Quality Control Approach Research Articles

Deep-Learned Perceptual Quality Control for Intelligent Video Communication

With the development of video technology, a large amount of video data generated from video conferences, sports events, live broadcasts and network classes flows into our daily lives. However, ultra-high-definition video transmission is still a challenge due to the limited network bandwidth and instability, which further affects the quality of video service closely linked with consumer electronic video display. To address this challenge, we propose a deep-learned perceptual quality control approach, which can significantly improve the video quality and visual experience at the same bandwidth. The proposed scheme mainly involves saliency region extraction, perceptual-based bits allocation, and video enhancement. Firstly, we exploit a multi-scale deep convolutional network module to predict the static saliency map that semantically highlights the salient regions. Secondly, we develop a recurrent neural network model to extract the dynamic saliency regions. Finally, a three-level rate allocation scheme is developed based on the resulted saliency guidance, which is more reasonable by taking into account the visual characteristics of human eyes. Experimental results on a large dataset show that our method achieves an average gain of 1.5dB on the salient regions without introducing an extra bandwidth burden, which significantly improves the visual experience and paves the way to intelligent video communication.

Improvement of Ultrasound Image Quality Using Non-Local Means Noise-Reduction Approach for Precise Quality Control and Accurate Diagnosis of Thyroid Nodules.

This study aimed to improve the quality of ultrasound images by modeling an algorithm using a non-local means (NLM) noise-reduction approach to achieve precise quality control and accurate diagnosis of thyroid nodules. An ATS-539 multipurpose phantom was used to scan the dynamic range and gray-scale measurement regions, which are most closely related to the noise level. A convex-type 3.5-MHz frequency probe is used for scanning according to ATS regulations. In addition, ultrasound images of human thyroid nodules were obtained using a linear probe. An algorithm based on the NLM noise-reduction approach was modeled based on the intensity and relative distance of adjacent pixels in the image, and conventional filtering methods for image quality improvement were designed as a comparison group. When the NLM algorithm was applied to the image, the contrast-to-noise ratio and coefficient of variation values improved by 28.62% and 19.54 times, respectively, compared with those of the noisy images. In addition, the image improvement efficiency of the NLM algorithm was superior to that of conventional filtering methods. Finally, the applicability of the NLM algorithm to human thyroid images using a high-frequency linear probe was validated. We demonstrated the efficiency of the proposed algorithm in ultrasound images and the possibility of capturing improved images in the dynamic range and gray-scale region for quality control parameters.

Highly-sensitive volatile organic compounds evaluation by three-dimensional ZnFe2O4/ZnSnO3 heterostructures and their predictive grain quality monitoring

As a common metabolite of various agricultural products, 1-octen-3-ol plays a key role in cereal quality evaluation with adverse impacts on human health after inhalation, of which the detectors meeting the detection limit of ppb level that satisfies the request of application have been reported rarely. Herein, ZnFe2O4 and ZnSnO3 are selected to design heterostructured materials for highly sensitive and selective detection of 1-octen-3-ol gas. The sensing results prove that fine-tuned surface oxygen vacancies and highly-efficient electron transition of the nanocomposites are achieved through modulating the loading amount of ZnFe2O4, contributing to the significantly enhanced response (40.15 @ 50 ppm), low limit of detection (420 ppb), optimized selectivity and excellent long-term stability (40.786 ± 1.693 @ 50 ppm for 15 days). The enhancement mechanism is explained by a large specific surface area with hollow mesoporous structures, consequent bandgap narrowing and decreased charge transfer resistance, which is further verified through DFT analysis. Moreover, the practical application for the unhusked rice detection is carried out. This work shed light on a new promising candidate for detecting characterized volatile organic compounds released from mildewed rice which may strikingly affect the rice storage industry by simplifying the quality control approach to a great extent.

Commissioning, characterisation and temperature stabilisation of a 22000 channel SiPM-on-tile hadron calorimeter system

With the successful construction and operation of a highly granular hadron calorimeter system, featuring ∼22000 individually read out SiPM-on-tile channels, the CALICE collaboration has set the next milestone in proving the scalability of the concept for a future high energy linear collider experiment. For this large sample of photosensors a new approach of quality control was required to sufficiently characterise and monitor device parameters for both, test bench and in-situ beam test data. In the presence of temperature fluctuations during operation, it was possible to stabilise the SiPM responses with a fully automated adjustment of the bias voltage based on frequent temperature measurements, thanks to the excellent parameter uniformity of the devices. This contribution presents the results of SiPM parameter studies during the construction and commissioning phase and reports about the system performance and the experience of automated temperature compensation at system level during operation.

