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UKF-FNN-RIC: A Highly Accurate UWB Localization Algorithm for TOA Scenario

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Zhang, Shuning Wang, Ershen Zhu, Zhanchen Yi, Jingyi Wang, Yongkang Kuai, Enze Shenyang Aerosp Univ Coll Aeronaut & Astronaut Shenyang 110136 Peoples R China Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110136 Peoples R China Monash Univ Dept Data Sci & AI Melbourne Vic 3800 Australia

UWB positioning has become an important branch of indoor localization technology, which has high accuracy in the ideal state but is easily affected by the environment and other factors. To address the accuracy enhancement challenges of indoor UWB positioning across various conditions, a versatile UKF-FNN-RIC algorithm is proposed, leveraging the unscented Kalman filter (UKF), feedforward neural network (FNN), and redundant information correction (RIC). First, various existing UWB positioning algorithms are analyzed, the redundant information in the UWB solution model is studied in depth, and the relationship between the redundant information and the measurement vectors is clarified, thus the error correction algorithm of gradient descent of redundant information is proposed, and the data meets the preconditions of its application through UKF and FNN transforms. The UKF-FNN-RIC algorithm is proposed in time of arrival (TOA) scenarios. Finally, simulation experiments and practical experiments are carried out for two-way ranging (TWR) TOA scenario, which show that the algorithm in the article can greatly improve the accuracy and robustness. The accuracy of fixed point in the practical experiment reaches the millimeter level, the accuracy of track determination reaches the centimeter level, and the algorithms can be easily migrated to other positioning systems.

关键词： Accuracy Location awareness Signal processing algorithms Kalman filters Distance measurement Base stations Heuristic algorithms Robustness clustering algorithms Synchronization Feedforward neural network (FNN) gradient descent method redundant information correction (RIC) time of arrival (TOA) unscented Kalman filter (UKF) UWB localization

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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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Cost-Effective Deployment for Fully-Decoupled RAN: A Techno-Economic Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第11期73卷 17007-17023页

作者： Zhao, Jiwei Chen, Jiacheng Cheng, Bo Qian, Bo Xu, Yunting Zhou, Haibo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310058 Peoples R China Peng Cheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518000 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China China United Network Commun Corp Guangzhou Branch Guangzhou 510630 Peoples R China Natl Inst Informat Informat Syst Architecture Sci Res Div Tokyo 1018430 Japan

With the advent of the Internet of Everything, the forthcoming 6G networks will necessitate an extensive deployment of base stations to achieve comprehensive network coverage. This development presents a formidable challenge for operators due to the surging expenses associated with each base station and the exponential increase in their quantity. Consequently, formulating strategies to mitigate network deployment costs has become an imperative challenge that operators must address urgently. The fully-decoupled radio access network (FD-RAN), through its innovative separation of uplink and downlink base stations, provides the flexibility for deployment in response to asymmetrical service demands, offering a promising solution to the deployment cost dilemma. In this study, we first propose a techno-economic cost model (TECM) for FD-RAN deployment based on the techno-economic approach and then formulate a cost-minimization problem for decoupled network deployment. To tackle the complexity of massive terminals, we present an adjacent gravity-based optimal link clustering algorithm (AGOC) to transform the significant 0-1 programming problem into a mixed integer program. Leveraging FD-RAN's uplink-downlink decoupling, we decompose the original problem into separate subproblems for uplink and downlink network deployments. For the decoupled problem, we further propose a branch-and-cut based network deployment (BCND) algorithm to solve the two decoupled deployment subproblems, respectively. Simulation results indicate that FD-RANs can offer significant cost benefits when facing differentiated service demands. Furthermore, the analysis reveals that power consumption and rental costs are the primary factors influencing network expenses.

关键词： Costs Base stations Uplink Downlink Wireless networks Investment clustering algorithms Branch and cut clustering FD-RAN network deployment techno-economic

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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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Fast Color Images clustering for Real-Time Computer Vision and AI System 8

Fast Color Images Clustering for Real-Time Computer Vision a...

