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Evaluation of cluster algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving

SENSORS 2024年第22期24卷 7219页

作者： de Ramos, Daniel Carvalho Ferreira, Lucas Reksua Santos, Max Mauro Dias Teixeira, Evandro Leonardo Silva Yoshioka, Leopoldo Rideki Justo, Joao Francisco Malik, Asad Waqar Fed Technol Univ Parana Dept Elect BR-84017220 Ponta Grossa PR Brazil Univ Brasilia Fac Sci & Technol Engn BR-72444240 Gama DF Brazil Univ Sao Paulo Polytech Sch BR-05508010 Sao Paulo SP Brazil Mississippi State Univ Dept Elect & Comp Engn 406 Hardy Rd 216 Simrall Hall Mississippi State Starkville MS 39762 USA

Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable and robust navigation system. Radar, in particular, operates with electromagnetic waves and remains effective under a variety of weather conditions. It uses point cloud technology to map the objects in front of you, making it easy to group these points to associate them with real-world objects. Numerous clustering algorithms have been developed and can be integrated into radar systems to identify, investigate, and track objects. In this study, we evaluate several clustering algorithms to determine their suitability for application in automotive radar systems. Our analysis covered a variety of current methods, the mathematical process of these methods, and presented a comparison table between these algorithms, including Hierarchical clustering, Affinity Propagation Balanced Iterative Reducing and clustering using Hierarchies (BIRCH), Density-Based Spatial clustering of Applications with Noise (DBSCAN), Mini-Batch K-Means, K-Means Mean Shift, OPTICS, Spectral clustering, and Gaussian Mixture. We have found that K-Means, Mean Shift, and DBSCAN are particularly suitable for these applications, based on performance indicators that assess suitability and efficiency. However, DBSCAN shows better performance compared to others. Furthermore, our findings highlight that the choice of radar significantly impacts the effectiveness of these object recognition methods.

关键词： perception systems driving assistance autonomous vehicles object identification cluster algorithms radar sensor point clouds K-Means Mean-Shift DBSCAN automotive applications

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RARITY OF EXTREMAL EDGES IN RANDOM SURFACES AND OTHER THEORETICAL APPLICATIONS OF cluster algorithms

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ANNALS OF APPLIED PROBABILITY 2020年第5期30卷 2439-2464页

作者： Cohen-Alloro, Omri Peled, Ron Tel Aviv Univ Sch Math Sci Tel Aviv Israel

Motivated by questions on the delocalization of random surfaces, we prove that random surfaces satisfying a Lipschitz constraint rarely develop extremal gradients. Previous proofs of this fact relied on reflection positivity and were thus limited to random surfaces defined on highly symmetric graphs, whereas our argument applies to general graphs. Our proof makes use of a cluster algorithm and reflection transformation for random surfaces of the type introduced by Swendsen-Wang, Wolff and Evertz et al. We discuss the general framework for such cluster algorithms, reviewing several particular cases with emphasis on their use in obtaining theoretical results. Two additional applications are presented: A reflection principle for random surfaces and a proof that pair correlations in the spin O (n) model have monotone densities, strengthening Griffiths' first inequality for such correlations.

关键词： Random surfaces extremal gradients cluster algorithms Swendsen-Wang algorithm Wolff algorithm

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Classification and Flooding Potential Assessment of Subbasins of a Tropical River Using cluster algorithms

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Journal of Flood Risk Management 2025年第2期18卷

作者： Ajith G. Nair R. Kiran Department of Civil Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala India Department of Electronics and Communication Engineering Government Engineering College Idukki Idukki Kerala India

Three clustering algorithms, K-means clustering analysis (KCA), fuzzy cluster analysis (FCA), and density-based spatial clustering of applications with noise (DBSCAN), are applied to classify the 13 subbasins of the Mahe River, southwest India, based on 13 morphometric parameters of each. Suitable validation indices, such as Davies–Bouldin and Calinski–Harabasz indices, have been used to select the optimal number of clusters using KCA and FCA techniques. All three analyses have yielded three clusters, with subbasins 3–8 forming the first one. These constitute 23% of the total basin area of the Mahe. SW 12 forms a grouping of its own. The rest, SW 1–2, 9–11, and 13, form the third cluster. The first cluster corresponds to the subbasins identified as most susceptible to flooding. cluster 3 encompasses the subbasins falling in the “Moderate” and “Least” categories with respect to the risk of flooding. The subbasin 12 (< 1 km 2 ) exhibits a deviant morphometric pattern likely due to its specific topographical and network characteristics. The study reveals that cluster algorithms are effective in ranking and prioritizing subbasins of a river based on their potential for natural hazards like flooding. Moreover, the DBSCAN averts the use of cluster validation indices to determine the optimum clusters without compromising the results. All these methods would be beneficial in chalking out suitable management measures for different subbasins of a river based on their potential for any given hazard.