New HPLC pattern-oriented approach for quality control of enteric coated tablets containing aescin from Aesculus hippocastanum

From the seed of Aesculus hippocastanum L., a complex mixture of triterpene saponin glycosides known as aescin was isolated, purified and characterized to be an active pharmaceutical ingredient for preparing of drug products. A reversed phase high performance liquid chromatographic method with UV detection was developed for the determination of total triterpene glycosides in enteric coated tablets containing aescin in a pattern-oriented manner. The mobile phase consisted of a mixture of acetonitrile and 0.1 % phosphoric acid solution (40: 60). UV detection was performed at 220 nm. The chromatographic column was Gemini C18 column, 5 µm 250 mm × 4.6 mm, which was maintained at 25 ◦C, connected with a precolumn (C18, 4.0 × 3.0 mm, 5 µm). The linear range was from 53.4 to 160.1 μg·mL−1. The RSD of intra-and inter-day precision variations were less than 1 % and the mean recovery was (100.66 ± 0.49) % (RSD = 0.64 %). The method showed its suitability, simplicity, rapidity and precision. Accordingly, it was proven adequate for quality control of this herbal drug product either for registration with the regulartory authority or routine analysis.

Quality monitoring in vibro ground improvement – A hybrid machine learning approach

AbstractThis article presents a new approach of quality control to vibro ground improvement techniques based on hybrid machine learning (ML), i.e., a combination of classical analysis and ML techniques. The process is monitored with an instrumented rig equipped with multiple sensors. Key performance indicators (KPIs) are used to identify anomalous foundation columns. As the foundation columns are sub‐surface, there is no direct access to ground truth; consequently, unsupervised ML is applied to the recorded time‐series data. The risk of not detecting defective elements is reduced by the combination of two independent methods for anomaly detection, KPI‐ and ML‐based classification. The ML is used to gain a deeper process understanding and to detect anomalies which were not considered in the design phase of the KPI. New pre‐processing techniques were derived from the insights gained from the ML classifier; this led to a more robust classifier. It is shown how unsupervised ML, using a multi‐channel variational autoencoder (VAE) with long short‐term memory (LSTM) layers, can be utilized in a knowledge discovery process (KDP).

A new approach for quality prediction and control of multistage production and manufacturing process based on Big Data analysis and Neural Networks

As consumers care more and more about product quality, it is important to mine the deep correlations between production and manufacturing parameters and the evaluation of product quality through the analysis of industrial big data. The existing research of product quality prediction faces several major problems: the lack of diverse quality features, the poor tractability of abnormal parameters, the strong nonlinearity of parameters, the obvious sequential property of data, and the severe time lag of data. To solve these problems, this paper explores the quality prediction and control of multistage MP process (MPMP) based on big data analysis. Firstly, the prediction strategy and flow were specified for MPMP product quality prediction, and the features were extracted from MPMP product quality. After that, the MPMP product quality features were described in multiple dimensions, the attention mechanism was introduced to the prediction process. In addition, the recurrent neural network was improved, and an MPMP product quality prediction model was established on bidirectional long short-term memory (BiLSTM) network. Our model was compared with AdaBoost and XGBoost through experiments. The effectiveness of our model was demonstrated by the results of the appearance quality PQ1, and the area under the curve (AUC) for each process parameter. In general, our model is superior to other algorithms in the accuracy, mean accuracy, and precision of product quality prediction.

