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IEEE International Instrumentation and Measurement Technology Conference

作者： Massimo D'Apuzzo Mauro D'Arco Giacomo Ianniello Annalisa Liccardo Dept. of Electrical Engineering University ofNaples Federico II via Claudio 21 80125 Napoli ITALY

ISBN: (纸本)9781467346214

Modeling and analysis of the functioning of power systems is very difficult because of time varying and non-linear behaviors. Often power systems are described trough simplified approaches. The most widespread one recognizes load conditions that can be considered stationary within limited time intervals, in order to apply thus standard harmonic analysis techniques. Here a data segmentation approach to distinguish the time intervals in which stationary load conditions can be recognized is discussed and assessed.

关键词： System identification load conditions data segmentation harmonic source power quality

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Protecting Privacy, Civil Rights and Civil Liberties using data Labeling and segmentation

Protecting Privacy, Civil Rights and Civil Liberties using D...

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IEEE International Symposium on Technologies for Homeland Security (HST)

作者： Schmoyer, Timothy R. Doyle, Bill Dufel, Michael Higa-Smith, Karyn Jericho Syst Corp Dallas TX 75205 USA Homeland Secur Adv Res Projects Agcy Dept Homeland Secur Sci & Technol Directorate Washington DC USA

ISBN: (纸本)9781479917372

The Cyber Security Division of Department of Homeland Security (DHS) Science and Technology's Homeland Security Advanced Research Projects Agency is prototyping data labeling and data segmentation services that leverage dynamic fine-grained access control to protect information considered sensitive and/or related to privacy, civil rights, and civil liberties (P/CRCL). Prototype operationalizes significant work that has already been completed in the Information Sharing Environment (ISE) and the schemas and documentation available in National Information Exchange Model (NIEM) Information Exchange Package Documentation (IEPD) for Critical Infrastructure and Key Resources fusion center information sharing requirements. This paper describes three Unified Modeling Language (UML) use cases, concept of operations, and architecture for using dynamic fine-grained access control, data labeling, and data segmentation to anonymize, mask, or redact data that might be shared by a Fusion Center in accordance with organizational, jurisdictional, and Community of Interest (COI) laws, regulations, and policies.

关键词： cybersecurity privacy fusion center information sharing access control data segmentation content filtering de-identification P/CRCL NIEM UML IAM IdAM FICAM BAE ABAC DHS

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The Multifractal Gaussian Mixture Model for unsupervised segmentation of complex data sets

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SPATIAL STATISTICS 2025年 66卷

作者： Jacyna, Garry Frezza, Damon Slater, David M. Thompson, James R. Mitre Corp 7515 Colshire Dr Mclean VA 22201 USA

We derive the Multifractal Gaussian Mixture Model algorithm for decomposing data sets into different multifractal regimes building on the empirical observation that simulated multifractals have log wavelet leaders that are well-approximated by a Gaussian distribution. We test the algorithm on composite images constructed from multifractal random walks with known multifractal spectra. The algorithm is able to correctly segment the pixels corresponding to different multifractals when the constituent multifractals are most distinct from each other. It also estimates the multifractal parameters with minimal error when compared to the theoretical spectra used to generate the original multifractal random walks. We also apply the algorithm to satellite images with varying degrees of cloud cover taken from the LandSat 8 Cloud Validation data set. The algorithm is able to segment the pixels into their corresponding cloud mask category, and it detects different texture and features in the images that are unrelated to clouds. The results indicate that the Multifractal Gaussian Mixture Model algorithm is well-suited for semi-automated unsupervised data segmentation when the data being analyzed exhibit complex, scale-invariant characteristics.

关键词： Multifractal analysis data segmentation Gaussian mixture models Image analysis

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segmentation of Multivariate mixed data via lossy data coding and compression

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2007年第9期29卷 1546-1562页

作者： Ma, Yi Derksen, Harm Hong, Wei Wright, John Univ Illinois Dept Elect & Comp Engn Coordinated Sci Lab Urbana IL 61801 USA Univ Michigan Dept Math Ann Arbor MI 48109 USA Texas Instruments Inc DSP Solut Res & Dev Ctr Dallas TX 75266 USA

