检索结果-内蒙古大学图书馆

Human Locomotion Monitoring in Space Flight: Retrospective Nonparametric Changepoint Detection Methods

MICROGRAVITY SCIENCE AND TECHNOLOGY 2025年第2期37卷 1-27页

作者： Shestoperov, A. I. Ivchenko, A. V. Fomina, E. V. Russian Acad Sci Keldysh Inst Appl Math Miusskaya Sq 4 Moscow 125047 Russia Russian Acad Sci Inst Biomed Problems State Sci Ctr Russian Federat Khoroshevskoe ShKhoroshevskoe ShKhoroshevskoe ShKh Moscow 123007 Russia

The paper is dedicated to the analysis of medico-biological data obtained during locomotor testing of astronauts. Accurate data interpretation plays a crucial role in locomotion system monitoring, prophylaxis of long-duration spaceflight negative effects and thus in the development of an autonomous medical support system for deep space expeditions. During the locomotor testing the astronaut changes motion modes in accordance with the prescribed training protocol while running on the treadmill, and data such as speed, support pressure, heart rate frequency, etc., are collected simultaneously. The astronaut may follow either an individual protocol developed by specialists or perform his personal protocol at every fourth day of the micro cycle. Our task is to identify unknown motion modes by the means of a posteriori time series segmentation and, specifically, in the presence of various transitional processes as well as signal loss periods. The presence of tricky profiles does not allow for preliminary hypotheses about the distribution pattern of the dataset under study. The article consists of two parts. Firstly, it provides a detailed overview of several modern retrospective (offline) nonparametric multiple changepoint detection methods in multidimensional time series. A change point means an abrupt change in the probability properties of the observed series occurring at an unknown time instant. When describing the algorithms, emphasis is placed on statistics as a measure of data homogeneity, numerical methods for solving optimization problems, and model selection methods. Secondly, the real speed profiles resulting from locomotor testing have been handled through the mentioned algorithms. The validation was performed on three characteristic experimental data samples, allowing for an assessment of the prospects of applying the described methods to the entire dataset.

关键词： Aerospace medicine Locomotion test Time series Changepoint segmentation Nonparametric Retrospective binary segmentation Dynamic programming Sliding window

来源：评论

学校读者我要写书评

暂无评论

Dual region mutual enhancement network for camouflaged object detection

引用

IMAGE AND VISION COMPUTING 2025年 158卷

作者： Yin, Chao Li, Xiaoqiang Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

Camouflaged Object Detection (COD) is a promising yet challenging task that aims to segment objects hidden in intricate surroundings. Current methods often struggle with identifying background regions that resemble camouflaged objects, posing a significant challenge. To mitigate this issue, we propose a novel Dual Region Mutual Enhancement Network (DRMENet), which separately extracts camouflaged object and background region features and these branches mutually assist each other to refine their respective region features. Specifically, in the foreground segmentation branch, we utilize the Background-assisted Foreground Region Enhancement (BFRE) subnetwork to enhance camouflaged object region features with background information. BFRE subnetwork consists of two parts: the Background-subtracted Foreground Refinement (BFR) module and the Scale-wise Feature Capturing (SFC) module, where the former obtains corresponding camouflaged object region features through cross-layer refinement with the assistance of background region features, and the latter captures scale-wise features and outputs a side output for region prediction result. Additionally, considering the noise present in low-level visual features, we introduce the Semantic-Guided Refinement (SGR) module, which progressively refines visual features based on enhanced semantic features. Experiments on challenging datasets show DRMENet's superiority over the existing state-of-the-art methods. The source codes will be available at https://***/ycyinchao/DRMENet.

关键词： Camouflaged object detection binary segmentation Feature interaction Feature enhancement Coarse-to-fine refinement

来源：评论

学校读者我要写书评

暂无评论

Improving power by conditioning on less in post-selection inference for changepoints

引用

STATISTICS AND COMPUTING 2025年第1期35卷 1-23页

作者： Carrington, Rachel Fearnhead, Paul Univ Lancaster Sch Math Sci Lancaster England

Post-selection inference has recently been proposed as a way of quantifying uncertainty about detected changepoints. The idea is to run a changepoint detection algorithm, and then re-use the same data to perform a test for a change near each of the detected changes. By defining the p-value for the test appropriately, so that it is conditional on the information used to choose the test, this approach will produce valid p-values. We show how to improve the power of these procedures by conditioning on less information. This gives rise to an ideal post-selection p-value that is intractable but can be approximated by Monte Carlo. We show that for any Monte Carlo sample size, this procedure produces valid p-values, and empirically that noticeable increase in power is possible with only very modest Monte Carlo sample sizes. Our procedure is easy to implement given existing post-selection inference methods, as we just need to generate perturbations of the data set and re-apply the post-selection method to each of these. On genomic data consisting of human GC content, our procedure increases the number of significant changepoints that are detected when compared to the method of Jewell et al. (J R Stat Soc Ser B 84(4):1082-1104, 2022).

