Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis
The paper explores the application of Steiner’s most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8856 |
| _version_ | 1797631245507624960 |
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| author | Victor V. Golovko Oleg Kamaev Jiansheng Sun |
| author_facet | Victor V. Golovko Oleg Kamaev Jiansheng Sun |
| author_sort | Victor V. Golovko |
| collection | DOAJ |
| description | The paper explores the application of Steiner’s most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method’s versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy for 1342 h of exposure, with an uncertainty range of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>3.41</mn></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>3.59</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy, with an uncertainty range of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>3.58</mn></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>3.48</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research. |
| first_indexed | 2024-03-11T11:21:05Z |
| format | Article |
| id | doaj.art-204418b0c74a445d8ef2f5020d872ff0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:05Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-204418b0c74a445d8ef2f5020d872ff02023-11-10T15:12:23ZengMDPI AGSensors1424-82202023-10-012321885610.3390/s23218856Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data AnalysisVictor V. Golovko0Oleg Kamaev1Jiansheng Sun2Canadian Nuclear Laboratories, 286 Plant Road, Chalk River, ON K0J 1J0, CanadaCanadian Nuclear Laboratories, 286 Plant Road, Chalk River, ON K0J 1J0, CanadaCanadian Nuclear Laboratories, 286 Plant Road, Chalk River, ON K0J 1J0, CanadaThe paper explores the application of Steiner’s most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method’s versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy for 1342 h of exposure, with an uncertainty range of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>3.41</mn></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>3.59</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy, with an uncertainty range of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>3.58</mn></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>3.48</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>Gy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research.https://www.mdpi.com/1424-8220/23/21/8856sensor data analysismost-frequent-valuethermoluminescent dosimetersenvironmental gamma backgrounddark matterbootstrapping |
| spellingShingle | Victor V. Golovko Oleg Kamaev Jiansheng Sun Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis Sensors sensor data analysis most-frequent-value thermoluminescent dosimeters environmental gamma background dark matter bootstrapping |
| title | Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis |
| title_full | Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis |
| title_fullStr | Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis |
| title_full_unstemmed | Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis |
| title_short | Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis |
| title_sort | unveiling insights harnessing the power of the most frequent value method for sensor data analysis |
| topic | sensor data analysis most-frequent-value thermoluminescent dosimeters environmental gamma background dark matter bootstrapping |
| url | https://www.mdpi.com/1424-8220/23/21/8856 |
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