ANALISA PARAMETER MESIN INJECTION MOLDING TERHADAP CACAT PRODUK HANDLE GRIP MENGGUNAKAN METODE TAGUCHI

One of the causes of losses from the company is the presence of product defects in the production process, the cause of product defects that occur is suboptimal machine parameters. This study aims to analyze machine parameters so that product defects are reduced or minimal. In this study, the largest product defects were found, significant product defect factors, optimal combination of machine parameters and minimizing the defects of the company's products using the Taguchi Method with a Statistical Quality Control approach. The research data comes from the defects of the PT.X. Handle Grip machine product, from these data, it will then be processed using the Statistical Quality Control method, taguchi method, analysis of variants (ANOVA) and repair control. From statistical quality control analysis data, the biggest product defects can be obtained, namely heavy product defects of 4.74%. The analysis of the taguchi method can be obtained the best combination of parameters, namely an injection pressure of 52 bar, a cooling duration of 5 s and an injection temperature of 195oC. While the most influential factors on product defects based on ANOVA are injection pressure, injection temperature and finally cooling duration because F hit > F table with successive results of 403 > 3.04, 61 > 3.04 and 27 > 3.04. The results of the repair control were obtained better results, before the study of severe product defects of 4.74% dropped to 0.08%.

Expedient validation of LED reliability with anomaly detection through multi-output Gaussian process regression

The existing LED reliability assessment procedures are costly and time-consuming. It poses even greater challenges for the purchasers to validate the manufacturer test results, prior to goods acceptance. In absence of a simple and expedient validation, purchasers often bear the reliability risk in good faith. Leveraging on the 3-stage early degradation modes, as a signature, for LED under the moisture-electrical-temperature (MET) test, an expedient assessment framework is proposed to validate if the supplied LED batches conform to the contracted reliability. Multi-output Gaussian process regression (MOGPR) is performed on sparse reference data to establish an acceptable threshold for the degradation signature. When regressed against the reference data with contracted reliability, the test LED that displays signature anomaly will be deliberated for rejection. The acceptable threshold is calibrated to minimise false negative rate and false positive rate. The procedure is repeated in accordance with acceptable quality level (AQL) or existing sampling policies to accept or reject the batch. The framework achieves a reduction of the validation time from 6000 h to 100 h. While the initial motivation is to facilitate acceptance by purchasers, manufacturer may also adopt this approach for out-going quality control (QC) or rapid evaluation during design prototyping.

Inexpensive and simple tool for quality control of nutraceutical and tonic products with capillary electrophoresis and contactless conductivity detection: Some developments in Vietnam

In this study, we report the development of versatile and cost-effective analytical approaches for analysis and quality control of different nutraceutical and tonic products, using capillary electrophoresis (CE) coupled with contactless conductivity detection (C4D). Insights into CE-C4D instrumental designs that are adapted to the context in developing countries are provided. A highlight was made for quality control of vitamins, glucosamine and some minerals in different nutraceutical formulations collected from pharmacies in Vietnam. The lowest detection limit (LOD) achieved with the developed methods was 0.1 mg/L for vitamins, using the optimized background electrolyte (BGE) composed of 12 mM arginine (Arg)/acetic acid (AcOH) at pH 7.5 with 10 % acetonitrile (ACN). For magnesium and B6, the optimized CE-C4D conditions with the BGE composed of 10 mM Arg/AcOH (pH 5) with 20 % ACN allows its detection down to 0.05 mg/L. For glucosamine and calcium, the best performance with LOD of 0.05 mg/L was achieved with BGE composed of 10 mM Tris/AcOH (pH 5). Good agreement between results from CE-C4D and the confirmation methods was achieved for all investigated compounds, with their result deviations less than 15 %.

Introduction of a monitoring system for Bingham fluids in additive manufacturing with concrete

Additive manufacturing of concrete structures is a novel and emerging technology. Without a doubt, a great advantage is the free contouring which enables completely new designs in civil engineering. In future, lower construction costs are expected due to an increased construction speed and a reduction of required material and workers. Nevertheless, architects and civil engineers rely on a certain quality of execution to fulfil construction standards and codes. Although several techniques and approaches demonstrate the advantages, quality control during the printing process is considered extremely rarely. At the Bundesanstalt für Materialforschung und -prüfung (BAM), a new test rig for additive manufacturing using concrete extrusion is established. This test rig allows to manufacture structures with a maximum dimension of 1 m x 1 m x 2 m. The main purpose is the design of new cement and mortar mixtures and the measurement of their properties during the printing process. Although mortar properties vary only locally, the entire integrity of the structure may be compromised. Therefore, a real-time process monitoring is required which records and documents the entire printing process. At the end, additional destructive tests are possible to determine the mechanical properties of the printed elements. In a first approach, the water-cement ratio, the viscosity, the density, the temperature, and the volume flow are monitored. The test rig and the used measurement techniques will be described in detail. Measurement data will be shown and parameters to control the printing process are discussed. This novel process monitoring is a unique approach for a holistic quality control of additive manufactured concrete structures.