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Machine Learning Workshop of the 8th International Conference on Computational Linguistics and Intelligent Systems, MLW-CoLInS 2024

作者： Vysotska, Victoria Smelyakov, Kirill Sharonova, Nataliia Vakulik, Eugen Filipov, Oleksii Kotelnykov, Ruslan Lviv Polytechnic National University Stepan Bandera Street 12 Lviv79013 Ukraine Kharkiv National University of Radio Electronics Nauky Ave. 14 Kharkiv61166 Ukraine National Technical University "KhPI" Kyrpychova str. 2 Kharkiv61002 Ukraine

The article describes the development of a color image clustering algorithm for a real-time computer vision and AI system. An important feature of the algorithm is the preliminary use of a large number of fast image preprocessing algorithms to optimize the preparation of data for efficient clustering in the color space. The proposed clustering algorithm is focused on processing clusters of arbitrary shape. The paper presents the results of experiments on the processing of monochromatic and non-monochromatic color images. These clustering results are shown as images and as clusters in the Unity environment. Recommendations for practical use, conclusions and links to repositories of experiment results are given. The analysis of the results of a large number of experiments shows that the proposed adaptive clustering algorithm can be effectively used both in powerful computer vision and artificial intelligence systems and in relatively low-power embedded systems by adjusting the parameters to the specifics of the problem. © 2024 Copyright for this paper by its authors.

关键词： clustering algorithms

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Sparse Pinball Twin Bounded Support Vector clustering

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第6期9卷 1820-1829页

作者： Tanveer, M. Tabish, M. Jangir, Jatin IIT Indore Dept Math Indore 453552 India IIT Indore Dept Elect Engn Indore 453552 India

Analyzing unlabeled data is of prime importance in machine learning. Creating groups and identifying an underlying clustering principle is essential to many fields, such as biomedical analysis and market research. Novel unsupervised machine learning algorithms, also called clustering algorithms, are developed and utilized for this task. Inspired by twin support vector machine (TWSVM) principles, a recently introduced plane-based clustering algorithm, the twin bounded support vector clustering (TBSVC), is used in widespread clustering problems. However, TBSVC is sensitive to noise and suffers from low resampling stability due to usage of hinge loss. Pinball loss is another type of loss function that is less sensitive toward noise in the datasets and is more stable for resampling of datasets. However, the use of pinball loss negatively affects the sparsity of the solution of the problem. In this article, we present a novel plane-based clustering method, the sparse TBSVC using pinball loss (pinSTBSVC). The proposed pinSTBSVC is the sparse version of our recently proposed TBSVC using pinball loss (pinTBSVC). Sparse solutions help create better-generalized solutions to clustering problems;hence, we attempt to use the epsilon-insensitive pinball loss function to propose pinSTBSVC. The loss function used to propose pinSTBSVC provides sparsity to the solution of the problem and improves the aforementioned plane-based clustering algorithms. Experimental results performed on benchmark University of California, Irvine (UCI) datasets indicate that the proposed method outperforms other existing plane-based clustering algorithms. Additionally, we also give the application of our method in biomedical image clustering and marketing science. We show that the proposed method is more accurate on real-world datasets too. The code for the proposed algorithm is also provided on the author's Github page: https://***/mtanveer1.

关键词： Support vector machines clustering algorithms Static VAr compensators clustering methods Machine learning algorithms Kernel Stability analysis clustering concave convex procedure (CCCP) machine learning support vector machines (SVMs)

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Fuzzy K-Means clustering With Discriminative Embedding

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第3期34卷 1221-1230页

作者： Nie, Feiping Zhao, Xiaowei Wang, Rong Li, Xuelong Li, Zhihui Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr Opt Imagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian 710072 Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM algorithms assign each data point to multiple clusters with some degree of certainty measured by the membership function. In these methods, the fuzzy membership degree matrix is obtained based on the calculation of the distance between data points in the original space. However, this operation may lead to suboptimal results because of the influence of noises and redundant features. Besides, some FKM clustering methods ignore the importance of the weighting exponent. In this paper, we propose a novel FKM method called Fuzzy K-Means clustering With Discriminative Embedding. Within this method, we simultaneously conduct dimensionality reduction along with fuzzy membership degree learning. To retain most information in the embedding subspace and improve the robustness of this method, principal component analysis is incorporated into our framework. An iterative optimization algorithm is proposed to solve the model. To validate the efficacy of the proposed method, we perform comprehensive analyses, including convergence behavior, parameter determination and computational complexity. Moreover, we also match a appropriate weighting exponent for each data set. Experimental results on benchmark data sets show that the proposed method is more discriminative and effective for clustering tasks.