关键词： cluster algorithms flooding Mahe morphometric parameters subbasins

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Recent progress on cluster and meron algorithms for strongly correlated systems

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INDIAN JOURNAL OF PHYSICS 2021年第8期95卷 1669-1680页

作者： Banerjee, D. HBNI Saha Inst Nucl Phys 1-AF Bidhannagar Kolkata 700064 India

Ab initio studies of strongly interacting bosonic and fermionic systems are greatly facilitated by efficient Monte Carlo algorithms. This article emphasizes this requirement and outlines the ideas behind the construction of the cluster algorithms and illustrates them via specific examples. Numerical studies of fermionic systems at finite densities and at real-times are sometimes hindered by the infamous sign problem, which is also discussed. The construction of meron cluster algorithms, which can solve certain sign problems, is discussed. cluster algorithms which can simulate certain pure Abelian gauge theories (realized as quantum link models) are also discussed.

关键词： cluster algorithms Fermionic and spin models Lattice gauge theories

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Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature

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Computer Methods and Programs in Biomedicine Update 2021年 1卷 100010页

作者： Reddy, Sandeep Bhaskar, Ravi Padmanabhan, Sandosh Verspoor, Karin Mamillapalli, Chaitanya Lahoti, Rani Makinen, Ville-Petteri Pradhan, Smitan Kushwah, Puru Sinha, Saumya Deakin University Australia Medi-AI Australia University of Glasgow United Kingdom University of Melbourne Australia Springfield Clinic IL United States Technology Mindz CA United States South Australian Health and Medical Research Institute RMIT Australia Ausnet Australia

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) late last year has not only led to the world-wide coronavirus disease 2019 (COVID-19) pandemic but also a deluge of biomedical literature. Following the release of the COVID-19 open research dataset (CORD-19) comprising over 200,000 scholarly articles, we a multi-disciplinary team of data scientists, clinicians, medical researchers and software engineers developed an innovative natural language processing (NLP) platform that combines an advanced search engine with a biomedical named entity recognition extraction package. In particular, the platform was developed to extract information relating to clinical risk factors for COVID-19 by presenting the results in a cluster format to support knowledge discovery. Here we describe the principles behind the development, the model and the results we obtained. © 2021

关键词： Biomedical NLP cluster algorithms COVID-19 Text Mining

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Transition matrix cluster algorithms

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2019年第1期30卷

作者： Yevick, David Lee, Yong Hwan Univ Waterloo Dept Phys Waterloo ON N2L 3G1 Canada

We demonstrate that a series of procedures for increasing the efficiency of transition matrix calculations can be realized by integrating the standard single-spin flip transition matrix method with global cluster flipping techniques. Our calculations employ a simple and accurate method based on detailed balance for computing the density of states from the Ising model transition matrix.

关键词： Monte-Carlo methods statistical physics transition matrix methods phase transitions cluster algorithms

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Text Summarization clustered Transformer (TSCT)

Text Summarization Clustered Transformer (TSCT)

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International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Ahmed, Rowanda D. A. AbdulHak, Mansoor ELNabrawy, Omar Hesham Uskudar Univ Istanbul Turkiye Knowbis Solut Consultancy Puchong Selangor Malaysia

ISBN: (数字)9781665473644

ISBN: (纸本)9781665473644

Natural language processing has recently had a considerable reputation due to the quick increase in online and offline data worldwide. The Extractive text summarization grabs the sentence from the corpus using salient related information to produce a concise summary. However, most existing approach to extracting sentence feature engineering has not utilized related contextual information and relation among the sentence. We present clustered Transformer models to mitigate this issue, namely Text summarization using clustered Transformer models. Our proposal has the highest benefit. The utility of our framework working on contextual representation is to grab various linguistic context information. We also use surface features to improve our understanding of word and sentence elements. Another utility is that the hierarchical attention mechanism can capture the contextual relation from the word and sentence levels using the transform model. Also, we added clustering after the transformer model to capture the most similar sentence to improve the attentive quality for producing the extractive text summarization.

