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6th International Conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Sadri, Alireza Tennakoon, Ruwan Hoseinnezhad, Reza Bab-Hadiashar, Alireza RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (纸本)9781467389105

This paper approaches the problem of geometric multi-model fitting as a data segmentation problem which is solved by a sequence of sampling, model selection and clustering steps. We propose a sampling method that significantly facilitates solving the segmentation problem using the Normalized cut. The sampler is a novel application of Markov-Chain-Monte-Carlo (MCMC) method to sample from a distribution in the parameter space that is obtained by modifying the Least kth Order Statistics cost function. To sample from this distribution effectively, our proposed Markov Chain includes novel long and short jumps to ensure exploration and exploitation of all structures. It also includes fast local optimization steps to target all, even fairly small, putative structures. This leads to a clustering solution through which final model parameters for each segment are obtained. The method competes favorably with the state-of-the-art both in terms of computation power and segmentation accuracy.

关键词： Robust multi-model fitting data segmentation Normalized cut MCMC

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Measuring power systems load conditions through data segmentation

Measuring power systems load conditions through data segment...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： D'Apuzzo, Massimo D'Arco, Mauro Ianniello, Giacomo Liccardo, Annalisa Univ Naples Federico II Dept Elect Engn I-80125 Naples 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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Bidirectional Removal of Reverse Gravitational Acceleration Based on data segmentation 12

Bidirectional Removal of Reverse Gravitational Acceleration ...

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IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green Computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of Things (iThings) / 5th IEEE Int Conf on Smart data

作者： Li Xing Hou Zhenjie Liang Jiuzhen Chang Xingzhi Changzhou Univ Coll Informat Sci & Engn Changzhou 213164 Peoples R China Jiangsu Prov Networking & Mobile Internet Technol Huaian 223003 Peoples R China Changzhou Coll Informat Technol Changzhou Key Lab Large Plast Parts Intelligence Changzhou 213164 Peoples R China

ISBN: (纸本)9781728129808

In this paper, we propose a method called bidirectional removal of the reverse gravitational acceleration algorithm based on data segmentation. The stationary points are used as the endpoint to segment the motion data to avoid inter-segmentation accumulation of error in the angular velocity integration process. Then the bidirectional angle method is presented to remove the reverse gravitational acceleration in each data segment. Our method is able to improve the accuracy of the linear acceleration. The results indicate that our method outperforms the exiting methods.

关键词： reverse gravitational acceleration accumulation error stationary points data segmentation

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A New Method of Vehicle Target Radar data segmentation Based on DBSCAN and Lane Recognition

A New Method of Vehicle Target Radar Data Segmentation Based...

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2021 CIE International Conference on Radar, Radar 2021

作者： Jiao, Jian Li, Gang Wang, Jianwen Zhao, Zhichun Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9781665498142

Since the raw radar data is always seriously mixed with echoes from multiple targets in a complex traffic environment, it is extremely difficult to segment the target data for feature extraction. In this paper, a new method of vehicle target radar data segmentation based on Density Based Spatial Clustering of Applications with Noise (DBSCAN) and lane recognition is proposed. After signal preprocessing, the vehicle features with lane label are used to train lane recognition model. Then, the coarse segmentation can be obtained by DBSCAN. Finally, the coarse segmentation results are further finely segmented through the above trained lane recognition model. By real-data comparative experiment based on a millimeter-wave (MMW) radar, the effectiveness and superiority of the proposed method for vehicle targets data segmentation in complex traffic environment over the DBSCAN are verified. © 2021 IEEE.

关键词： data segmentation DBSCAN lane recognition millimeter-wave radar

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data segmentation technique for triangular meshes in reverse engineering

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Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao 2007年第3期28卷 289-300页

作者： Lai, Jiing-Yih Tsai, Yao-Chen Department of Mechanical Engineering National Central University Jhong-Li 320 Taiwan

In reverse engineering it is necessary to reconstruct a surface that can be trimmed with other surfaces. The cloud points should be partitioned into appropriate feature regions so that each of them can be fitted into a surface. We proposed a data segmentation algorithm for the partition of the triangular meshes. It was composed of the following four steps: computing characteristic values, feature regions classification, merging unnecessary regions and partitioning meshes. An integrated procedure incorporating the above steps and appropriate user interface was proposed for realistic implementation. Successful examples were provided also to demonstrate the feasibility of the proposed method.

关键词： data segmentation Point curvature Reverse engineering

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Machine Learning and data segmentation for Building Energy Use Prediction-A Comparative Study

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ENERGIES 2021年第18期14卷 5947-5947页

作者： Mounter, William Ogwumike, Chris Dawood, Huda Dawood, Nashwan Teesside Univ Sch Comp Engn & Digital Technol SCEDT Middlesbrough TS1 3BA Cleveland England

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building's long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively.