关键词： clustering algorithms Linear programming clustering methods Principal component analysis Noise measurement Dimensionality reduction Task analysis Fuzzy K-Means dimensionality reduction most information principal component analysis

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A combined clustering/symbolic regression framework for fluid property prediction

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PHYSICS OF FLUIDS 2022年第6期34卷 1-14页

作者： Sofos, Filippos Charakopoulos, Avraam Papastamatiou, Konstantinos Karakasidis, Theodoros E. Univ Thessaly Condensed Matter Phys Lab Dept Phys Lamia 35100 Greece

Symbolic regression techniques are constantly gaining ground in materials informatics as the machine learning counterpart capable of providing analytical equations exclusively derived from data. When the feature space is unknown, unsupervised learning is incorporated to discover and explore hidden connections between data points and may suggest a regional solution, specific for a group of data. In this work, we develop a Lennard-Jones fluid descriptor based on density and temperature values and investigate the similarity between data corresponding to diffusion coefficients. Descriptions are linked with the aid of clustering algorithms, which lead to fluid groups with similar behavior, bound to physical laws. Keeping in mind that the fluid data space goes over the gas, liquid, and supercritical states, we compare clustering results to this categorization and found that the proposed methods can detect the gas and liquid states, while distinct supercritical region characteristics are discovered, where fluid density and temperature affect the diffusion coefficient in a more complex way. The incorporation of symbolic regression algorithms on each cluster provides an in-depth investigation on fluid behavior, and regional expressions are proposed. Published under an exclusive license by AlP Publishing.

关键词： clustering algorithms

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Recognition of Trip-Based Aggressive Driving: A System Integrated With Gaussian Mixture Model Structured of Factor-Analysis, and Hierarchical clustering

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 20442-20451页

作者： Wang, Junhua Xu, Wenxiang Fu, Ting Jiang, Rui Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China China Shandong Int Econ & Tech Cooperat Grp Ltd Investment & Dev Dept Jinan 250098 Shandong Peoples R China

Recognition of aggressive driving behavior helps future research and practices in Intelligent Transport Systems. This paper tries to briefly explain the concepts related to aggressive driving behavior and introduces a system integrated with a novel machine learning algorithm for the recognition of trip-based aggressive driving behavior. The algorithm is a Gaussian Mixture Model (GMM) structured with Factor Analysis (FA), and Hierarchical clustering (HC): common factors were extracted using FA, which is further applied to HC and GMM in the recognition of trip-based aggressive driving. The system is applied in a case study using data from the Shanghai Naturalistic Driving Study, for simulating data collection using the Advanced Driving Assistance System (ADAS) system in a real-traffic situation. Three behavior types (cautious, regular, and aggressive driving) were successfully clustered. For validity, the real aggressive driving behavior records were extracted based on the video, and the proposed system was compared with existing recognition methods. Results indicate that the accuracy of aggressive driving recognition of the system is higher than others (accuracy = 87%). This paper provides a reference in defining and determining aggressive driving, and a robust system for aggressive driving behavior recognition along with the trained algorithm, which can be used in real-world applications for improving driving safety with the applications in ADAS systems, auto-insurance industry.

关键词： Behavioral sciences Vehicles Data models clustering algorithms Computational modeling Machine learning algorithms Dimensionality reduction Aggressive driving driving behavior mixture model hierarchical clustering Gaussian mixture model

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Joint Representation Learning and clustering: A Framework for Grouping Partial Multiview Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3826-3840页

作者： Zhuge, Wenzhang Tao, Hong Luo, Tingjin Zeng, Ling-Li Hou, Chenping Yi, Dongyun Natl Univ Def Technol Coll Liberal Arts & Sci Changsha 410073 Peoples R China Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Peoples R China Hunan First Normal Univ Sch Math & Comp Sci Changsha 410205 Peoples R China

Partial multi-view clustering has attracted various attentions from diverse fields. Most existing methods adopt separate steps to obtain unified representations and extract clustering indicators. This separate manner prevents two learning processes to negotiate to achieve optimal performance. In this paper, we propose the Joint Representation Learning and clustering (JRLC) framework to address this issue. The JRLC framework employs representation matrices to extract view-specific clustering information directly from the presence of partial similarity matrices, and rotates them to learn a common probability label matrix simultaneously, which connects representation learning and clustering seamlessly to achieve better clustering performance. Under the guidance of JRLC framework, several new incomplete multi-view clustering methods can be developed by extending existing single-view graph-based representation learning methods. For illustration, within the framework, we propose two specific methods, JRLC with spectral embedding (JRLC-SE) and JRLC via integrating nonnegative embedding and spectral embedding (JRLC-NS). Two iterative algorithms with guaranteed convergence are designed to solve the resultant optimization problems of JRLC-SE and JRLC-NS. Experimental results on various datasets and news topic clustering application demonstrate the effectiveness of the proposed algorithms.

关键词： Kernel clustering algorithms clustering methods Optimization Iterative methods Convergence Task analysis Representation learning clustering partial multi-view data graph

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