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4 results for “Sensor Defect”
Sensor Defect Detection Datasets
<p><strong>Deprecated</strong> - Use https://zenodo.org/record/48728 for a more comprehensive version.</p> <p> </p> <p>Two datasets of sensor values, with each dataset including one defect sensor that delivers incorrect values. The datasets where gathered during tests in a hazardous material storage demonstrator.</p> <p>The datasets are given as comma-separated values in text files. The first line in each file holds time stamps, while the following lines hold the sensor values. The first entry in every line gives the name of the sensor.</p> <p>The first dataset (data_scenario_1.txt) was recorded under normal operating conditions, with the sensor Temperature_Inside_8 delivering incorrect values. In the second scenario there is a leakage of fluid inside the hazardous material storage. At the same time the sensor Smoke_Inside_0 delivers incorrect values.</p>
Sensor Defect Detection Datasets with Configuration
<p>Two datasets of sensor values, with each dataset including one defect sensor that delivers incorrect values. The datasets where gathered during tests in a hazardous material storage demonstrator.</p> <p>The datasets are given as comma-separated values in text files. The first line in each file holds time stamps, while the following lines hold the sensor values. The first entry in every line gives the name of the sensor.</p> <p>The first dataset (data_scenario_1.csv) was recorded under normal operating conditions, with the sensor Temperature_Inside_8 delivering incorrect values. In the second scenario (data_scenario_2.csv) there is a leakage of fluid inside the hazardous material storage. At the same time the sensor Smoke_Inside_0 delivers incorrect values.</p> <p>Additionally attached is configuration data (Configurations.pdf) for the sensor fusion approach that was used to classify the datasets.</p> <p>For more information please contact the uploader.</p>
Typical Sensor Defects Dataset
<p>Thirteen datasets of sensor values, with one dataset without sensor defects (data_standard.csv). All other datasets are based on the dataset without a defect, with the values of Temp_Sensor_2 modified to simulate different sensor defects:</p> <ul> <li>Sensor Drift: 1‰/hour (data_drift_0_001.csv), 2.5‰/hour (data_drift_0_0025.csv), 5‰/hour (data_drift_0_005.csv)</li> <li>Sensor Offset: 1°C Offset (data_offset_1.csv), 2°Offset (data_offset_2.csv), 5°Offset (data_offset_5.csv)</li> <li>Sensor Peaks: 1 Peak/Minute (data_peak_1.csv), 2 Peaks/Minute (data_peak_2.csv), 5 Peaks/Minute (data_peak_5.csv), 10 Peaks/Minute(data_peak_10.csv)</li> <li>Sensor Noise: 10 dB SNR (data_noise_10dB.csv), 0 dB SNR (data_noise_0dB.csv)</li> </ul> <p>The datasets are given as comma-separated values in text files. The first column in each file holds time stamps, while the following columns hold the sensor values. The first entry in every column gives the name of the sensor. All datasets are zipped into one file (data.zip).</p> <p>Additionally attached is configuration data (Configuration.pdf) for the sensor fusion approach that was used to classify the datasets.</p> <p>For more information please contact the uploader.</p>
Mapping the Local Spatial Charge in Defective Diamond by Means of N-V Sensors—A Self-Diagnostic Concept
<p>Mapping the Local Spatial Charge in Defective Diamond by Means of N-V Sensors—A Self-Diagnostic Concept</p> <p>Electrically active defects have a significant impact on the performance of electronic devices based on wide-band-gap materials. This issue is ubiquitous in diamond science and technology, since the presence of charge traps in the active regions of different classes of diamond-based devices (detectors, power diodes, transistors) can significantly affect their performance, due to the formation of space charge, memory effects, and the degradation of the electronic response associated with radiation-induced damage. Among the most common defects in diamond, the nitrogen-vacancy (N-V) center possesses unique spin properties that enable high-sensitivity field sensing at the nanoscale. Here, we demonstrate that N-V ensembles can be successfully exploited to perform direct local mapping of the internal electric-field distribution of a graphite-diamond-graphite junction exhibiting electrical properties dominated by trap- and space-charge-related conduction mechanisms. By means of optically detected magnetic resonance measurements, we performed both point-by-point readout and spatial mapping of the electric field in the active region at different bias voltages. In this novel “self-diagnostic” approach, defect complexes represent not only the source of detrimental space-charge effects but also a unique tool for their direct investigation, by providing an insight on the conduction mechanisms that could not be inferred in previous studies on the basis of conventional electrical and optical characterization techniques.</p>
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