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Coordination for Connected and Autonomous Vehicles at Unsignalized Intersections: An Iterative Learning-Based Collision-Free Motion Planning Method

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 5439-5454页

作者： Wang, Bowen Gong, Xinle Wang, Yafei Lyu, Peiyuan Liang, Sheng Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 10084 Peoples R China Beijing Inst Technol Dept Mech Engn Beijing 100081 Peoples R China

Motion planning and control of connected and autonomous vehicles (CAVs) for improving traffic efficiency and safety in intersections still meets many challenges due to its dynamic and complex nature. In this article, an innovative collision-free and time-optimal multivehicle motion planning method for the CAVs at unsignalized intersection scenarios is proposed. We systematically analyze the regularity of intersection crossing mode and summarize the overall conflict scenario. To eliminate the vehicles potential collision, a learning-based iterative optimization (LBIO) algorithm is designed to solve the collision-free trajectories generating problem iteratively and offline. The terminal constraint set, terminal cost, and global safe constraints of the LBIO are constructed and updated from the historical data in previous iterations. The algorithm can finally converge to time-optimal trajectories for multivehicle only after several iterations. To apply the trained trajectories into the continuous intersection traffic flow, an online cluster-based motion planning (CBMP) algorithm is developed to coordinate the vehicle velocities and movements in the cooperative control area surrounding the intersection. With an LTV-MPC algorithm for the low-level control, the proposed approach is validated on the SUMO in typical intersection scenarios. The results show that the proposed method allows the potentially conflicting vehicles passing the intersection simultaneously and quickly without waiting, and significantly improves the overall traffic efficiency.

关键词： Trajectory Planning Safety Iterative methods Vehicle dynamics Costs clustering algorithms Connected and autonomous vehicles (CAVs) learning-based iterative optimization (LBIO) motion control trajectory planning unsignalized intersection

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Protecting Privacy in Knowledge Graphs With Personalized Anonymization

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第4期21卷 2181-2193页

作者： Hoang, Anh-Tu Carminati, Barbara Ferrari, Elena Univ Insubria Dept Theoret & Applied Sci DiSTA I-21100 Varese Italy

Knowledge graphs (KGs) are emerging data models allowing data providers to share data. This data sharing might bring new knowledge and collaborations, with evident benefits for providers. However, since KGs might contain sensitive information about users, it is of utmost importance to ensure KG anonymization before publishing. Recently, some proposals have addressed the problem of KGs' anonymization based on the k-anonymity principle. These techniques propose to anonymize the whole dataset with the same anonymization level. However, in a contest where data are collected from different users, it is crucial to consider also users' preferences on the anonymization level to adopt for their data. To cope with this requirement, this paper presents the Personalized k-Attribute Degree (p-k-ad) principle. It allows users to specify their anonymity levels (the k values) while preventing adversaries from re-identifying them with a confidence higher than 1/k with their specified k. Moreover, we design the Personalized Cluster-Based Knowledge Graph Anonymization Algorithm (PCKGA) to generate anonymized KGs satisfying p-k-ad. We conduct experiments on four real-life datasets and show that PCKGA greatly improves the quality of anonymized KGs comparing to previous algorithms.

关键词： Data privacy Information integrity Information filtering clustering algorithms Privacy Proposals Knowledge graphs k-anonymity knowledge graphs personalized privacy

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ESNet: An Efficient Framework for Superpixel Segmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 5389-5399页

作者： Xu, Sen Wei, Shikui Ruan, Tao Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High Speed Railway Syst Beijing 100044 Peoples R China

Superpixel segmentation divides an original image into mid-level regions to reduce the number of computational primitives for subsequent tasks. The two-stage approaches work better but have high computational complexity among the existing deep superpixel algorithms. In contrast, the FCN style approaches cannot extract specific image features for the superpixel task. To combine the advantages of both types of methods, we propose a carefully designed framework termed Efficient Superpixel Network (ESNet) to explicitly enhance the capability of the network to describe clustering-friendly features and simultaneously preserve the simple network structure. Concretely, two points are concerned with ESNet. First, meaningful features need to be constructed for effective superpixel clustering;hence we propose the Pyramid-gradient Superpixel Generator(PSG) to decouple the ESNet into two joint parts, i.e., the feature extractor and the superpixel generator. Second, the superpixel generator is designed in an efficient manner, which performs multi-scale sampling of input images, and can work independently by replacing the introduced feature extractor with two initial convolutional layers. Extensive experiments show that our framework achieves state-of-the-art performances on multi-datasets and is 5.3x smaller on inference than the best existing one-stage FCN-based methods.

