Our study is based on Z+jet simulated events using the JEWEL Monte Carlo event generator to account for jet quenching effects. We apply the tau\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau $$\end{document} re-clustering algorithm to extract a proxy for a time axis (formation time) within the evolving medium. This information allows us to label jets according to their fragmentation pattern and select populations with enhanced sensitivity to quenching effects. Our results illustrate the potential of jets as precision tools for QGP tomography. Further, we show that the discussed method minimizes the biases stemming from pT\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_{T}$$\end{document}-, Delta R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta R$$\end{document}- or mass-based jet selection.

关键词： clustering algorithms

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The Application of FLICSP Algorithm Based on Multi-Feature Fusion in Image Saliency Detection

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IEEE ACCESS 2024年 12卷 2100-2112页

作者： Li, Zhixian Wu, Jianwei Wang, Guoqiang Luoyang Inst Sci & Technol Sch Comp & Informat Engn Luoyang 471023 Peoples R China

To raise the performance of the current image saliency detection method and promote the extraction effect of salient regions in complex images, from the perspective of multi feature fusion, the image saliency detection method is studied. Corresponding saliency maps is obtained from color and texture channels respectively, and the final saliency map is obtained through linear fusion. On the basis of deep prior information, an improved deep convolutional neural network is used to extract relevant feature vectors such as image pixels, and the nearest neighbor classifier is trained to determine the ownership between pixels and image regions. Zero sample learning iterations are performed to improve the effectiveness of image saliency detection. On this basis, the combination of the above two methods is carried out to obtain the final image saliency detection method. Experimental design is conducted and Matlab software is selected, with a learning rate of 0.001 and 100 iterations. The results showed that compared to other algorithms, the improved algorithm had better detection performance. The detection error of this algorithm was smaller and the accuracy was higher. Its average absolute error value in the JUDD dataset was the smallest, 0.143, which was 0.34 less than the original algorithm. Its detection accuracy in the PASCAL dataset was the highest at 0.786. The missed detection rate and error detection rate of the improved algorithm were both higher than those of the original algorithm, with a missed detection rate of 4.7%, which was 2.5% lower than the original algorithm. Its error detection rate was 5.1%, while the original algorithm's missed detection rate was 3.2%. The improved algorithm resulted in better image quality, with a maximum peak signal-to-noise ratio of 39.45-dB, which was 6.82-dB higher than the original algorithm. The maximum similarity index value was 0.892. Research methods can effectively detect the saliency of complex images.

关键词： Feature extraction clustering algorithms Saliency detection Mathematical models Image segmentation Image color analysis Task analysis FLIC VGG network model texture features color features saliency testing

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SMOTE-MRS: A Novel SMOTE-Multiresolution Sampling Technique for Imbalanced Distribution to Improve Prediction of Anemia

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IEEE ACCESS 2024年 12卷 154675-154699页

作者： Chaerul Ekty Saputra, Dimas Sunat, Khamron Ratnaningsih, Tri Khon Kaen Univ Coll Comp Dept Comp Sci Khon Kaen 40002 Thailand Gadjah Mada Univ Fac Med Publ Hlth & Nursing Dept Clin Pathol & Lab Med Yogyakarta 55281 Indonesia

