Real-time Quality Control System Research Articles

Moving average quality control of routine chemistry and hematology parameters- a toolbox for implementation.

Moving average quality control (MA QC) is apatient-based real-time quality control system. Advantages compared to conventional periodic internal quality control (IQC) include absence of commutability problems and continuous monitoring of performance. We implemented MA QC for multiple routine hematology and chemistry parameters. We describe the evaluation process and provide practical tools to aid MA QC implementation. Nine parameters (serum sodium, calcium, bicarbonate and free thyroxine, hemoglobin [Hb], mean corpuscular volume, mean corpuscular hemoglobin concentration [MCHC], reticulocyte count and erythrocyte sedimentation rate [ESR]) were chosen for initial consideration. Using data extractions from the laboratory information system (LIS;General Laboratory Information Management System), evaluation of usefulness and optimization of MA QC settings was performed using bias detection curves. After this, MA QC settings were incorporated in our LIS for further evaluation and implementation in routine care. Three out of nine parameters (Hb, ESR, and sodium) were excluded from MA QC implementation due tohigh variation and technical issues in the LIS. For the sixremaining parameters, MA QC showed added value to IQC and was therefore implemented in the LIS. For three parameters a direct MA alarm work-up method was set up, including newly developed built-in features in the LIS. Forthe other parameters, we identified MA utilization beyond real-time monitoring. Implementation of MA QC has added value for our laboratory setting. Additional utilization beyond real-time QC monitoring was identified. We find MA QC especially useful for trend monitoring, detection of small shifts after maintenance and inter-analyzer comparisons.

A Real-Time Quality Control System Based on Manufacturing Process Data

Quality prediction is one of the key links of quality control. Benefitting from the development of digital manufacturing, manufacturing process data have grown rapidly, which allows product quality predictions to be made based on a real-time manufacturing process. A real-time quality control system (RTQCS) based on manufacturing process data is presented in this paper. In this study, the relationship between the product real-time quality status and processing task process was established by analyzing the relationship between the product manufacturing resources and the quality status. The key quality characteristics of the product were identified by analyzing the similarity of the product quality characteristic variations in the manufacturing process based on the big data technology, and a quality-resource matrix was constructed. Based on the quality-resource matrix, the RTQCS was established by introducing an association-rule incremental-update algorithm. Finally, the RTQCS was applied in actual production, and the performance of RTQCS was verified by experiments. The experiments showed that the RTQCS can effectively guarantee the quality of product manufacturing and improve the manufacturing efficiency during production.

기상청 수원기상대 농업기상 관측요소의 품질관리

In this research, we applied a procedure of quality control (QC) to the agro-meteorological data measured at the Suwon weather station of Korea Meteorological Administration (KMA). The QC was conducted through six steps based on the KMA Real-time Quality control system for Meteorological Observation Data (RQMOD) and four steps based on the International Soil Moisture Network (ISMN) QC modules. In addition, we set up our own empirical method to remove erroneous data which could not be filtered by the RQMOD and ISMN methods. After all these QC procedures, a well-refined agro-meteorological dataset was complied at both air and soil temperatures. Our research suggests that soil moisture requires more detailed and reliable grounds to remove doubtful data, especially in winter with its abnormal variations. The raw data and the data after QC are now available at the NCAM website (http://ncam.kr/page/req/agri_weather.php).

ISKANDARnet CORS Network Integrity Monitoring

The increasing use of real-time high precision Global Positioning System (GPS) methods has leads in raising number of Continuously Operating Reference Station (CORS) establishment. However, it is complicated to expect the disparity in the performance and status of the reference stations. Thus, a system for dealing with data quality control in order to supervise data streams in real-time has been proposed. This paper focuses on the number of existing approaches to review the quality of raw GPS observations and presents architecture for the development of a real time quality control system in Peninsular Malaysia.

Tailor-made proficiency curves in laparoscopic hysterectomy: enhancing patient safety using CUSUM analysis

The objective of this study is to develop a risk-adjusted real-time quality control system in laparoscopic hysterectomy with respect to blood loss, operative time and adverse events in order to signal derailing surgical performance in a timely fashion. Based on prior research, uterus weight, body mass index, number of surgeons, prior abdominal surgery, and type of laparoscopic hysterectomy were identified as significant covariates predicting successful surgical outcome. Cumulative sum (CUSUM) analysis, a model based on dichotomous input (success or “failure”), was selected as a predictive tool for performance analysis. Cutoff values were set at blood loss <200 mL and operative time <120 min and no adverse event. Risk-adjusted CUSUM graphs were constructed. In order to detect progressive failure rates (odds ratio 2.0 compared to average) in surgical performance (for blood loss, operative time, and adverse events) within 20 procedures, as a result, surgeons with average clinical outcomes will be flagged once in every 70–75 procedures (median) without justified derailing performance. With proposed validated and risk-adjusted CUSUM graphs, gynecologists are able to continuously monitor their surgical performance in laparoscopic hysterectomy. Consequently, this identifies suboptimal factors, which allow improvement of their surgical outcomes (by means of adjustment) and further enhancement of patient safety.