关键词： Feature extraction Generators Image segmentation Computer architecture clustering algorithms Task analysis Classification algorithms Superpixel segmentation deep clustering

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Learning With Multiple Kernels

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IEEE ACCESS 2024年 12卷 56973-56980页

作者： Almahdawi, Mahdi A. Cabrera, Omar De La C. King Abdulaziz Univ Appl Coll Dept GRC Jeddah 21589 Saudi Arabia Kent State Univ Coll Arts & Sci Dept Math Sci Kent OH 44242 USA

Over the last decades, learning methods using kernels have become very popular. The main reason is that real data analysis often requires nonlinear methods to detect the dependencies that allow successful predictions of properties of interest. Gaussian kernels have been used in many studies such as learning algorithms and data analysis. Most of these studies have shown that the parameter chosen for a Gaussian kernel could have a huge impact on the desired results. Therefore, it is essential to understand this impact on a theoretical level. The main contribution of this paper is to study the effect of the Gaussian kernel bandwidth parameter on how well an empirical operator defined from data approximates its continuous counterpart. Some results in spectral approximations are provided as well as some examples.

关键词： Eigenvalues and eigenfunctions Bandwidth clustering algorithms Approximation algorithms Principal component analysis Data analysis Gaussian processes Support vector machines Gaussian kernel radial kernels kernel principal component analysis reproducing kernel support vector machine

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Analysis of the Running Ability Mining Model of Football Trainers Based on Dynamic Incremental clustering Algorithm

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022年第1期2022卷 3255886页

作者： Zhao, Peng Xue, Fei Zhang, Xipeng Huanghai Univ Univ Phys Educ Dept Qingdao 266427 Shandong Peoples R China Huanghai Univ Sch Presch Educ Qingdao 266427 Shandong Peoples R China

Fast-running ability is a very important basic quality of football players. However, players are dynamic. It is difficult for coaches to grasp the running speed, instantaneous acceleration, and other indicators of small athletes in real time with the naked eye. Therefore, to accurately test the performance of athletes in fast-running ability, this paper studies the running ability mining model of football coaches based on the dynamic incremental clustering algorithm. According to scientific procedures and methods, the evaluation model and standard of running ability of Chinese elite female football players are established. The effectiveness of the model is 0.83, as verified by the standard recognition method, which shows that the evaluation model is efficient. The research considers the denoising of the original data. The model has rich data and standard test methods and procedures. It can be used as a measure of the running ability of China's elite female football players in a certain period and range. The research solves the problem of the insufficient running ability of domestic football players. It provides an important reference for training the next generation of excellent national football players.

关键词： clustering algorithms

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A Dynamic Programming Algorithm Based clustering Model and Its Application to Interval Type-2 Fuzzy Large-Scale Group Decision-Making Problem

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第1期30卷 108-120页

作者： Pan, Xiaohong Wang, Yingming He, Shifan Chin, Kwai-Sang Fuzhou Univ Decis Sci Inst Fujian 350116 Peoples R China Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing Fujian 350108 Peoples R China City Univ Hong Kong Syst Engn & Engn Management Hong Kong 999077 Peoples R China

This article focuses on employing the dynamic programming algorithm to solve the large-scale group decision-making problems, where the preference information takes the form of linguistic variables. Specifically, considering the linguistic variables cannot be directly computed, the interval type-2 fuzzy sets are employed to encode them. Then, new distance model and similarity model are respectively developed to measure the relationships between the interval type-2 fuzzy sets. After that, a dynamic programming algorithm-based clustering model is proposed to cluster the decision-makers from the overall perspective. Moreover, by taking both the cluster center and the group size into consideration, a new model is introduced to determine the weights of clusters and decision-makers, respectively. Finally, a centroid-based ranking method is developed to compare and rank the alternatives, and two illustrative experiments are provided to illustrate the effectiveness of the proposed method. Comparisons and discussions are also conducted to verify its superiority.

关键词： Linguistics Decision making clustering algorithms Fuzzy sets clustering methods Dynamic programming Heuristic algorithms Centroid-based ranking method dynamic programming algorithm-based clustering model interval type-2 fuzzy sets large-scale group decision-making (GDM)

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Cauchy Density-Based Algorithm for VANETs clustering in 3D Road Environments

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IEEE ACCESS 2022年 10卷 76376-76385页

作者： Al-Obaidi, Ahmed Salih Jubair, Mohammed Ahmed Aziz, Izzatdin Abdul Ahmad, Mohd Riduan Mostafa, Salama A. Mahdin, Hairulnizam Al-Tickriti, Abdullah Talaat Hassan, Mustafa Hamid Univ Tekn Malaysia Melaka UTeM Ctr Telecommun & Res Innovat CeTRI Broadband & Networking BBNET Res Grp Fak Kejuruteraan Elekt & Kejuruteraan Komputer FK Durian Tunggal 76100 Malacca Malaysia Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Tech Engn Al Muthanna 66002 Iraq Univ Teknol PETRONAS Comp & Informat Sci Dept CISD Ctr Res Data Sci CeRDaS Seri Iskandar 32610 Perak Darul Rid Malaysia Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Parit Raja 86400 Johor Malaysia