Anemia is a widespread worldwide health problem that has a substantial effect on groups who are particularly susceptible. The objective of this work is to improve the diagnosis of anemia by creating a hybrid machine learning model called SMOTE-MRS. This model combines SMOTE, K-Means clustering, and Random Over Sampling techniques. The model aims to enhance diagnosis accuracy and ultimately improve healthcare outcomes, specifically in Indonesia, by resolving the imbalance in the dataset. The SMOTE-MRS model mitigates the issue of imbalanced datasets by combining the techniques of SMOTE, K-Means clustering, and random oversampling. K-Means clustering first divides the dataset into K groups. SMOTE then produces artificial instances of the underrepresented class inside every cluster. Random oversampling is a technique that replicates instances of the minority class to make the dataset balanced and enhance the training of machine learning models. The research evaluated the performance of Random Forest (RF), Na & iuml;ve Bayes (NB), and Support Vector Machine (SVM) models in predicting anemia, using the SMOTE-MRS technique. The SMOTE-MRS algorithm exhibited outstanding performance, achieving scores of 0.973 for Accuracy, 0.990 for Recall, 0.968 for Precision, 0.979 for F1-Score, and 0.994 for AUC. These findings demonstrate its exceptional capacity to effectively handle class imbalance and enhance prediction accuracy. The SMOTE-MRS algorithm has outstanding performance on a wide range of datasets, such as those related to anemia, diabetes, breast cancer, and kidney failure datasets. This showcases its robustness and versatility in many predictive modeling scenarios. The findings demonstrate that SMOTE-MRS outperforms standard approaches such as SMOTE, SMOTE-ENC, and ROS in effectively addressing class imbalance. The research affirms the better performance of SMOTE-MRS in addressing class imbalance for the prediction of anemia. SMOTE-MRS surpasses traditional approaches, a

关键词： Accuracy Machine learning algorithms Prediction algorithms Data models Adaptation models Public healthcare Medical diagnostic imaging Computational modeling clustering algorithms SMOTE-MRS anemia imbalanced dataset K-means clustering machine learning SMOTE SMOTE

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Inter-Modality Similarity Learning for Unsupervised Multi-Modality Person Re-Identification

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第10期34卷 10411-10423页

作者： Pang, Zhiqi Zhao, Lingling Liu, Yang Sharma, Gaurav Wang, Chunyu Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China Yangtze River Delta HIT Robot Technol Res Inst Wuhu 241000 Peoples R China Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

RGB (visible), near-infrared (NI), and thermal infrared (TI) imaging modalities are commonly combined for round-the-clock surveillance. We introduce a novel unsupervised multi-modality person re-identification (MM-ReID) task, which, based on an individual's image in any one modality, seeks to identify matches in the other two modalities. Compared to prior MM-ReID problem formulations, unsupervised MM-ReID significantly reduces labeling cost and imaging constraints. To address the unsupervised MM-ReID task, we propose a novel inter-modality similarity learning (IMSL) framework consisting of four synergistic interconnected modules: modality mean clustering (MMC), multi-modality reliability estimation (MMRE), shape-based mutual reinforcement (SMR), and modality-aware invariant learning (MIL). MMC iterates with SMR and MIL in a mutually beneficial manner to provide pseudo-labels that are robust to modality gap. MMRE normalizes sample weights, mitigating the impact of noisy labels in the multi-modality setting. SMR emphasizes shape information to implicitly enhance the model's robustness to the modality gap and is additionally guided by pseudo-labels provided by MMC to attend to identity-related details. MIL explicitly encourages learning of modality-invariant and identity-related features via contrastive feedback for the MMC module. Extensive experimental results on the multi-modality and cross-modality datasets demonstrate that IMSL provides substantial performance gains over existing methods. Code is made available at https://***/zqpang/IMSL.

关键词： Shape Task analysis Reliability clustering algorithms Imaging Feature extraction Circuits and systems Multi-modality person re-identification unsupervised learning contrastive learning

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CoMP and RIS-Assisted Multicast Transmission in a Multi-UAV Communication System

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第6期72卷 3602-3617页

作者： Chen, Jian Zhai, Kaili Wang, Zhaolin Liu, Yuanwei Jia, Jie Wang, Xingwei Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Minist Educ Engn Res Ctr Secur Technol Complex Network Syst Shenyang 110819 Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang 110819 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Kyung Hee Univ Dept Elect Engn Yongin 17104 Gyeonggi Do South Korea

