TSFEL: Time Series Feature Extraction Library

Barandas, Marília; Folgado, Duarte; Fernandes, Letícia; Santos, Sara; Abreu, Mariana; Bota, Patrícia J.; Liu, Hui; Schultz, Tanja; Gamboa, Hugo

doi:10.1016/j.softx.2020.100456

Cited by 342 publications

(168 citation statements)

References 10 publications

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“…However, the raw data representing the charge level of the capacitor, measured every 250 ms, are not suitable as input data for the model. Therefore, seven common statistical measures [ 32 ] have been calculated from the distribution of the capacitor charge level as provided in Table 6 :…”

Section: Resultsmentioning

confidence: 99%

“…This characteristic indicates the degree of similarity between values of the same variables over two time intervals. This concept has been used for defining the attribute ACorr , which refers to the average autocorrelation value calculated between two measures of the capacitor charge level at times

and

[ 32 ]:

where value

—is the time interval (the lag), which represents autocorrelation between values that are one time interval apart.…”

Section: Resultsmentioning

confidence: 99%

“…Another popular time series feature is “peak-to-peak” which calculates the distance between two peaks: lowest and highest [ 32 ]:

…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

Ostaševičius

Karpavicius

Paulauskaitė-Tarasevičienė

et al. 2021

Sensors

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There are many tool condition monitoring solutions that use a variety of sensors. This paper presents a self-powering wireless sensor node for shank-type rotating tools and a method for real-time end mill wear monitoring. The novelty of the developed and patented sensor node is that the longitudinal oscillations, which directly affect the intensity of the energy harvesting, are significantly intensified due to the helical grooves cut onto the conical surface of the tool holder horn. A wireless transmission of electrical impulses from the capacitor is proposed, where the collected electrical energy is charged and discharged when a defined potential is reached. The frequency of the discharge pulses is directly proportional to the wear level of the tool and, at the same time, to the surface roughness of the workpiece. By employing these measures, we investigate the support vector machine (SVM) approach for wear level prediction.

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Section: Resultsmentioning

confidence: 99%

and

[ 32 ]:

where value

—is the time interval (the lag), which represents autocorrelation between values that are one time interval apart.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

Ostaševičius

Karpavicius

Paulauskaitė-Tarasevičienė

et al. 2021

Sensors

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“…With data collected from one subject (single ear), we run the developed sensing pipelines for each application to compare the final sensing accuracy (recall), as presented in Table 1. For accelerometer data, we extract around 130 statistical and spectral features using the TSFEL Python library [28]. Logistic regression is used as the classifier.…”

Section: Baselines Benchmarkingmentioning

confidence: 99%

OESense

Ferlini

Mascolo

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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Smart earbuds are recognized as a new wearable platform for personal-scale human motion sensing. However, due to the interference from head movement or background noise, commonly-used modalities (e.g. accelerometer and microphone) fail to reliably detect both intense and light motions. To obviate this, we propose OESense, an acoustic-based in-ear system for general human motion sensing. The core idea behind OESense is the joint use of the occlusion effect (i.e., the enhancement of low-frequency components of bone-conducted sounds in an occluded ear canal) and inward-facing microphone, which naturally boosts the sensing signal and suppresses external interference. We prototype OESense as an earbud and evaluate its performance on three representative applications, i.e., step counting, activity recognition, and hand-to-face gesture interaction. With data collected from 31 subjects, we show that OESense achieves 99.3% step counting recall, 98.3% recognition recall for 5 activities, and 97.0% recall for five tapping gestures on human face, respectively. We also demonstrate that OESense is compatible with earbuds' fundamental functionalities (e.g. music playback and phone calls). In terms of energy, OESense consumes 746 mW during data recording and recognition and it has a response latency of 40.85 ms for gesture recognition. Our analysis indicates such overhead is acceptable and OESense is potential to be integrated into future earbuds. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools.

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“…Ideally, we want to identify features (e.g., frequency components, wavelet coefficients, average amplitude) that create the greater separation between the classes of interest (i.e., corrosion level). For this purpose, we employed the python TSFEL (Barandas et al (2020)) library to automatically extract features from the time series signals. TSFEL extracts over 60 different temporal, statistical, and spectral features.…”

Section: Feature Engineering and Extraction And ML Model Trainingmentioning

confidence: 99%

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

Santos-Villalobos

Ramuhalli

Alanazi

2020

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This project studied the capabilities of Model-Based Image Reconstruction (MBIR) and Machine Learning (ML) algorithms in the imaging and estimation of rebar corrosion from ultrasound signals. The application was challenging due to the introduction of a steel liner between the sensor and the concrete containing the rebar. The study focused in a synthetic specimen with a 6.35 mm thick steel liner, and a 19.05 mm diameter rebar at a depth of 44.5 mm and with four levels of corrosion; 0%, 20%, 50%, and 100% corrosion level. The corrosion was applied uniformly around the rebar, generating a ring in the perimeter of the rebar with different acoustic density. In order to generate a realistic synthetic dataset, we added random texture to the concrete of the synthetic specimens. This texture will mimic the variations encountered in real concrete specimens. We adapted our MBIR algorithm for the application and enhanced the algorithm to integrate in the physical model known specimen features, such as the steel liner thickness. The new MBIR method was able to cancel out reverberation from the steel liner and properly image the rebar. In particular, corrosion for the 50% and 100% cases were easily visible and quantitative metrics showed a slight separation for the 0% and 20% levels. The quantitative results were in agreement with the qualitative assessment. We also developed a ML XGBoost model to estimate corrosion level from the ultrasound signals. By combining the ML method with MBIR, we can pinpoint in the ultrasound signals the section that corresponds to the echoes from the rebar. The extract signals are processed by the XGBoost model for prediction. From each system scan, we obtain 15 predictions. The median prediction value is used as the final prediction. The True Acceptance Rate for the method is over 99%.

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TSFEL: Time Series Feature Extraction Library

Cited by 342 publications

References 10 publications

A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

OESense

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

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