关键词： buildings data segmentation energy prediction polynomial regression support vector regression artificial neural networks

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Enhanced seismic data segmentation using an assembled scSE-Res-UNet deep neural network

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 231卷

作者： Wang, Zhiguo Wang, Qiannan Zhang, Yijie Chen, Yumin Zhang, Xiaoyun Xi An Jiao Tong Univ Sch Math & Stat Xian Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian Peoples R China

To enhance the accuracy and robustness of seismic data segmentation, we propose a novel deep-learning network, named scSE-Res-UNet. We select a U-Net as the backbone and augment it with residual blocks to form a Res-UNet, enabling multi-scale feature extraction while reducing edge loss between strata. Furthermore, we introduce Spatial and Channel Squeeze & Excitation (scSE) attention blocks in the decoder to selectively emphasize meaningful features and long-range dependencies in the data. The scSE blocks suppress unimportant features and noise while enhancing spatial context. We apply the proposed scSE-Res-UNet to a public F3 seismic dataset and compare performance against a CNN benchmark model. Experiments demonstrate the proposed architecture achieves superior data segmentation accuracy and robustness. The integration of residual learning and scSE attention mechanisms enhances the network's capacity to extract geologically meaningful representations from the seismic data. This leads to improved delineation of stratigraphic units with similar characters that are challenging to differentiate. The proposed scSE-Res-UNet effectively combines proven deep learning techniques to advance seismic interpretation.

关键词： Seismic interpretation data segmentation U-net Residual block Spatial and channel squeeze & excitation block

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Pilot evaluation of sensitive data segmentation technology for privacy

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2020年 138卷 104121-104121页

作者： Grando, Adela Sottara, Davide Singh, Ripudaman Murcko, Anita Soni, Hiral Tang, Tianyu Idouraine, Nassim Todd, Michael Mote, Mike Chern, Darwyn Dye, Christy Whitfield, Mary Jo Arizona State Univ Coll Hlth Solut Biomed Informat Scottsdale AZ 85259 USA Mayo Clin Rochester MN USA Sch Comp Informat & Decis Syst Engn Tempe AZ USA Univ Arizona Coll Med Tucson AZ USA Arizona State Univ Coll Nursing & Hlth Innovat Phoenix AZ USA Hlth Current Phoenix AZ USA Partners Recovery Phoenix AZ USA Jewish Family & Childrens Serv Phoenix AZ USA

Background: Consent2Share (C2S) is an open source software created by the Office of the National Coordinator data segmentation for Privacy initiative to support electronic health record (EHR) granular segmentation. To date, there are no published formal evaluations of Consent2Share. Method: Structured data (e.g. medications) codified using standard clinical terminologies (e.g. RxNorm) was extracted from the EHR of 36 patients with behavioral health conditions from study sites. EHRs were available through a health information exchange and two sites. The EHR data was already classified into data types (e.g. procedures and services). Both Consent2Share and health providers classified EHR data based on value sets (e.g. mental health) and sensitivity (e.g. not sensitive. Descriptive statistics and Chi-square analysis were used to compare differences between data categorizations. Results: From the resulting 1,080 medical records items, 584 were distinct. Significant differences were found between sensitivity classifications by Consent2Share and providers (chi 2(2, N = 584) = 114.74, p = < 0.0001). Sensitivity comparisons led to 56.0 % of agreements, 31.2 % disagreements, and 12.8 % partial agreements. Most (97.8 %) disagreements resulted from information classified as not sensitive by Consent2Share, but sensitive by provider (e.g. behavioral health prevention education service). In terms of data types, most disagreements (57.1 %) focused on procedures and services information (e.g. ligation of fallopian tube). When considering value sets, most disagreements focused on genetic data (100.0 %), followed by sexual and reproductive health (88.9 %). Conclusions: There is a need to further validate Consent2Share before broad use in health care settings. The outcomes from this pilot study will help guide improvements in segmentation logic of tools like Consent2Share and may set the stage for a new generation of personalized consent engines.

关键词： data segmentation data privacy Electronic medical records

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Research on the data segmentation Technique of Large Rapid Prototyping Sample Based on Features

Research on the Data Segmentation Technique of Large Rapid P...

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2012 International Conference on Mechanical Materials and Manufacturing Engineering(ICMMME 2012)

作者： Xin Liu Wei Fan School of Numeral Control Engineering Zhejiang Industry Polytechnic College

Because of the small workbench molding size of rapid prototyping equipment, the processing of large rapid prototyping samples is a problem during the new product development process, for example, motorcycle covering. The relative merits of accuracy engraving technique and rapid prototyping technique during processing are discussed. The method combining accuracy engraving machine and rapid prototyping machine to processing new motorcycle cover samples is proposed. And the surface data segmentation technique based on features is adopt to divide the large rapid prototyping sample reasonably, and then the small parts are collaged after respectively processing, so the problem of large rapid prototyping sample cannot once molding is solved, the speed of new product development is accelerated, the cost of new product development is decreased, the rapid manufacturing is realized. This method has been applied to the processing of new motorcycle cover samples and the application method is expounded.

关键词： Accuracy engraving technique Rapid prototyping data segmentation New product development

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Research on Search Method Based on data segmentation of Related Attributes

Research on Search Method Based on Data Segmentation of Rela...

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2018 International Conference on Computer,Communication and Network Technology(CCNT 2018)

作者： Zhong-wen QIAN Jian-son ZHANG Xiang WU Xiao-ming JU StateGrid Zhejiang Electric Power Company Information and Communication Branch East China Normal University School Computer Science and Software Engineering

Aiming at the problem of safe protection and effective searching of texts in cloud environment, as well as improving the validity of cloud storage search, this paper proposes a text search method based on data segmentation of text attribute relationship. The index construction of the split text search method is carried out by firstly using the improved Apriori algorithm to obtain the associated word set of the search text to be uploaded, and then utilizing the associated word sets to split the text. Combining the text associated attribute word set and the text word vector space model, the keyword sets which are required for constructing the text index is selected. Experimental analysis shows that the search method constructed in this paper can achieve effective text search and restoration, and is suitable for text storage and search in complex cloud environments.

关键词： data segmentation Apriori Word vector Index

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