Vehicular ad hoc networks (VANETs) are emerging to serve various types of applications for serving smart cities and intelligent transportation systems. There are several challenging factors for ensuring reliable and stable VANETs communications. VANETs clustering is essential functionality to serve routing protocols and enable reliable VANETs. clustering algorithms for VANETs operate in a decentralized mode, which requires incorporating additional stages before deciding the clustering decisions and might create sub-optimality due to the local nature of the decentralized approach. In addition, the challenging architecture of the road environment can cause confusing clustering decisions. This problem becomes more challenging due to the evolving nature of clusters in VANETs in general and in 3D VANETs in particular. This paper attempts to solve the problem of VANETs in 3D road environments using a centralized clustering technique to develop a Cauchy density model. The model has been simulated by considering several simulation parameters including traffic, mobility, driving behavior, and road curvature. The simulator also includes an adjacency list that defines the road's points and straight-line segments. The clustering technique of the Cauchy density model determines the mobility vector to enable adding vehicles to their respective clusters. The simulator has been implemented in MATLAB to perform complex scenarios in three locations of 3D road environments. A comparison with selected benchmarks shows the superiority of our model over the benchmarks models in which our model achieves an improvement percentage of 1%, 10%, and 3% for average cluster head duration, average cluster member duration, and clustering efficiency, respectively.

关键词： Three-dimensional displays Roads clustering algorithms Vehicular ad hoc networks Solid modeling Routing Mathematical models VANETs 3D road environment clustering Cauchy density

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Single-Cell RNA Sequencing Data clustering by Low-Rank Subspace Ensemble Framework

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 1154-1164页

作者： Wang, ChuanYuan Gao, Ying-Lian Liu, Jin-Xing Kong, Xiong-Zhen Zheng, Chun-Hou Qufu Normal Univ Sch Comp Sci & Engn Rizhao 276826 Shandong Peoples R China Qufu Normal Univ Lib Qufu Normal Univ Rizhao 276826 Shandong Peoples R China Qufu Normal Univ Sch Software Engn Qufu 273165 Shandong Peoples R China

The rapid development of single-cell RNA sequencing (scRNA-seq)technology reveals the gene expression status and gene structure of individual cells, reflecting the heterogeneity and diversity of cells. The traditional methods of scRNA-seq data analysis treat data as the same subspace, and hide structural information in other subspaces. In this paper, we propose a low-rank subspace ensemble clustering framework (LRSEC)to analyze scRNA-seq data. Assuming that the scRNA-seq data exist in multiple subspaces, the low-rank model is used to find the lowest rank representation of the data in the subspace. It is worth noting that the penalty factor of the low-rank kernel function is uncertain, and different penalty factors correspond to different low-rank structures. Moreover, the single cluster model is difficult to find the cellular structure of all datasets. To strengthen the correlation between model solutions, we construct a new ensemble clustering framework LRSEC by using the low-rank model as the basic learner. The LRSEC framework captures the global structure of data through low-rank subspaces, which has better clustering performance than a single clustering model. We validate the performance of the LRSEC framework on seven small datasets and one large dataset and obtain satisfactory results.

关键词： clustering algorithms Bioinformatics Data models Mathematical model RNA Sequential analysis Periodic structures Ensemble clustering low-rank subspace learning clustering single-cell RNA sequencing data

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General Fuzzy C-Means clustering Strategy: Using Objective Function to Control Fuzziness of clustering Results

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第9期30卷 3601-3616页

作者： Zhao, Kaixin Dai, Yaping Jia, Zhiyang Ji, Ye Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

As one of the most commonly used clustering methods, the fuzzy C-means (FCM) clustering strategy extends the notion of hard clustering to associate each pattern with every cluster using a membership function. Although a lot of efforts have been made by the clustering community, it is still unclear how to evaluate the fuzziness of different versions of FCM. To fill this theoretical blank, by observing a family of objective functions, a definition of fuzzy degree is provided to quantify the fuzziness of different versions of FCM according to their dedicated objective functions. Then, a general fuzzy C-means (GFCM) clustering algorithm is proposed to solve the clustering problem of using FCM under different distance metrics and fuzzy degrees. From the perspective of fuzzy attribute, the using of this fuzzy degree can be used to reveal the essential difference among a group of FCM-based clustering algorithms, and the proposed GFCM achieves the aim of using objective functions to control the fuzziness of clustering results. Additionally, some properties and relations of these FCM-based clustering algorithms under different fuzzy degrees are discussed and the convergence and stability of the proposed GFCM are proved. Finally, extensive experiments have been performed to demonstrate that by comparison with the effect caused by distance metric, the choices of fuzzy degree have a more significant effect and improvement on the performances of a FCM-based clustering algorithm.

关键词： Linear programming clustering algorithms Prototypes Distributed databases Extraterrestrial measurements Optimization Euclidean distance Family of objective functions fuzzy C-means (FCM) fuzzy clustering fuzzy degree

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Dynamic Sparse Subspace clustering for Evolving High-Dimensional Data Streams

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 4173-4186页

作者： Sui, Jinping Liu, Zhen Liu, Li Jung, Alexander Li, Xiang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Aalto Univ Dept Comp Sci Espoo 02150 Finland Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 02150 Finland Natl Univ Def Technol Dept Elect Sci Changsha 410073 Peoples R China

In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.

关键词： clustering algorithms Indexes Heuristic algorithms Data models Adaptation models Task analysis Data structures Data stream clustering (DSC) high-dimensional data stream sparse representation subspace clustering (SC)

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