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4 results for “Intensity classification”
Language intensity classification and Neuronal Networks
<p>Kohonen Self-Organizing Maps (SOM) are a particular type of artificial neural network created by Teuvo Kohonen. Their unsupervised learning makes them suitable for application, among other things, to grouping tasks. Occupations have been grouped into this analysis considering the similarity in value of the variables that define this occupation in terms of language proficiency requirements. The 8 variables used were: v1-speaking skills, v2-writing skills, v3-speech clarity, v4-speech recognition, v5-English knowledge, v6-speaking ability, v7-communication with people outsiders, v8-communication with superiors, equals or subordinates. After applying this authomatic classification technique, the SOC occupations are classified according to five distinc groups with decreasing linguistic intensity:</p> <p>Class 1: High linguistic intenisty requirements<br> Class 2: Medium-high linguistic intenisty requirements<br> Class 3: Medium linguistic intenisty requirements<br> Class 4: Medium-low linguistic intenisty requirements<br> Class 5: Low linguistic intenisty requirements</p>
Image-based Classification of Intense Radio Bursts from Spectrograms: An Application to Saturn Kilometric Radiation
<p>A catalogue of 4874 of the Low Frequency Extensions (LFEs) of Saturn Kilometric Radiation (SKR) detected by Cassini/RPWS from the beginning of 2004 until mission end in 2017. The LFEs presented in this catalogue were identified using a modified U-Net architecture that applied semantic segmentation to spectrogram images in order to extract the exact frequency-time coordinates of the LFE. The files consist of a .json file with the coordinates of each LFE in Time Frequency Catalogue (TFCat) format (Cecconi et. al. 2023). We also include a .csv file with the start and stop times of each LFE in the form of python datetime timestamps, with the average predicted probability per LFE as an accompanying column. </p>
Exploring the Relationship Between Land Cover Classifications and Urban Heat Island Intensity
<p>This dataset is a collection of data and results from a research project conducted by NASA SEES Interns. The research project aimed to study urban heat islands and their relationship with land cover observations. This dataset upload consists of 12 files. One file is a poster pdf that includes all the information needed about the project. The other 11 files are png images of heatmaps, bar graphs, scatter plots, and tables used in the analysis of our project. For quick reference, the abstract to this project is below:</p> <p><strong>The urban heat island (UHI) effect refers to the phenomenon in which urban areas experience higher temperatures compared to their rural counterparts. This research aims to quantify and examine the UHI effect within three areas of interest (AOIs) by utilizing LANDSAT imagery. In addition, this study seeks to explore the relationship between land cover classifications, which represent the most green (rural) and the most urban areas, and the intensity of the UHI effect. To achieve this, temperature data from local weather stations are analyzed, and statistical methods are employed to determine whether a correlation exists between the difference in land cover classifications and the intensity of the UHI effect, as determined by the average temperature difference between urban and rural areas. Google Earth Engine is used to visualize LANDSAT data from 2013 to 2022 in the months of July and August for each AOI. Subsequently, the data is compared with the land cover classifications from Collect Earth Online using statistical models in Microsoft Excel. These tools were used to take data from three pre-selected areas of interest in GLOBE Observer. The data findings from this analysis suggest that the more tree cover and rural an area is according to our classification method, the lower the UHI intensity. On the other hand, the higher the urban area, the higher the UHI intensity. By beginning this research, we have reinforced the validity of land cover classifications, and we now have the capability to generally predict the UHI intensity of locations based on their classifications. Overall, this investigation aims to contribute to a better understanding of the GLOBE land cover classifications and their potential indications of UHI intensity.</strong></p>
Activity and intensity data for UWB radar classification
<p>The task of automated activity classification has previously attracted various avenues of research, and has inspired different methodologies in solving the problem. We outline an unobtrusive method of detecting and classifying different activities and exercises using a 24 GHz UWB radar transceiver and a DNN. The radar transceiver module is used to record the data of a single individual carrying out 6 different activities within a closed environment, and the subsequently processed radar signals are used to train a CNN, which is used to classify the human activities and the intensity of the activities. </p> <p>Using a custom-designed experimental set-up, we measure 500 signal samples consisting of 6 different activities from each of the 7 participants using the UWB radar system. The dataset was recorded in a controlled environment and background noise was recorded prior to the experimentation and subsequently post-processed from the measurements. We define the methods used to record the activity data using the radar transceiver, and the techniques used to process the raw radar signals in this section using the denoising filter selection method.</p>
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