In this paper, based on ideas from lossy data coding and compression, we present a simple but effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions, which are allowed to be almost degenerate. The goal is to find the optimal segmentation that minimizes the overall coding length of the segmented data, subject to a given distortion. By analyzing the coding length/rate of mixed data, we formally establish some strong connections of data segmentation to many fundamental concepts in lossy data compression and rate-distortion theory. We show that a deterministic segmentation is approximately the (asymptotically) optimal solution for compressing mixed data. We propose a very simple and effective algorithm that depends on a single parameter, the allowable distortion. At any given distortion, the algorithm automatically determines the corresponding number and dimension of the groups and does not involve any parameter estimation. Simulation results reveal intriguing phase-transition-like behaviors of the number of segments when changing the level of distortion or the amount of outliers. Finally, we demonstrate how this technique can be readily applied to segment real imagery and bioinformatic data.

关键词： multivariate mixed data data segmentation data clustering rate distortion lossy coding lossy compression image segmentation microarray data clustering

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segmentation of vehicle detector data for improved k-nearest neighbours-based traffic flow prediction

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IET INTELLIGENT TRANSPORT SYSTEMS 2015年第3期9卷 264-274页

作者： Bernas, Marcin Placzek, Bartlomiej Porwik, Piotr Pamula, Teresa Univ Silesia Inst Comp Sci PL-41200 Sosnowiec Poland Silesian Tech Univ Fac Transport PL-40019 Katowice Poland

This study presents a data segmentation method, which was intended to improve the performance of the k-nearest neighbours algorithm for making short-term traffic volume predictions. According to the introduced method, selected segments of vehicle detector data are searched for records similar to the current traffic conditions, instead of the entire database. The data segments are determined on the basis of a segmentation procedure, which aims to select input data that are useful for the prediction algorithm. Advantages of the proposed method were demonstrated in experiments on real-world traffic data. Experimental results show that the proposed method not only improves the accuracy of the traffic volume prediction, but also significantly reduces its computational cost.

关键词： road vehicles pattern classification road traffic vehicle detector data segmentation improved k-nearest neighbours traffic flow prediction traffic volume predictions data segmentation segmentation procedure traffic volume prediction

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A Novel Accuracy-Constrained Scheme for Efficient Trend Extraction of Industrial Time-Series data

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Liu, Ju Zhao, Jiayi Ye, Hao Huang, Dexian Shang, Chao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Luoyang Elect Equipment Test Ctr Luoyang 471000 Henan Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

data trend extraction provides a useful means to qualitatively capture the underlying variations of time-series data. A class of algorithms is built upon segmentation and piecewise polynomial fitting, where the model complexity is primarily controlled by the number of data segments. However, it is not trivial to specify when tackling datasets of different sizes, and expensive computations are required in current global optimization algorithms. To address these issues, we propose a novel data trend extraction and segmentation method based on accuracy-constrained polynomial fitting. Two normalized indices are coined to define constraints on the accuracy of piecewise polynomial fitting, which allows for an interpretable and clear tuning guideline to regulate model complexities of segmentation when facing data trajectories of different lengths. By exploiting the structure of the constrained fitting problem, a breadth-first search (BFS) algorithm is established, with two branch pruning (BP) strategies designed to remarkably improve the solution efficiency. In particular, we prove that the proposed solution algorithm has a desirable O(n(2)) complexity that does not grow with the number of segments and is much lower than that of generic global optimization algorithms. Comprehensive case studies show that compared with conventional methods, our approach enjoys better empirical performance, easier tuning of parameters, and lower computational cost.

关键词： data segmentation dynamic programming (DP) polynomial fitting polynomial fitting tree search tree search trend extraction trend extraction trend extraction

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MLS: a novel hybrid security framework utilizing the Wiedemann algorithm and chaotic mapping for MQTT

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-34页

作者： Rezaei, Azita Broumandnia, Ali Mirabedini, Seyed Javad Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran

The increasing demand for the Internet of Things (IoT) influences cloud computing security policies. Various methods have been developed to protect against hackers, with advanced techniques often favored due to their ability to enhance the robustness of algorithms. Each cloud platform includes a security broker (SB) section that plays a vital role in data balancing and management while supporting Message Queuing Telemetry Transport (MQTT) protocols. This article presents a multilayered security (MLS) approach that integrates a phased encryption method, combining the Wiedemann algorithm and chaotic mapping to enhance data security. Initial results show enhancements in three key areas: security, efficiency, and availability, achieved through a weighted and prioritized strategy. Furthermore, the article discusses the customer satisfaction index and techniques for attack mitigation. The results indicate a 60.67% increase in the customer satisfaction index and a 61.41% reduction in total assigned penalty costs.

