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Discussion of 'Detecting possibly frequent change-points: Wild Binary segmentation 2 and steepest-drop model selection'

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2020年第4期49卷 1076-1080页

作者： Cho, Haeran Kirch, Claudia Univ Bristol Sch Math Bristol Avon England Otto von Guericke Univ Dept Math Ctr Behav Brain Sci CBBS Magdeburg Germany

We congratulate the author for this interesting paper which introduces a novel method for the data segmentation problem that works well in a classical change point setting as well as in a frequent jump situation. Most notably, the paper introduces a new model selection step based on finding the 'steepest drop to low levels' (SDLL). Since the new model selection requires a complete (or at least relatively deep) solution path ordering the change point candidates according to some measure of importance, a new recursive variant of the Wild Binary segmentation (Fryzlewicz in Ann Stat 42:2243-2281, 2014, WBS) named WBS2, has been proposed for candidate generation.

关键词： Change point detection data segmentation Multiscale procedure MOSUM procedure

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Discussion of 'Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection'

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2020年第4期49卷 1071-1075页

作者： Banerjee, Moulinath Univ Michigan Ann Arbor MI 48109 USA

We congratulate the author for this interesting paper which introduces a novel method for the data segmentation problem that works well in a classical change point setting as well as in a frequent jump situation. Most notably, the paper introduces a new model selection step based on finding the ‘steepest drop to low levels’ (SDLL). Since the new model selection requires a complete (or at least relatively deep) solution path ordering the change point candidates according to some measure of importance, a new recursive variant of the Wild Binary segmentation (Fryzlewicz in Ann Stat 42:2243–2281, 2014, WBS) named WBS2, has been proposed for candidate generation.

关键词： Change point detection data segmentation Multiscale procedure MOSUM procedure

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Geometric in-line inspection of profiled strips and welding seams

Geometric in-line inspection of profiled strips and welding ...

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Conference on Image Processing - Machine Vision Applications

作者： Reisinger, Johann Niel, Kurt S. Tratnig, Mark Vatron Gmbh A-4031 Linz Austria Fachhochschule Oberos Studien GMbH A-4600 Wels Austria

ISBN: (纸本)9780819469854

In this paper two in-line measurement systems for the geometric inspection of welded or profiled steel strips are presented. For the first case, the welded strips, the paper presents a light sectioning based measurement system for the in-line inspection of the welding seam. The system measures the vertical and angular misalignment of the welded parts in-line, right after the welding station. Based on the calculated offset of the two welded parts on one hand the welding adjustment can be optimized. On the other hand, quality relevant data is collected. The inspection of the vertical offset is done with an accuracy of +/- 5 mu m (+/- 2 sigma). In addition to the measurement of the vertical and angular offset, the system also inspects the width of the welding seam and the seam's shape and depth, all three with an accuracy of +/- 15 mu m (+/- 2 sigma). The second system presented in the paper is a measurement system for the inspection of the cross section of profiled strips. The system consists of three light sectioning based measurement heads and is mounted right after the profiling plates. Regarding the measurement precision, the shape of the profiled strip can be measured with an accuracy of better than +/- 10 mu m (+/- 2 sigma). The paper for both applications describe the sensor setup, geometric conditions, the used hardware and the calibration and registration approaches.

关键词： strip inspection welding inspection quality control light sectioning data segmentation fitting

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Randomized Approximations of Operators and their Spectral Decomposition for Diffusion Based Embeddings of Heterogeneous data 3

Randomized Approximations of Operators and their Spectral De...

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3rd International Workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)

作者： Czaja, Wojciech Hafftka, Ariel Manning, Benjamin Weinberg, Dan Univ Maryland Dept Math College Pk MD 20742 USA

ISBN: (纸本)9781479974207

Modern sensing platforms acquire heterogeneous data collected from multiple sensing modalities. We are then faced with the challenge of fusing the already collected heterogeneous data for effective use in such applications as data classification, segmentation, or target detection. data fusion is a non-trivial problem and many existing approaches are either highly dependent on the types of data or fuse the information post classification. When the data is high dimensional and possesses a large number of data points (pixels), such as is the case for Hyperspectral Satellite Imagery, it can be burdensome on even the most efficient algorithms. Previous techniques of data integration have relied harmonic analysis methods to fuse data and produce low dimensional representations of large heterogeneous datasets. These techniques construct data-driven operators whose spectral decomposition creates a representation of the data. The bottlenecks of this approach are in the construction of the operator and its spectral decomposition, which may require a significant computational cost. Thus, we introduce randomized approximations at the most computationally intensive steps to significantly reduce the computational cost with minimal loss of fidelity, thus enabling the practical use of these highly sophisticated and geometrically reliable methods.

