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Discovering a celestial object using a non-parametric algorithm

PRAMANA-JOURNAL OF PHYSICS 2021年第4期95卷 1-5页

作者： Vyasanakere, Jayanth P. Bhatnagar, Siddharth Murthy, Jayant Azim Premji Univ Sch Arts & Sci Bengaluru 562125 India Univ Geneva Dept Astron Chemin Pegasi 51 CH-1290 Versoix Switzerland Indian Inst Astrophys Bengaluru 560034 India

We describe a method that does not use any orbital parameters, to arrive at the position and mass of a new celestial object, using high-precision orbital state vector data of the rest of the objects in the system. As ... 详细信息

关键词： non-parametric algorithm Neptune iteration method 95 10 Ce 45 50 Pk 95 10 Eg 96 30 Rm

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Gauging-δ: A non-parametric Hierarchical Clustering algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第6期47卷 4897-4907页

作者： Yao, Jinli Pan, Jie Zeng, Yong Concordia Univ Montreal PQ H3G 1M8 Canada Univ Calgary Calgary AB T2N 1N4 Canada

The development of a nonparametric and versatile clustering algorithm has been a longstanding challenge in unsupervised learning due to the exploratory nature of the clustering problem. This study presents a novel algorithm, named Gauging-delta, which can handle diverse cluster shapes and operate in a nonparametric manner. The algorithm employs a hierarchical merging process that starts from individual data points until no further clusters can be merged. The central component of Gauging-delta is the adaptive mergeability function, which progressively determines if two clusters are mergeable considering the perceptual statistics of the clusters and their environment. Empirical evaluations on 105 synthetic datasets demonstrate the superiority of the proposed algorithm, particularly in accurately handling well-separated clusters. Experiments on real-world datasets highlight the impact of selecting appropriate data features and distance metrics on clustering results.

关键词： Clustering algorithms Shape Merging Training Synthetic data Standards Noise Electronic mail Couplings Complexity theory Hierarchical clustering non-parametric algorithm cluster shapes

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KmL3D: A non-parametric algorithm for clustering joint trajectories

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013年第1期109卷 104-111页

作者： Genolini, C. Pingault, J. B. Driss, T. Cote, S. Tremblay, R. E. Vitaro, F. Arnaud, C. Falissard, B. Univ Toulouse 3 INSERM U1027 F-31062 Toulouse France Univ Paris Ouest Nanterre La Def UFR STAPS CeRSM EA 2931 Paris France Univ Montreal Res Unit Childrens Psychosocial Maladjustment Montreal PQ Canada St Justine Hosp Montreal PQ Canada Univ Montreal Int Lab Child & Adolescent Mental Hlth Dev Montreal PQ Canada INSERM U669 Paris France Univ Paris Sud Paris France Univ Paris 05 Paris France Natl Univ Ireland Univ Coll Dublin Sch Publ Hlth Physiotherapy & Populat Sci Dublin 4 Ireland

In cohort studies, variables are measured repeatedly and can be considered as trajectories. A classic way to work with trajectories is to cluster them in order to detect the existence of homogeneous patterns of evolution. Since cohort studies usually measure a large number of variables, it might be interesting to study the joint evolution of several variables (also called joint-variable trajectories). To date, the only way to cluster joint-trajectories is to cluster each trajectory independently, then to cross the partitions obtained. This approach is unsatisfactory because it does not take into account a possible co-evolution of variable-trajectories. KmL3D is an R package that implements a version of k-means dedicated to clustering joint-trajectories. It provides facilities for the management of missing values, offers several quality criteria and its graphic interface helps the user to select the best partition. KmL3D can work with any number of joint-variable trajectories. In the restricted case of two joint trajectories, it proposes 3D tools to visualize the partitioning and then export 3D dynamic rotating-graphs to PDF format. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

关键词： Longitudinal data k-means Cluster analysis non-parametric algorithm Joint trajectories

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A secure edge monitoring approach to unsupervised energy disaggregation using mean shift algorithm in residential buildings

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COMPUTER COMMUNICATIONS 2020年 162卷 187-195页

作者： Liu, Qi Nakoty, Francis Mawuli Wu, Xueyan Anaadumba, Raphael Liu, Xiaodong Zhang, Yonghong Qi, Lianyong Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm 219 Ningliu Rd Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software 219 Ningliu Rd Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Peoples R China Minist Educ Engn Res Ctr Digital Forens Beijing Peoples R China Edinburgh Napier Univ Sch Comp 10 Colinton Rd Edinburgh EH10 5DT Midlothian Scotland Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Jiangsu Peoples R China Qufu Normal Univ Coll Informat Sci & Engn Rizhao Shandong Peoples R China