WISE 복합기상센서 관측 자료 품질관리시스템

A real-time quality control system for meteorological data (air temperature, air pressure, relative humidity, wind speed, wind direction, and precipitation) measured by an integrated meteorological sensor has been developed based on comparison of quality control procedures for meteorological data that were developed by the World Meteorological Organization and the Korea Meteorological Administration (KMA), using time series and statistical analysis of a 12-year meteorological data set observed from 2000 to 2011 at the Incheon site in Korea. The quality control system includes missing value, physical limit, step, internal consistency, persistence, and climate range tests. Flags indicating good, doubtful, erroneous, not checked, or missing values were added to the raw data after the quality control procedure. The climate range test was applied to the monthly data for air temperature and pressure, and its threshold values were modified from <TEX>${\pm}2{\sigma}$</TEX> and <TEX>${\pm}3{\sigma}$</TEX> to <TEX>${\pm}3{\sigma}$</TEX> and <TEX>${\pm}6{\sigma}$</TEX>, respectively, in order to consider extreme phenomena such as heat waves and typhoons. In addition, the threshold values of the step test for air temperature, air pressure, relative humidity, and wind speed were modified to <TEX>$0.7^{\circ}C$</TEX>, 0.4 hPa, 5.9%, and <TEX>$4.6m\;s^{-1}$</TEX>, respectively, through standard deviation analysis of step difference according to their averaging period. The modified quality control system was applied to the meteorological data observed by the Weather Information Service Engine in March 2014 and exhibited improved performance compared to the KMA procedures.

Prediction of fatty acids content in pig adipose tissue by near infrared spectroscopy: At-line versus in-situ analysis

A handheld micro-electro-mechanical system (MEMS) based spectrometer working in the near infrared region (NIR) (1600–2400nm) was evaluated for in-situ and non-destructive prediction of main fatty acids in Iberian pig (IP) carcasses. 110 IP carcasses were measured. Performance of the instrument was compared with at-line high-resolution NIRS monochromators working in two analysis modes: melted fat samples (transflectance cups) and intact adipose tissues (interactance fiber optic). Standard Error of Prediction (SEP) values obtained on the MEMS-NIRS device were: 0.68% (stearic), 1.30% (oleic), 0.55% (linoleic) and 1% (palmitic), explaining a variability of 83%, 84%, 81% and 78%, respectively. As expected, this represented a loss of predictive capability in comparison to at-line models, even with the same spectral characteristics as on the handheld device. However, the estimated total errors were at the same level for gas chromatography and NIRS analysis. This indicates that the MEMS-NIRS in-situ analysis of each individual carcass provides a cost-effective and real-time quality control system with suitable accuracy.

Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process

The monitoring of welding process is crucial for the development of a real time quality control system for the pulsed metal inert gas welding (PMIGW) process. This work introduces an intelligent system for weld joint strength prediction in a PMIGW process based on the analysis of acquired current signal by wavelet packet transform. A thirteen-dimensional array of process features, i.e. six process parameters and seven wavelet packet features, are used to describe various welding conditions. These process features obtained from a set of experiments are employed as input vectors of an artificial neural network model to predict the corresponding weld joint strengths. The results, i.e. the prediction errors, show that the use of wavelet packet features gives much accurate prediction as compared to the use of the purely time domain features.

Real Time Quality Assessment for CORS Networks

The growing use of real time high accuracy Global Positioning System (GPS) techniques has resulted in an increase in the number of critical decisions made on the basis of a GPS derived position. When making these decisions mobile users require assurance that the GPS position quality meets their requirements. Providers of Continually Operating Reference Stations (CORS), whom mobile users are generally reliant upon, must also be able to assure users that their data meets agreed quality standards. Unfortunately, the realistic and reliable description of position and data quality is an area in which GPS has traditionally been weak. Research being undertaken as part of the Cooperative Research Centre for Spatial Information (CRC-SI) is attempting to address this problem by assessing and reporting on the quality of raw GPS observations in real time. This paper examines a number of existing approaches to assessing the quality of raw GPS observations and presents a conceptual architecture for the development of a real time quality control system.

Real-Time Quality Control of Wave Observations in the North Sea

Abstract The use of ocean wave data in new data assimilation techniques prompted the development of a real-time quality control system for wave height and wave period observations. Over the North Sea, a relatively large number of wave observations, as well as a reliable wave model, are available. Therefore, a variety of tests can be made, allowing for a quite sophisticated wave-data quality control system. The system uses some of the ideas of Gandin's comprehensive quality control. In particular, an ensemble of single-variate tests is used instead of one large multivariate test, and data are not rejected and thrown away in successive tests but can be rehabilitated until the very end. Three months of data have been used to test the system. The decisions of the system are found to agree with manual analyses. The statistical properties of the accepted and rejected data are checked. After quality control, the residuals of the quality control tests have almost Gaussian distributions.

A neural network based quality control system for steel strip manufacturing

A real time quality control system for flat products, applied to steel strip manufacturing, is introduced. The system is able to perform this task when the product runs at its maximum speed (15 m/s). The outlines of the overall system are explained, paying special attention to the neural classifier. A new model of neural network has been developed in order to implement this subsystem. Some benchmark results are resembled in order to ease the understanding of the model.