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Evaluation of Low-cost AirQuality Sensor Calibration Models

ACM TRANSACTIONS ON SENSOR NETWORKS 2022年第4期18卷 72-72页

作者： Aula, Kasimir Lagerspetz, Eemil Nurmi, Petteri Tarkoma, Sasu Univ Helsinki PO 68Pietari Kalmin Katu 5 FI-00014 Helsinki Finland

We contribute a novel model evaluation technique that divides available measurements into training and testing sets in a way that adheres to the requirements imposed on professional monitoring stations. We perform extensive and systematic experiments with a wide range of state-of-the-art calibration models to demonstrate that our approach provides accurate insights about the performance of calibration models in real-world deployments, while at the same time highlighting issues with evaluation techniques used in previous works. Among others, our results show that although trained and tested in the same location, calibration errors can exhibit deviation up to 116% depending on the evaluation protocol that is being adopted. We also demonstrate that models trained with continuous data can suffer up to 76% greater error when tested with data coming from diverse environmental conditions. In contrast, when models are trained and tested with our method, the variability of errors is significantly reduced and the robustness of calibration models is significantly improved. The overall performance improvements depend on pollutant concentration, ranging from 10% for low concentrations to 90% for high concentrations that represent conditions that are most dangerous for human health.

关键词： Machine learning sensor calibration model evaluation data segmentation

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System identification with historical data: A multivariable approach applied to a 50MW petrochemical furnace

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COMPUTERS & CHEMICAL ENGINEERING 2022年 157卷 107623-107623页

作者： Mastrocinque Santo, Giulio Cesare Garcia, Claudio Chaves, Carlos Roberto Univ Sao Paulo Escola Politecn Sao Paulo SP Brazil Petrobras SA BR 476 Sao Mateus Do Sul PR Brazil

This paper addresses the complex problem of scanning historical data from industrial processes with the purpose of finding useful intervals for system identification. More specifically, it considers the problem in its multivariable version, for a system under closed-loop control. A detailed review of the literature on the subject is presented and some of the studied methodologies are condensed into an algorithm. The resulting algorithm is applied to seven months of historical data from a petrochemical furnace of the Brazilian company Petrobras. The objective of the algorithm is to obtain process models that represent the dynamics of the system between the set-points and the output variables with the goal of designing predictive controllers. A comparison of different techniques is presented, elucidating their impacts on the results in the furnace dataset. In addition, a viable way of choosing parameters is presented, allowing the use of the algorithm in massive data. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Automatic system identification Condition number data mining data quality data segmentation Effective rank Historical data Multivariable system identification

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Systém poloautomatizované segmentace online písma

Systém poloautomatizované segmentace online písma

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作者： Gavenčiak, Michal Brno University of Technology

Vývojová dysgrafie postihuje 10-30% dětí školního věku, přesto v České republice neexistuje objektivní způsob její diagnózy, či určení její závažnosti. Proběhl... 详细信息

Vývojová dysgrafie postihuje 10-30% dětí školního věku, přesto v České republice neexistuje objektivní způsob její diagnózy, či určení její závažnosti. Proběhlé studie ukázaly, že lze k automatické diagnóze použít digitální data získaná pomocí digitalizujícího tabletu a stylusu. V rámci probíhající studie jsou získána data online písma obsahující informace o poloze, časové informaci, náklonu, přítlaku a azimutu stylusu. Tato data však nejsou připravena na další analýzu vzhledem k obsahu nespecifikovanému počtu cvičení v souborech. V rámci této práce jsou data analyzována a je navržen a implementován program sloužící k segmentaci těchto dat do celků cvičení, umožňující vizuální kontrolu práce programu.

关键词： GUI online písmo Python segmentace dat vývojová dysgrafie data segmentation developmental dysgraphia GUI online handwriting Python Text

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Hodnocení viability kardiomyocytů

Hodnocení viability kardiomyocytů

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作者： Kremličková, Lenka Brno University of Technology

Cílem této diplomové práce je seznámení se s vlastnostmi obrazových dat a principem jejich snímání. Dále literární rešerše metod zabývajíc... 详细信息

Cílem této diplomové práce je seznámení se s vlastnostmi obrazových dat a principem jejich snímání. Dále literární rešerše metod zabývajících se segmentací obrazu v oblasti zpracování obrazů srdeční tkáně a v neposlední řadě snaha o nalezení metod pro klasifikaci mrtvých kardiomyocytů a analýzu jejich viability. Mrtvé kardiomyocyty byly analyzovány z hlediska jejich tvaru a podobnosti s templátem vytvořeným jako průměr z mrtvých buněk. Dalším přístupem byla aplikace metody založená na lokálních binárních znacích a výpočet příznaků z jednoduchého a sdruženého histogramu.