关键词： object detection sensor fusion computational cost data classification data fusion data segmentation diffusion based embedding harmonic analysis heterogeneous data hyperspectral satellite imagery sensing platforms target detection Approximation algorithms Approximation methods data integration Eigenvalues and eigenfunctions Laplace equations Laser radar Matrix decomposition

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Integrating Temporal Context into Streaming data for Human Activity Recognition in Smart Home 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Vicini, Marina Rudorfer, Martin Dai, Zhuangzhuang Manso, Luis J. Aston Univ Aston St Birmingham B4 7ET England Legrand Care Innovat HouseNorthumberland Business Pk5 Silver Cramlington NE23 7RY England

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

With the global population ageing, it is crucial to enable individuals to live independently and safely in their homes. Using ubiquitous sensors such as Passive InfraRed sensors (PIR) and door sensors is drawing increasing interest for monitoring daily activities and facilitating preventative healthcare interventions for the elderly. Human Activity Recognition (HAR) from passive sensors mostly relies on traditional machine learning and includes data segmentation, feature extraction, and classification. While techniques like Sensor Weighting Mutual Information (SWMI) capture spatial context in a feature vector, effectively leveraging temporal information remains a challenge. We tackle this by clustering activities into morning, afternoon, and night, and encoding them into the feature weighting method calculating distinct mutual information matrices. We further propose to extend the feature vector by incorporating time of day and day of week as cyclical temporal features, as well as adding a feature to track the user's location. The experiments show improved accuracy and F1-score over existing state-of-the-art methods in three out of four real-world datasets, with highest gains in a low-data regime. These results highlight the potential of our approach for developing effective smart home solutions to support ageing in place.

关键词： Activity Recognition data segmentation Temporal Context Mutual Information Sliding Window Smart Home

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DYNAMIC FACTOR GRAPHS: A NOVEL FRAMEWORK FOR MULTIPLE FEATURES data FUSION

DYNAMIC FACTOR GRAPHS: A NOVEL FRAMEWORK FOR MULTIPLE FEATUR...

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2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Kampa, Kittipat Principe, Jose C. Slatton, K. Clint Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)9781424442966

The Dynamic Tree [1] (DT) Bayesian Network is a powerful analytical tool for image segmentation and object segmentation tasks. Its hierarchical nature makes it possible to analyze and incorporate information from different scales, which is desirable in many applications. Having a flexible structure enables model selection, concurrent with parameter inference. In this paper, we propose a novel framework, dynamic factor graphs (DFG), where data segmentation and fusion tasks are combined in the same framework. Factor graphs (FGs) enable us to have a broader range of modeling applications than Bayesian networks (BNs) since FGs include both directed acyclic and undirected graphs in the same setting. The example in this paper will focus on segmentation and fusion of 2D image features with a linear Gaussian model assumption.

关键词： dynamic factor graphs sum-product algorithm linear Gaussian models data fusion data segmentation

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Optimal Length-Constrained segmentation and Subject-Adaptive Learning for Real-time Arrhythmia Detection 3

Optimal Length-Constrained Segmentation and Subject-Adaptive...

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3rd IEEE/ACM International Conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Kha Vo Dutkiewicz, Eryk Univ Technol Sydney Global Big Data Technol Ctr Sch Elect & Data Engn Sydney NSW Australia

ISBN: (纸本)9781538672068

An algorithm of data segmentation with length constraints for each segment is presented and applied in the context of arrhythmia detection. The additivity property of the cost function for each segment yields the induction proof of the exact global optimal solution. The experiments were conducted on the MIT-BIH arrhythmia dataset with the heartbeat categories recommended by the ANSI/AAMI EC57: 1998 standard. The heartbeat classification task is enhanced by an adaptive learning scheme. Incremental support vector machine is used to integrate a small number of expert-annotated samples specific to the subject into the existing classifier previously learned from the dataset. The proposed segmentation scheme obtains the sensitivity of 99.89% and the positive predictivity of 99.83%. The classification sensitivities of ventricular and supraventricular detection are significantly boosted from 85.9% and 83.5% (subject-unadaptive) to 97.7% and 93.2% (subject-adaptive), respectively. Similarly the predictivities increase from 94.8% to 99.3% (ventricular), and from 67.7% to 88.0% (supraventricular) when plugging in the adaptive learning method. The signal processing framework is conducted in a simulated real-time model. As compared to the previously reported studies we achieve a competitive performance in terms of all assessment measures.

