Time Series and Multiple Linear Regression Calibration Model for CO Monitoring Data

CO is a kind of air pollutant with the largest amount and the widest distribution in the atmosphere produced by combustion of carbon containing substances. Real-time monitoring of the concentration of CO can grasp the air quality in time and take corresponding measures to the pollution sources. Monitoring data may be affected by the internal factors and the external factors. ARIMA was used for the internal factor as A. Meteorological factors were taken as external factors, and the difference of CO between the standard data and monitoring data was taken as dependent variable. Multivariate linear regression was modeled as B. Time series calibration model was obtained Y=A+B. The error analysis showed that the accuracy of CO was improved. The additive model could effectively calibrate CO monitoring data

ARIMA and Multiple Linear Regression Additive Model for SO2 Monitoring Data’s Calibration

SO2 is one of the main air pollutants produced by industrial waste gas, civil combustion and automobile exhaust. Real-time monitoring of the concentration of SO2 can grasp the air quality in time and take corresponding measures to the pollution sources. Monitoring data may be affected by the internal factors and the external factors. ARIMA was used for the internal factor as A. Meteorological factors were taken as external factors, and the difference of SO2 between the standard data and monitoring data was taken as dependent variable. Multivariate linear regression was modeled as B. Time series calibration model was obtained Y=A+B. The error analysis showed that the accuracy of SO2 was improved. The additive model could effectively calibrate SO2 monitoring data

Assessing system impedance based on data regrouping

In recent years, assessing supply system impedance has become crucial due to the concerns on power quality and the proliferation of distributed generators. In this paper, a novel method is shown for passive measurement of system impedances using the gapless waveform data collected by a portable power quality monitoring device. This method improves the overall measurement accuracy through data regrouping. Compared with the traditional methods that use the consecutive measurement data directly, the proposed method regroups the data to find better candidates with less flotation on the system side. Simulation studies and extensive field tests have been conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method can serve as a useful tool for impedance measurement tasks performed by utility companies

Assessing system impedance based on data regrouping

In recent years, assessing supply system impedance has become crucial due to the concerns on power quality and the proliferation of distributed generators. In this paper, a novel method is shown for passive measurement of system impedances using the gapless waveform data collected by a portable power quality monitoring device. This method improves the overall measurement accuracy through data regrouping. Compared with the traditional methods that use the consecutive measurement data directly, the proposed method regroups the data to find better candidates with less flotation on the system side. Simulation studies and extensive field tests have been conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method can serve as a useful tool for impedance measurement tasks performed by utility companies

A Method for Utility Harmonic Impedance Estimation Based on Constrained Complex Independent Component Analysis

Utility harmonic impedance is an important parameter for harmonic mitigation. In this paper, a method for utility harmonic impedance estimation method based on constrained independent component analysis is proposed. The conventional impedance estimation method based on ComplexICA has two major problems: the algorithm is not suitable for separating weak and strong source mixed signals, and lots of sample data should be provided to avoid converging on a local optimum. To solve the two problems, the prior information of the utility harmonic source is added to the objective function of ComplexICA; in this paper, the measurement data at PCC when the load is shutdown are chosen as the prior information. Then the utility harmonic source signal can be recovered and the separated matrix can be obtained effectively. The connection between the utility harmonic source, utility harmonic impedance and the data at PCC are established using Norton equivalent circuit, and then the separation matrix is used to calculate utility harmonic impedance. The performance and feasibility of the proposed method are verified by the computer simulation and field test. Compared with the current ComplexICA method, the proposed method is more adaptive to changes in the background harmonic and the calculation result is more stable

Ultrasound image-based deep learning to assist in diagnosing gross extrathyroidal extension thyroid cancer: a retrospective multicenter studyResearch in context

Summary: Background: The presence of gross extrathyroidal extension (ETE) in thyroid cancer will affect the prognosis of patients, but imaging examination cannot provide a reliable diagnosis for it. This study was conducted to develop a deep learning (DL) model for localization and evaluation of thyroid cancer nodules in ultrasound images before surgery for the presence of gross ETE. Methods: From January 2016 to December 2021 grayscale ultrasound images of 806 thyroid cancer nodules (4451 images) from 4 medical centers were retrospectively analyzed, including 517 no gross ETE nodules and 289 gross ETE nodules. 283 no gross ETE nodules and 158 gross ETE nodules were randomly selected from the internal dataset to form a training set and validation set (2914 images), and a multitask DL model was constructed for diagnosing gross ETE. In addition, the clinical model and the clinical and DL combined model were constructed. In the internal test set [974 images (139 no gross ETE nodules and 83 gross ETE nodules)] and the external test set [563 images (95 no gross ETE nodules and 48 gross ETE nodules)], the diagnostic performance of DL model was verified based on the pathological results. And then, compared the results with the diagnosis by 2 senior and 2 junior radiologists. Findings: In the internal test set, DL model demonstrated the highest AUC (0.91; 95% CI: 0.87, 0.96), which was significantly higher than that of two senior radiologists [(AUC, 0.78; 95% CI: 0.71, 0.85; P < 0.001) and (AUC, 0.76; 95% CI: 0.70, 0.83; P < 0.001)] and two juniors radiologists [(AUC, 0.65; 95% CI: 0.58, 0.73; P < 0.001) and (AUC, 0.69; 95% CI: 0.62, 0.77; P < 0.001)]. DL model was significantly higher than clinical model [(AUC, 0.84; 95% CI: 0.79, 0.89; P = 0.019)], but there was no significant difference between DL model and clinical and DL combined model [(AUC, 0.94; 95% CI: 0.91, 0.97; P = 0.143)]. In the external test set, DL model also demonstrated the highest AUC (0.88, 95% CI: 0.81, 0.94), which was significantly higher than that of one of senior radiologists [(AUC, 0.75; 95% CI: 0.66, 0.84; P = 0.008) and (AUC, 0.81; 95% CI: 0.72, 0.89; P = 0.152)] and two junior radiologists [(AUC, 0.72; 95% CI: 0.62, 0.81; P = 0.002) and (AUC, 0.67; 95 CI: 0.57, 0.77; P < 0.001]. There was no significant difference between DL model and clinical model [(AUC, 0.85; 95% CI: 0.79, 0.91; P = 0.516)] and clinical + DL model [(AUC, 0.92; 95% CI: 0.87, 0.96; P = 0.093)]. Using DL model, the diagnostic ability of two junior radiologists was significantly improved. Interpretation: The DL model based on ultrasound imaging is a simple and helpful tool for preoperative diagnosis of gross ETE thyroid cancer, and its diagnostic performance is equivalent to or even better than that of senior radiologists. Funding: Jiangxi Provincial Natural Science Foundation (20224BAB216079), the Key Research and Development Program of Jiangxi Province (20181BBG70031), and the Interdisciplinary Innovation Fund of Natural Science, Nanchang University (9167-28220007-YB2110)