关键词： Kardiomyocyt segmentace dat templát LBP klasifikace SVM Cardiomyocytes data segmentation template local binary pattern (LBP) classification support vector machine (SVM) Text

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An efficient framework for few-shot skeleton-based temporal action segmentation

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COMPUTER VISION AND IMAGE UNDERSTANDING 2023年第1期232卷

作者： Xu, Leiyang Wang, Qiang Lin, Xiaotian Yuan, Lin Harbin Inst Technol Dept Control Sci & Engn 92 West Dazhi St Harbin 150001 Peoples R China

Temporal action segmentation (TAS) aims to classify and locate actions in the long untrimmed action sequence. With the success of deep learning, many deep models for action segmentation have emerged. However, few-shot TAS is still a challenging problem. This study proposes an efficient framework for the few-shot skeleton-based TAS, including a data augmentation method and an improved model. The data augmentation approach based on motion interpolation is presented here to solve the problem of insufficient data, and can increase the number of samples significantly by synthesizing action sequences. Besides, we concatenate a Connectionist Temporal Classification (CTC) layer with a network designed for skeleton-based TAS to obtain an optimized model. Leveraging CTC can enhance the temporal alignment between prediction and ground truth and further improve the segment-wise metrics of segmentation results. Extensive experiments on both public and self -constructed datasets, including two small-scale datasets and one large-scale dataset, show the effectiveness of two proposed methods in improving the performance of the few-shot skeleton-based TAS task.

关键词： Temporal action segmentation data segmentation Synthetic action sequences Connectionist temporal classification

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Massively-parallel column-level segmentation of depth images

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JOURNAL OF COMPUTATIONAL SCIENCE 2021年 50卷 101298-101298页

作者： Cebrian, P. Moure, J. C. Univ Autonoma Barcelona UAB Comp Architecture & Operating Syst Dept Barcelona Spain

Column-level segmentation of depth images is an energy-efficient strategy to perform 3D perception in autonomous-driving systems. These systems must perform 3D perception in real time through a pipeline of multiple tasks, which benefits from proposals that prioritize low complexity and short execution time over high levels of accuracy. For many years, column-level segmentation of depth images has been solved with the Stixels proposal, which uses an optimization algorithm with O(n(2)) computational complexity. This manuscript is an extended version of the ICCS paper "GPU-accelerated RDP Algorithm for data segmentation"(Cebrian and Moure, 2020). We present an alternative column-level segmentation proposal based on the RDP split-and-merge strategy, which has O(*** n) computational complexity. The qualitative results obtained with the KITTI and Synthia image datasets evidence that our proposal can generate depth representations with greater compression and accuracy than the Stixels proposal. More importantly, we engineered a massively parallel design optimized for the low-power, GPU-accelerated embedded systems typically used for autonomous driving applications. For the datasets above, our proposal runs on a low-power NVIDIA Volta GPU 22 to 68 times faster than Stixels GPU-accelerated code. Additionally, our code achieves higher performance speedups as the computational capabilities and size of depth images increase.

关键词： GPU acceleration data segmentation Segmented parallel reduction

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A new statistical method to segment photogrammetry data in order to obtain geological information

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2022年第0期150卷 105008-105008页

作者： Yazdanpanah, M. Xu, C. Sharifzadeh, M. Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Northern Star Resources Ltd Subiaco WA Australia

A novel method is presented that is capable of more accurately extracting rock surface features based on the geometrical characteristics of a 3D point cloud obtained from a survey. The core feature of the algorithm is a newly established method that can provide a robust estimation of point normals, while excluding statistical outliers from the calculation. A resilient multivariate mean and covariance estimator, termed Deterministic Multivariate Mean (DetMM) estimator, is used, which is capable of removing outlier effectively when calculating normals. To ensure the efficient operation of the estimator, a hybrid method is proposed where a simple and efficient method to estimate normal vectors is implemented first to filter the data before the robust DetMM method is applied for more accurate estimation of difficult cases. By using this strategy, the efficiency is significantly improved while the same level of accuracy is maintained. In addition, a novel segmentation algorithm, a region growing method, is introduced to handle complex geological features, which is capable of accurately detecting very small rock surfaces. The robustness and reliability of the developed method are compared with those of the well-known normal estimation method using principal component analysis (PCA). Finally, the method is validated on two case studies where the 3D point datasets were gathered from scanning two very different rock faces. The results have demonstrated that the proposed method outperforms significantly the conventional techniques in terms of accuracy with an acceptable increase in computation cost.