Compared to Intrusive Load Monitoring which uses smart power meters at each level to be monitored, non-Intrusive Load Monitoring (NILM) is an ingenious way that relies on signal readings at a single point to deduce the share of the devices that have contributed to the overall load. This reliable technique that guarantees the safety and privacy of individual users has recently become an increasingly popular topic, as it turns out to be a major solution to assist household users in the process of obtaining details of their electricity consumption. The detailed consumption promotes better management of the electrical power on the consumer side by helping to eliminate any waste of energy. In this paper, an edge gateway has been implemented to safely monitor the overall load in a smart energy system. A load separation method has been introduced based on events detected on a low-frequency power signal, which allows the consumption profile of On/Off and multi-state devices to be generated without relying on the knowledge of the cardinality of these devices Following the extraction of significant features contained in the aggregate signal, an appliance profile recognition approach is presented based on the non-parametric Mean Shift algorithm. The ability of the proposed method to learn and deduce devices profile is validated using the Reference Energy Disaggregation Dataset (REDD). The experimental results show that the proposed approach is efficient in detecting events of binary state and finite state appliances.

关键词： Home energy management non-intrusive load monitoring (NILM) Unsupervised learning non-parametric algorithm

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MEAN SHIFT: A non-parametric algorithm FOR THE SEGMENTATION OF ANOMALIES IN GEOPHYSICAL IMAGES OBTAINED FROM MAGNETIC PROSPECTION DATA

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ARCHAEOMETRY 2011年第3期53卷 642-659页

作者： Salguero, F. Prat, F. Moreno, F. Romero, S. Univ Huelva Dept Min Engn Mech & Energet Huelva 21071 Spain Univ Huelva Dept Math Huelva 21071 Spain

This paper studies the applicability of the Mean Shift algorithm as support in interpreting geophysical images produced, on this occasion, from magnetic prospection data. The data obtained from a magnetic survey carried out in Gilena (Seville province, Spain) by the La Rabida Archaeophysics Group will be used for the research. Its applicability is illustrated by comparing, on the one hand, some reduction-to-pole algorithms and on the other, the (well-known) k-means algorithm. Finally, the paper shows the results obtained by applying the Mean Shift algorithm as an alternative method to 'unsupervised clustering' of anomalies that appear in images obtained from geophysical data, in which the a priori knowledge of the number of classes is difficult or impossible.

关键词： MEAN SHIFT SEGMENTATION MAGNETIC PROSPECTION GILENA (SW SPAIN) DIGITAL IMAGE PROCESSING non-parametric algorithm k-MEANS

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DEGPACK: A web package using a non-parametric and information theoretic algorithm to identify differentially expressed genes in multiclass RNA-seq samples

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METHODS 2014年第3期69卷 306-314页

作者： An, Jaehyun Kim, Kwangsoo Chae, Heejoon Kim, Sun Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Seoul Natl Univ Bioinformat Inst Seoul South Korea Seoul Natl Univ Interdisciplinary Program Bioinformat Seoul South Korea Indiana Univ Dept Comp Sci Sch Informat & Comp Bloomington IN 47405 USA

Gene expression in the whole cell can be routinely measured by microarray technologies or recently by using sequencing technologies. Using these technologies, identifying differentially expressed genes (DEGs) among multiple phenotypes is the very first step to understand difference between phenotypes. Thus many methods for detecting DEGs between two groups have been developed. For example, T-test and relative entropy are used for detecting difference between two probability distributions. When more than two phenotypes are considered, these methods are not applicable and other methods such as ANOVA F-test and Kruskal-Wallis are used for finding DEGs in the multiclass data. However, ANOVA F-test assumes a normal distribution and it is not designed to identify DEGs where genes are expressed distinctively in each of phenotypes. Kruskal-Wallis method, a non-parametric method, is more robust but sensitive to outliers. In this paper, we propose a non-parametric and information theoretical approach for identifying DEGs. Our method identified DEGs effectively and it is shown less sensitive to outliers in two data sets: a three-class drought resistant rice data set and a three-class breast cancer data set. In extensive experiments with simulated and real data, our method was shown to outperform existing tools in terms of accuracy of characterizing phenotypes using DEGs. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Differentially expressed genes Multiclass RNA-seq non-parametric algorithm Information theoretic algorithm

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An adaptive mode control algorithm of a scalable intrusion tolerant architecture

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2012年第6期78卷 1751-1774页

作者： Dohi, Tadashi Uemura, Toshikazu Hiroshima Univ Grad Sch Engn Dept Informat Engn Higashihiroshima 7398527 Japan

