Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms.

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user's sport activity.

Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL).

Perfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts.

Comparison of Structural Integrated Piezoceramics, Piezoelectric Patches and Strain Gauges for Condition Monitoring.

This paper presents a new approach to the structural integration of piezoceramics into thin-walled steel components for condition-monitoring applications. The procedure for integrating the sensors into metal components is described, and their functionality is experimentally examined with a 2 mm-thick steel sheet. The signal quality of the produced sensors is investigated in a frequency range from 100 Hz to 50,000 Hz and is compared with the results of piezo patches and strain gauges under the same conditions. The results show that due to a higher signal-to-noise ratio and a better coherence, the structurally integrated piezoceramics and the piezo patches are more qualified sensors for vibration measurement in the examined frequency range than the strain gauges. The measurements also indicate that the patches provide higher amplitudes for the frequency range up to 20 kHz. Beyond that, up to 40 kHz, the integrated sensors supplied higher amplitudes. The better signal quality in different frequency ranges as well as the different manufacturing and application methods can be interpreted as an advantage or disadvantage depending on the boundary conditions of the condition-monitoring system. In summary, structural integrated piezoceramics extend the options of monitoring technology.

The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP.

Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.