关键词： data segmentation heartbeat classification adaptive learning arrhythmia detection

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A hand gesture recognition using single-channel electrodes based on artificial neural network 8

A hand gesture recognition using single-channel electrodes b...

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IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Sheng, Ranran Zhang, Zhen Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

ISBN: (纸本)9781538681787

A hand gesture recognition using surface electromyography signals can be applied in several scenarios such as prosthetic limb control, human computer interface and so on. In this paper, we propose a hand gesture recognition method using single-channel electrodes to classify 5 hand gestures: Pinch, Fingers spread, Fist, Wave in, and Wave out. The proposed method is consisted of 4 steps: preprocessing, data segmentation, feature extraction and classification. At preprocessing step, a muscle activity detection algorithm is applied to remove rest parts of the dataset. At data segmentation step, an overlapped windowing algorithm is applied to segment the main window for acquiring sub-windows. At feature extraction step, one part of features is observed from above mentioned sub-windows. And time domain features of the raw surface electromyography signals are extracted as another part. Finally for classification, an artificial neural network is modelled to label sub-window observation. Experiment shows the proposed method has an accuracy of 91.3%.

关键词： Gesture recognition artificial neural network hand gesture single-channel electrodes data segmentation

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Solid model reconstruction from triangular meshes

Solid model reconstruction from triangular meshes

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2nd International Conference on Digital Image Processing

作者： Chung, Tien-Tung Chuang, Li-Chang Lee, Jhe-Wei Hsu, Shun-Hsiung Natl Taiwan Univ Dept Mech Engn Taipei 10764 Taiwan Investigat Lab Aviat Safety Council Taipei Taiwan

ISBN: (纸本)9780819479426

This paper presents an approach to reconstruct solid models from triangular meshes of STL files. First, suitable slicing planes should be selected for extracting parallel intersection contours, which will be used for solid model reconstruction. Usually, a suitable flat region of triangular meshes of the STL model is selected as the bottom surface, and it can be fitted into a plane from the selected flat region. The flat region is separated by a mesh segmentation method, which uses a specified small threshold dihedral angle to divide all triangular facets into separated regions. Next, a series of parallel slicing contours are obtained by cutting the STL model through specified parallel cutting planes. Slicing contours are originally composed of a lot of line segments, which should be simplified and refitted into 2D NURBS curves for data reduction and contour smoothing. The number of points on each slicing contour is reduced by comparing the variation of included angles of each two adjacent line segments. Reduced points of each slicing contour are fitted into a NURBS curve in commercial CAD software. Finally, with a series of parallel 2D NURBS curves, the solid model of the STL facets is established by loft operations supplied in almost all popular CAD software. The established solid model can be used for other post processing such as finite element mesh generation.

关键词： Solid model reconstruction STL facet data segmentation surface fitting NURBS curve

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Window-size impact on detection rate of wearable-sensor-based fall detection using supervised machine learning 1

Window-size impact on detection rate of wearable-sensor-base...

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IEEE Life Sciences Conference (LSC)

作者： Putra, I. Putu Edy Suardiyana Vesilo, Rein Coventry Univ Fac Engn Environm & Comp Coventry CV1 5FB W Midlands England Macquarie Univ Sch Engn Sydney NSW 2109 Australia

ISBN: (纸本)9781538610305

For a supervised machine-learning based fall-detection approach, data segmentation is needed to split the data sequence into several segments. Then features are extracted from each segment, where those features are used to train and test the classifier using supervised machine-learning algorithms. A sliding window is normally used to segment a data sequence. Although the sliding window takes an important role in the training and testing processes, most existing studies in fall detection rely on the figures from previous studies, without any studies to support them. This study investigates two types of the sliding window that are usually used by existing studies: Fixed-size Non-overlapping Sliding Window (FNSW) and Fixed-size Overlapping Sliding Window (FOSW). Two publicly-accessible datasets are used in this study. Two machine-learning algorithms, Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN), are used to train and test the classifier. The experiments show that using a window size of 2 seconds for FNSW is recommended to achieve a better F-score, where using a 25% window overlap for FOSW is recommended to get a better precision. Overall, FNSW-based machine learning approaches can achieve up to 95.1% of F-score on average, while FOSW-based machine learning approaches can achieve up to 93.3% of F-score on average.

关键词： data segmentation fall detection supervised machine learning sliding window wearable sensor

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