In this paper we consider an intrusion tolerant system with two detection modes;automatic detection mode and manual detection mode for intrusions, which is called SITAR (Scalable Intrusion Tolerant Architecture), and describe the dynamic transition behavior by a continuous-time semi-Markov chain (CTSMC). Based on the embedded Markov chain (EMC) approach, we derive the steady-state probability of the CTSMC, the steady-state system availability and the mean time to security failure (MTTSF). Especially, we show necessary and sufficient conditions to exist the optimal switching time from an automatic detection mode to a manual detection mode, which maximizes the steady-state system availability. Next, we develop an adaptive mode control scheme to estimate the optimal switching time without specifying the associated probability distribution function, whose idea behind is based on a statistically non-parametric algorithm by means of the total time on test concept. Numerical examples through a comprehensive simulation study are presented for illustrating the optimal switching of detection mode, and investigating the asymptotic property of the resulting adaptive mode control scheme. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Adaptive mode control Intrusion tolerance SITAR System availability MTTSF Semi-Markov modeling EMC approach Statistical estimation non-parametric algorithm Security/dependability analysis

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Kml: A package to cluster longitudinal data

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2011年第3期104卷 E112-E121页

作者： Genolini, Christophe Falissard, Bruno INSERM U669 F-75014 Paris France Univ Paris 11 Paris France Univ Paris 05 UMR S0669 Paris France Univ Paris 10 UMR S0669 Paris France Hop Bicetre AP HP Psychiat Serv Le Kremlin Bicetre France

Cohort studies are becoming essential tools in epidemiological research. In these studies, measurements are not restricted to single variables but can be seen as trajectories. Thus, an important question concerns the existence of homogeneous patient trajectories. KmL is an R package providing an implementation of k-means designed to work specifically on longitudinal data. It provides several different techniques for dealing with missing values in trajectories (classical ones like linear interpolation or LOCF but also new ones like copyMean). It can run k-means with distances specifically designed for longitudinal data (like Frechet distance or any user-defined distance). Its graphical interface helps the user to choose the appropriate number of clusters when classic criteria are not efficient. It also provides an easy way to export graphical representations of the mean trajectories resulting from the clustering. Finally, it runs the algorithm several times, using various kinds of starting conditions and/or numbers of clusters to be sought, thus sparing the user a lot of manual re-sampling. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Package presentation Longitudinal data k-Means Cluster analysis non-parametric algorithm

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KmL: k-means for longitudinal data

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COMPUTATIONAL STATISTICS 2010年第2期25卷 317-328页

作者： Genolini, Christophe Falissard, Bruno INSERM U669 Paris France Univ Paris Ouest Nanterre Def Paris France Univ Paris Sud Paris France Univ Paris 05 UMR S0669 Paris France Hop Paul Brousse AP HP Dept Sante Publ Villejuif France

Cohort studies are becoming essential tools in epidemiological research. In these studies, measurements are not restricted to single variables but can be seen as trajectories. Statistical methods used to determine homogeneous patient trajectories can be separated into two families: model-based methods (like Proc Traj) and partitional clustering (non-parametric algorithms like k-means). KmL is a new implementation of k-means designed to work specifically on longitudinal data. It provides scope for dealing with missing values and runs the algorithm several times, varying the starting conditions and/or the number of clusters sought;its graphical interface helps the user to choose the appropriate number of clusters when the classic criterion is not efficient. To check KmL efficiency, we compare its performances to Proc Traj both on artificial and real data. The two techniques give very close clustering when trajectories follow polynomial curves. KmL gives much better results on non-polynomial trajectories.

关键词： Functional analysis Longitudinal data k-means Cluster analysis non-parametric algorithm

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Towards Autonomic Mode Control of a Scalable Intrusion Tolerant Architecture

Towards Autonomic Mode Control of a Scalable Intrusion Toler...

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7th International Conference on Autonomic and Trusted Computing

作者： Dohi, Tadashi Uemura, Toshikazu Hiroshima Univ Grad Sch Engn Dept Informat Engn Higashihiroshima 7398527 Japan

ISBN: (纸本)9783642165757

In this article we consider an intrusion tolerant system with two detection modes;automatic detection mode and manual detection mode for intrusions, and describe the dynamic transition behavior by a continuous-time semi-Markov chain (CTSMC). Based on the embedded Markov chain (EMC) approach, we derive the steady-state probability of the CTSMC, the steady-state system availability and the mean time to security failure (MTTSF). Especially, we show necessary and sufficient conditions to exist the optimal switching time from an automatic detection mode to a manual detection mode, which maximizes the steady-state system availability. Next, we develop an autonomic mode control scheme to estimate the optimal switching time without specifying any probability distribution function in an adaptive way, where the basic idea comes from a statistically non-parametric algorithm by means of the total time on test concept. Numerical examples through a simulation study are presented for illustrating the optimal switching of detection mode, and investigating the asymptotic property of the resulting autonomic mode control scheme.

关键词： autonomic control intrusion tolerance SITAR system availability MTTSF CTSMC EMC approach statistical estimation non-parametric algorithm adaptive optimization

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