关键词： Robust estimation of normal vectors in point cloud Rock mass structures 3D point cloud data segmentation Spatial statistics Geotechnical remote sensing

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Surface Electromyography Signal Recognition Based on Deep Learning for Human-Robot Interaction and Collaboration

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2022年第2期105卷 42-42页

作者： Mendes, Nuno NOVA Univ Lisbon Res & Dev Unit Mech & Ind Engn Campus Caparica P-2829516 Lisbon Portugal

The interaction between humans and collaborative robots in performing given tasks has aroused the interest of researchers and industry for the development of gesture recognition systems. Surface electromyography (sEMG) devices are recommended to capture human hand gestures. However, this kind of technology raises significant challenges. sEMG signals are difficult to acquire and isolate reliably. The creation of a gesture representative model is hard due to the non-explicit nature of sEMG signals. Several solutions have been proposed for the recognition of sEMG-based hand gestures, but none of them are entirely satisfactory. This study contributes to take a step forward in finding the solution to this problem. A sEMG capturing prototype device was used to collect human hand gestures and a two-step algorithm is proposed to recognize five valid gestures, invalid gestures and non-gestures. The former algorithm step (segmentation) is used for sEMG signal isolation to separate signals containing gestures from signals containing non-gestures. The latter step of the algorithm (recognition) is based on a deep learning method, a convolutional neural network (CNN) that identifies which gesture is in the sEMG signals. The performances of the prototype device and recognition architecture were compared successfully with the off-the-shelf sEMG device Myo. Results indicated that the segmentation process played an important role in the success of the gesture recognition system, excluding sEMG signals containing non-gestures. The proposed system was applied successfully in the control loop of a collaborative robotic application, in which the gesture recognition system achieved an online class recognition rate (CR) of 98%, outperforming similar studies in the literature.

关键词： Pattern recognition data segmentation Deep learning Surface electromyography Robotics Industry 4 0

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APPLICATIONS OF VARIANCE FRACTAL DIMENSION: A SURVEY

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2014年第1-2期22卷 1450003-1450003页

作者： Phinyomark, Angkoon Phukpattaranont, Pornchai Limsakul, Chusak Prince Songkla Univ Fac Engn Dept Elect Engn Hat Yai 90112 Songkhla Thailand

Chaotic dynamical systems are pervasive in nature and can be shown to be deterministic through fractal analysis. There are numerous methods that can be used to estimate the fractal dimension. Among the usual fractal estimation methods, variance fractal dimension (VFD) is one of the most significant fractal analysis methods that can be implemented for real-time systems. The basic concept and theory of VFD are presented. Recent research and the development of several applications based on VFD are reviewed and explained in detail, such as biomedical signal processing and pattern recognition, speech communication, geophysical signal analysis, power systems and communication systems. The important parameters that need to be considered in computing the VFD are discussed, including the window size and the window increment of the feature, and the step size of the VFD. Directions for future research of VFD are also briefly outlined.

关键词： Biomedical Signal Processing Time Series Analysis data segmentation Feature Extraction Fractal Analysis Signal-to-Noise Ratio

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Adaptive Vibration Monitoring of Railway Track Structures Using the UWFBG by the Identification of Train-Load Patterns

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BUILDINGS 2024年第5期14卷 1239页

作者： Chen, Jiahui Li, Qiuyi Zhang, Shijie Lin, Chao Wei, Shiyin China Railway Siyuan Survey & Design Grp Co Ltd Wuhan 430063 Peoples R China Hubei Key Lab Track Secur Serv Wuhan 430063 Peoples R China Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China Minist Ind & Informat Harbin Inst Technol Key Lab Smart Prevent & Mitigat Civil Engn Disaste Harbin 150090 Peoples R China

Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected monitoring data can be huge with the great number of sensors. Even though the train-induced vibration responses of urban railway tracks constitute the most informative and crucial component, they comprised less than 7% of the total operational period. This is mainly attributed to the temporal sparsity of commuting trains. Consequently, the majority of the stored data consisted of low-informative environmental noise and interference excitation data, leading to an inefficient structural health monitoring (SHM) system. To address this issue, this paper introduced an adaptive monitoring strategy for railway track structures, which is capable of identifying train-load patterns by leveraging deep learning techniques. Inspired by image semantic segmentation, a U-net model with one-dimensional convolution layers (U-net-1D) was developed for the pointwise classification of vibration monitoring data. The proposed model was trained and validated using a dataset obtained from an actual urban railway track in China. Results indicated that the proposed method outperforms the traditional dual-threshold method, achieving an Intersection over Union (IoU) of 94.27% on the segmentation task of the test dataset.

关键词： urban railway track vibration monitoring ultra-weak fiber Bragg grating data segmentation dual-threshold method

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