关键词： binary segmentation Breakpoint Fused lasso Penalised likelihood Post-selection p-value

来源：评论

学校读者我要写书评

暂无评论

Robust change point detection for high-dimensional linear models with tolerance for outliers and heavy tails

引用

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2025年第1期53卷

作者： Yang, Zhi Zhang, Liwen Sun, Siyu Liu, Bin Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Shanghai Univ Finance & Econ Zhejiang Coll Dept Stat Jinhua Peoples R China Fudan Univ Sch Management Shanghai Peoples R China

This article focuses on detecting change points in high-dimensional linear regression models with piecewise constant regression coefficients, moving beyond the conventional reliance on strict Gaussian or sub-Gaussian noise assumptions. In the face of real-world complexities, where noise often deviates into uncertain or heavy-tailed distributions, we propose two tailored algorithms: a dynamic programming algorithm (DPA) for improved localization accuracy, and a binary segmentation algorithm (BSA) optimized for computational efficiency. These solutions are designed to be flexible, catering to increasing sample sizes and data dimensions, and offer a robust estimation of change points without requiring specific moments of the noise distribution. The efficacy of DPA and BSA is thoroughly evaluated through extensive simulation studies and application to real datasets, showing their competitive edge in adaptability and performance. Ce travail porte sur la d & eacute;tection de points de changement dans les mod & egrave;les de r & eacute;gression lin & eacute;aire de grande dimension o & ugrave;les coefficients de r & eacute;gression sont constants par morceaux. Les auteurs vont au-del & agrave;de l'hypoth & egrave;se conventionnelle de bruit gaussien ou sous-gaussien strict. Dans le monde r & eacute;el, le bruit s'& eacute;carte souvent vers des distributions incertaines ou & agrave;queues lourdes. Pour faire face & agrave;ces complexit & eacute;s, les auteurs proposent deux algorithmes sur mesure. Le premier est un algorithme de programmation dynamique (DPA) visant une meilleure pr & eacute;cision de localisation. Le second est un algorithme de segmentation binaire (BSA) optimis & eacute;pour l'efficacit & eacute;computationnelle. Ces solutions sont con & ccedil;ues pour & ecirc;tre flexibles, s'adaptant & agrave;des tailles d'& eacute;chantillons et des dimensions de donn & eacute;es croissantes. Elles permettent & eacute;galement une estimation robuste des points de cha

关键词： High dimensions change point detection linear model heavy tail dynamic programming binary segmentation

来源：评论

学校读者我要写书评

暂无评论

Extracting road maps from high-resolution satellite imagery using refined DSE-LinkNet

引用

CONNECTION SCIENCE 2021年第2期33卷 278-295页

作者： Das, Prativa Chand, Satish Jawaharlal Nehru Univ Sch Comp & Syst Sci Delhi India

Road detection and extraction have gained momentum in recent past years with crucial applications such as urban planning, autonomous driving, automated map update, providing aid to rescue missions, etc. The current methodologies generate the disconnected road segments, cause boundary loss, and also they are incapable of handling the imbalanced class distribution problems. In this paper, we propose a fully convolutional architecture, named as refined DSE-LinkNet, to extract the connected and precise road maps. We use a pre-trained encoder by combining the layers of the two very efficient and light-weight CNN models: DenseNet and SE-Net that makes the proposed model more expressive with faster convergence. We introduce a new module, Fusion block, in our architecture that enhances its precise localisation as well as classification ability by capturing multilevel as well as multiscale features. To address the imbalanced class distribution problem, a new aggregate loss function is proposed by integrating binary cross-entropy, Jaccard coefficient, and Lovasz sigmoid loss functions. The experiments are performed on a publicly available dataset, DeepGlobe Road Extraction Challenge 2018, to show its efficacy over the D-LinkNet, winner of DeepGlobe Challenge 2018, by achieving IoU of 0.69 with lesser number of parameters and better computational complexity.

关键词： CNNs road map extraction satellite imagery binary segmentation multiscale and multilevel features deep learning

来源：评论

学校读者我要写书评

暂无评论

Detecting change-points in multidimensional stochastic processes

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2006年第3期51卷 1892-1903页

作者： De Gooijer, Jan G. Univ Amsterdam Dept Quantitat Econ NL-1018 WB Amsterdam Netherlands

A general test statistic for detecting change-points in multidimensional stochastic processes with unknown parameters is proposed. The test statistic is specialized to the case of detecting changes in sequences of covariance matrices. Large-sample distributional results are presented for the test statistic under the null hypothesis of no-change. The finite-sample properties of the test statistic are compared with two other test statistics proposed in the literature. Using a binary segmentation procedure, the potential of the various test statistics is investigated in a multidimensional setting both via simulations and the analysis of a real life example. In general, all test statistics become more effective as the dimension increases, avoiding the determination of too many "incorrect" change-point locations in a one-dimensional setting. (c) 2005 Published by Elsevier B.V.