关键词： Cyber security Preventing attack management Cryptography Multilayered security (MLS) Algorithm Broker security management data segmentation Vulnerability

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segmentation of geophysical data: a big data friendly approach

Segmentation of geophysical data: a big data friendly approa...

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INNS Conference on Big data

作者： Stockwell, David R. B. Zhang, Ligang Verma, Brijesh Cent Queensland Univ Ctr Intelligent & Networked Syst Rockhampton Qld 4702 Australia

A new scalable segmentation algorithm is proposed in this paper for the forensic determination of level shifts in geophysical time series. While a number of segmentation algorithms exist, they are generally not 'big data friendly' due either to quadratic scaling of computation time in the length of the series N or subjective penalty parameters. The proposed algorithm is called SumSeg as it collects a table of potential break points via iterative ternary splits on the extreme values of the scaled partial sums of the data. It then filters the break points on their statistical significance and peak shape. Our algorithm is linear in N and logarithmic in the number of breaks B, while returning a flexible nested segmentation model that can be objectively evaluated using the area under the receiver operator curve (AUC). We demonstrate the comparative performance of SumSeg against three other algorithms. SumSeg is available as an R package from the development site at http://***/davids99us/anomaly.

关键词： data segmentation data size geophysical change points

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Unsupervised segmentation of Nonstationary data using Triplet Markov Chains 19

Unsupervised Segmentation of Nonstationary Data using Triple...

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19th International Conference on Enterprise Information Systems (ICEIS)

作者： Boudaren, Mohamed El Yazid Monfrini, Emmanuel Bey, Kadda Beghdad Habbouchi, Ahmed Pieczynski, Wojciech Ecole Mil Polytech POB 17 Algiers 16111 Algeria Univ Paris Saclay Telecom SudParis SAMOVAR CNRS F-91011 Evry France

ISBN: (纸本)9789897582479

An important issue in statistical image and signal segmentation consists in estimating the hidden variables of interest. For this purpose, various Bayesian estimation algorithms have been developed, particularly in the framework of hidden Markov chains, thanks to their efficient theory that allows one to recover the hidden variables from the observed ones even for large data. However, such models fail to handle nonstationary data in the unsupervised context. In this paper, we show how the recent triplet Markov chains, which are strictly more general models with comparable computational complexity, can be used to overcome this limit through two different ways: (i) in a Bayesian context by considering the switches of the hidden variables regime depending on an additional Markov process;and, (ii) by introducing Dempster-Shafer theory to model the lack of precision of the hidden process prior distributions. which is the origin of data nonstationarity. Furthermore, this study analyzes both approaches in order to determine which one is better-suited for nonstationary data. Experimental results are shown for sampled data and noised images.

关键词： data segmentation Hidden Markov Chains Nonstationary data Signal Processing Triplet Markov Chains

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MIXING data AUGMENTATION WITH PRESERVING FOREGROUND REGIONS IN MEDICAL IMAGE segmentation 20

MIXING DATA AUGMENTATION WITH PRESERVING FOREGROUND REGIONS ...

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20th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Liu, Xiaoqing Ono, Kenji Bise, Ryoma Kyushu Univ Grad Sch Fukuoka Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka Japan

ISBN: (纸本)9781665473583

The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also requires data augmentation to extend diversity for preventing overfitting. However, the existing methods for data augmentation of medical image segmentation are mainly based on models which need to update parameters and cost extra computing resources. We proposed data augmentation methods designed to train a high-accuracy deep learning network for medical image segmentation. The proposed data augmentation approaches are called KeepMask and KeepMix, which can create medical images by better identifying the boundary of the organ with no more parameters. Our methods achieved better performance and obtained more precise boundaries for medical image segmentation on datasets. The dice coefficient of our methods achieved 94.15% (3.04% higher than baseline) on CHAOS and 74.70% (5.25% higher than baseline) on MSD spleen with Unet.

关键词： Medical image segmentation data segmentation

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