关键词： Extractive Text Summarization Transformer Model contextualized representation cluster algorithms Word Embedding Hierarchical Attention Mechanism

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A Computationally Fast but Approximate MIP-DoM Calculation for Multi-Objective Optimization 22

A Computationally Fast but Approximate MIP-DoM Calculation f...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Lopes, Claudio L. d. V. Cruzeiro Martins, Flavio V. Wanner, Elizabeth F. Deb, Kalyanmoy CEFET MG PPMMC Belo Horizonte MG Brazil CEFET MG Dept Comp BR-30510000 Belo Horizonte MG Brazil Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781450392686

Quality indicators play an essential role in multi- and many-objective optimization. Dominance move (DoM) is a binary indicator that compares two solution sets. It measures the minimum move in elements of one set P must do in a way that every element of Q is dominated to at least one element of the moved set P '. As an indicator, it presents some outstanding characteristics as Pareto compliant, absence of the use of reference point or set, and robustness in terms of dominance-resistant solutions. However, DoM computation presents a combinatorial nature. A recent paper proposes a mixed-integer programming model, MIP-DoM, which exhibits a polynomial computational complexity to the number of solutions. Considering practical situations, its calculation on some problems may take hours. Using a cluster-based and divide-and-conquer strategy, this paper presents a computationally fast approximate MIP-DoM to deal with the combinatorial nature of the original calculation. Some classical problem sets are tested, showing that our approach is computationally faster and provides accurate estimates for the exact MIP-DoM.

关键词： Multi-objective optimization Evolutionary multi-objective optimization Machine Learning cluster algorithms

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GPU acceleration of Swendsen-Wang dynamics

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2024年第1期35卷 2450009-2450009页

作者： Protzman, Tristan Giedt, Joel Lehigh Univ Dept Phys Bethlehem PA 18015 USA Rensselaer Polytech Inst Dept Phys Appl Phys & Astron Troy NY 12180 USA

When simulating a lattice system near its critical temperature, local algorithms for modeling the system's evolution can introduce very large autocorrelation times into sampled data. This critical slowing down places restrictions on the analysis that can be completed in a timely manner of the behavior of systems around the critical point. Because it is often desirable to study such systems around this point, a new algorithm must be introduced. Therefore, we turn to cluster algorithms, such as the Swendsen-Wang algorithm and the Wolff clustering algorithm. They incorporate global updates which generate new lattice configurations with little correlation to previous states, even near the critical point. We look to accelerate the rate at which these algorithm are capable of running by implementing and benchmarking a parallel implementation of each algorithm designed to run on GPUs under NVIDIA's CUDA framework. A 17 and 90 fold increase in the computational rate was, respectively, experienced when measured against the equivalent algorithm implemented in serial code.

关键词： cluster algorithms GPU acceleration

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Data Mining to Determine Behavioral Patterns in Respiratory Disease in Pediatric Patients

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第7期12卷 428-436页

作者： Cabanillas-Carbonell, Michael Verdecia-Pena, Randy Herrera Salazar, Jose Luis Medina-Rafaile, Esteban Casazola-Cruz, Oswaldo Univ Privada Norte Fac Ingn Lima Peru Univ Politecn Madrid ETSI Telecomunicac Madrid Spain Univ Norbert Wiener Fac Ingn & Negocios Lima Peru Univ Nacl Santiago Antunez Mayolo Escuela Profes Ingn Sistemas & Informat Ancash Peru Univ Nacl Callao Fac Ingn Ind & Sistemas Callao Peru

There are several varieties of respiratory diseases which mainly affect children between 0 and 5 years of age, not having a complete report of the behavior of each of these. This research seeks to conduct a study of the behavior of patterns in respiratory diseases of children in Peru through data mining, using data generated by the health sector, organizations and research between the years 2015 to 2019. This process was given by means of the K-Means clustering algorithm which allowed performing an analysis of this data identifying the patterns in a total of 10,000 Peruvian clinical records between the years mentioned, generating different behaviors. Through the grouping obtained in the clusters, it was obtained as a result that most of the cases in all the ages studied, they presented diseases with codes between the range of 000 and 060 approximately. This research was carried out in order to help health centers in Peru for further study, documentation and due decision-making, waiting for optimal prevention strategies regarding respiratory diseases.

关键词： Respiratory diseases data mining cluster algorithms K-Means algorithm

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