Unmanned aerial vehicle (UAV) assisted communications have been regarded as an effective solution to provide instant services. This paper proposes a novel reconfigurable intelligent surface (RIS) assisted multi-UAV system, where ground users are formed as multicast groups and served by multiple UAVs with coordinated multi-point technique. The goal is to maximize the sum of the minimum rates for all groups by jointly optimizing the trajectories, the cooperative beamforming of the clustered UAVs, and the passive beamforming of the RIS. A hybrid learning scheme is proposed, integrating a multi-agent deep reinforcement learning algorithm, RES-QMIX, and a majorization-minimization (MM)-based alternating optimization. First, the RES-QMIX algorithm is proposed to optimize the trajectories of all UAVs. Then, the alternating optimization is invoked to decouple the joint beamforming into two sub-problems, and each is transformed into a convex quadratic cone programming problem with the MM algorithm. Moreover, the alternating optimization is employed to estimate the reward of the action in RES-QMIX algorithm, thus reducing the action space and achieving the joint optimization. Numerical results show that: 1) The proposed hybrid learning framework achieves fast convergence and outperforms heuristic algorithms;2) The proposed system obtains a more significant communication rate than CoMP and RIS-only systems.

关键词： Optimization Array signal processing Autonomous aerial vehicles Heuristic algorithms Trajectory Resource management clustering algorithms Beamforming CoMP MM multicast RESQMIX RIS

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A Distance Metric for Uneven Clusters of Unsupervised K-Means clustering Algorithm

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IEEE ACCESS 2022年 10卷 86286-86297页

作者： Raeisi, Mostafa Sesay, Abu B. Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada

In this paper, we propose a new distance metric for the K-means clustering algorithm. Applying this metric in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster with a centroid away from the origin, rather than a cluster closer to the origin. The proposed metric is based on the Canberra distances and it is useful for cases that require unequal size clusters. This metric can be used in connected autonomous vehicle wireless networks to classify mobile users such as pedestrians, cyclists, and vehicles. We use a combination of mathematical and exhaustive search to establish its validity as a true distance metric. We compare the K-Means algorithm using the proposed distance metric with five other distance metrics for comparison. These metrics include the Euclidean, Manhattan, Canberra, Chi-squared, and Clark distances. Simulation results depict the effectiveness of our proposed metric compared with the other distance metrics in both one-dimensional and two-dimensional randomly generated datasets. In this paper, we use three internal evaluation measures namely the Compactness, Sum of Squared Errors (SSE), and Silhouette measures. These measures are used to study the proper number of clusters for each of the K-Means algorithms and also select the best run among multiple centroid initializations. The elbow method and the local maximum approach are used alongside the evaluation measures to select the optimal number of clusters.

关键词： Measurement clustering algorithms Classification algorithms Partitioning algorithms Euclidean distance Road traffic Training data Unsupervised learning Canberra distance chi-squared distance clustering algorithm distance metrics Euclidean distance K-means algorithm unsupervised learning

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MetaMFL: Metamorphic Multiple Fault Localization Without Test Oracles

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IEEE TRANSACTIONS ON RELIABILITY 2024年

作者： Fu, Lingfeng Wu, Zhenyu Lei, Yan Yan, Meng Chongqing Univ Sch Big Data& Software Engn Chongqing 400044 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Multiple fault localization (MFL) identifies the positions of multiple faults (i.e., more than one fault) residing in a buggy program. It is notably more difficult as compared with single fault localization (SFL) which aims to locate a single fault (i.e., one fault) in a buggy program. clustering-based multiple fault localization (CBMFL) is amongst the most popular MFL approaches, showing promising results in multiple fault localization. The requisite of launching CBMFL depends on test oracles to acquire the test results (i.e., a pass or a failure). In practice, test oracles are commonly not available known as the oracle problem, and CBMFL becomes infeasible in these cases. Inspired by metamorphic testing in solving the oracle problem, we attempt to combine this technique into CBMFL to broaden its application scope. Thus, we propose MetaMFL: Metamorphic Multiple Fault Localization, which leverages metamorphic testing to extend CBMFL to the cases where test oracles are not available. Specifically, MetaMFL uses metamorphic testing groups as minimum units of testing. It defines metamorphic features for representing those that have violated metamorphic relations. Using these features, CBMFL can perform clustering to support parallel debugging, thus achieving MFL without test oracles. The large-scale experiments show that MetaMFL largely retains the effectiveness of CBMFL even though test oracles are not available.

关键词： Location awareness Testing Debugging clustering algorithms Terminology Runtime Feature extraction Fault diagnosis Codes Big Data Metamorphic testing multiple fault localization (MFL) multiple faults parallel debugging test oracles

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