关键词： binary segmentation Cramer-von Mises covariance matrices cumulative sum likelihood ratio

来源：评论

学校读者我要写书评

暂无评论

Transfer learning for surgical instrument segmentation in open surgery videos: a modified u-net approach with channel amplification

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2024年第11期18卷 8061-8076页

作者： Bakiya, K. Savarimuthu, Nickolas Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamilnadu India

Minimally invasive surgeries reduce blood loss and quicker recovery times for patients compared to open surgeries. Equipped with high-definition 3D cameras, robotic surgical systems offer an enhanced visual perspective, empowering surgeons to make informed decisions and reduce damage to healthy tissues around the surgical site. At this juncture, emphasizing the importance of precise localization of surgical instruments in robotic-assisted surgeries is of utmost significance. The primary intention of this paper is to address the challenge of achieving accurate localization of surgical tools within registered tissue to establish clear marginal boundaries and prevent tissue tearing. So, surgeons can reduce the risk of human error with extremely precise and steady movements. In this paper, we proposed a binary segmentation model complemented with a customized U-Net architecture to ensure precise inference in surgical instrument segmentation. Additionally, the accuracy was enhanced through the incorporation of Red channel amplification and the Otsu thresholding approach. The effectiveness of our proposed Modified U-Net is validated through experiments conducted on both the publicly available surgical instrument segmentation dataset, MICCAI 2017 EndoVis Challenge (EndoVis2017), and Neuro surgical tools (NST) dataset consisting of both brain and spine tumor removal procedures. The results presented compelling evidence of the superior performance attained through the integration of the modified U-Net with Red channel amplification and Otsu thresholding with remarkable outcomes including 99.5% accuracy and a Dice score of 0.9846 in binary segmentation, 99.41% accuracy, and a Dice score of 0.9751 in part segmentation, and 99.35% accuracy and a Dice score of 0.9726 in type segmentation. We presented both quantitative and qualitative inference results for NST dataset samples. These results were obtained by leveraging a transfer learning approach, where the model was trained on

关键词： Surgical instrument segmentation binary segmentation Part segmentation Type segmentation Open surgery dataset Transfer learning Red channel amplification Otsu thresholding

来源：评论

学校读者我要写书评

暂无评论

Robust and automatic data cleansing method for short-term load forecasting of distribution feeders

引用

APPLIED ENERGY 2020年第0期261卷 114405-000页

作者： Huyghues-Beaufond, Nathalie Tindemans, Simon Falugi, Paola Sun, Mingyang Strbac, Goran Imperial Coll London Dept Elect & Elect Engn South Kensington Campus London SW7 2AZ England Delft Univ Technol Dept Elect Sustainable Energy Mekelweg 4 NL-2628 CD Delft Netherlands Zhejiang Univ Coll Control Sci & Engn Hangzhou 310058 Peoples R China

Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed;this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.

关键词： Distribution systems Outlier detection binary segmentation Kalman smoothing Multi-step forecasts

来源：评论

学校读者我要写书评

暂无评论

binary trees for dissimilarity data

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2010年第6期54卷 1516-1524页

作者： Piccarreta, Raffaella Bocconi Univ Dept Decis Sci I-20136 Milan Italy

binary segmentation procedures (in particular, classification and regression trees) are extended to study the relation between dissimilarity data and a set of explanatory variables. The proposed split criterion is very flexible, and can be applied to a wide range of data (e.g., mixed types of multiple responses, longitudinal data, sequence data). Also, it can be shown to be an extension of well-established criteria introduced in the literature on binary trees. (C) 2010 Elsevier B.V. All rights reserved.

来源：评论

学校读者我要写书评

暂无评论

Multiple break detection in the correlation structure of random variables

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2014年 76卷 262-282页

作者： Galeano, Pedro Wied, Dominik Univ Carlos III Madrid Dept Estadist E-28903 Madrid Spain TU Dortmund Fak Stat D-44221 Dortmund Germany

Correlations between random variables play an important role in applications, e.g. in financial analysis. More precisely, accurate estimates of the correlation between financial returns are crucial in portfolio management. In particular, in periods of financial crisis, extreme movements in asset prices are found to be more highly correlated than small movements. It is precisely under these conditions that investors are extremely concerned about changes on correlations. A binary segmentation procedure to detect the number and position of multiple change points in the correlation structure of random variables is proposed. The procedure assumes that expectations and variances are constant and that there are sudden shifts in the correlations. It is shown analytically that the proposed algorithm asymptotically gives the correct number of change points and the change points are consistently estimated. It is also shown by simulation studies and by an empirical application that the algorithm yields reasonable results. (C) 2013 Elsevier B.V. All rights reserved.

关键词： binary segmentation Correlations CUSUM statistics Financial returns Multiple change point detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：