Development of GC-MS/MS Method for Simultaneous Estimation of Four Nitrosoamine Genotoxic Impurities in Valsartan.

Recently, N-nitrosamines were unexpectedly detected in valsartan and other generic sartan products. Taking into this account, we developed a sensitive and stable multiple reaction monitoring mode-based "gas chromatography-tandem mass spectrometry (GC-MS/MS)" approach for the quantification of "four N-nitrosamines" in valsartan, especially, N-nitrosodiisopropylamine, N-nitroso ethyl isopropylamine, N-nitrosodiethylamine, and N-nitrosodimethylamine. GC and MS conditions were optimized with specificity, sensitivity, linearity, precision, and accuracy of the parameters. The approach was validated as per the "International Council for Harmonization" recommendations. The identification limits and limits of quantification of N-nitrosamines in valsartan varied between 0.02 and 0.03 ppm, and 0.06-0.09 ppm, respectively. The obtained values were satisfactory with limits established by the United States Food and Drug Administration for sensitivity requirements. The regression coefficients greater than 0.999 for four N-nitrosamines in the calibration curve demonstrated the strong linearity of the process. The retrievals of "N-nitrosamines" in valsartan between 91.9-122.7%. For the intra-day and inter-day accuracy studies, the (relative standard deviation) was less than 9.15%. The proposed approach has rapid analysis capability, high precision, accuracy, and good sensitivity, which give a reliable approach for N-nitrosamines quality control in valsartan.

Supercritical fluid chromatography coupled to high-resolution tandem mass spectrometry: an innovative one-run method for the comprehensive assessment of chocolate quality and authenticity.

To assess chocolate quality and authenticity comprehensively, a combination of various analytical procedures is involved, thereby making the process time-consuming and costly. Thus, we investigated the potential of ultra-high performance supercritical fluid chromatography coupled to quadrupole-time of flight mass spectrometry (UHPSFC-QTOF-MS) as an alternative to "classic" methods. By combining hexane and aqueous extracts from sequential extraction, a single 8-min analytical run enabled us (i) to determine cocoa butter equivalents (CBEs) and milk fat content based on the detection of selected triacylglycerols, (ii) to calculate dry non-fat cocoa solids based on determined theobromine and caffeine content, and (iii) to profile contained sugars. To obtain the most comprehensive information about sample composition, the MS method comprised a full MS scan for non-target screening and several time-scheduled targeted MS/MS functions ("parallel reaction monitoring") optimized according to the possible concentration ranges of the analytes. For 40 different chocolate samples, our results and those obtained by using standard methods (LC-UV for non-fat cocoa solids, and GC-FID for CBEs) were in good agreement. Compared to the conventional approach for chocolate quality and authenticity control, the presented SFC-MS method is a fast, cost-effective, and efficient alternative, and only samples suspicious for the presence of CBE should be referred to the standard GC-FID method for exact CBE quantification. In the study, also some challenges offered by SFC-MS have been addressed.

Individual diametral compression behavior of a ceramic proppant

In this work we report the systematic individual diametral compression of a commercial ceramic proppants, describe the main features of their mechanical behavior and explain them in terms of morphology, microstructure and complementary characterizations.A fragile behavior was observed and described, and mechanical parameters were easily evaluated from the stress-strain measured curves (maximum stress and displacement), an apparent stiffness parameter (slope) was proposed from the linear part of the evaluated curves as well. Weibull analysis was performed for the measured stresses, with the resulting Weibull parameters were also illustrative and allowed a better understanding of the behavior of an individual proppant submitted to diametral compression.The possibility of employing this approach or strategy for both proppants development and quality control is proposed and discussed. This was found to be descriptive and complementary to the typical collective bed crush test.

A "smart" Imputation Approach for Effective Quality Control Across Complex Clinical Data Structures.

The overwhelming need to improve the quality of complex data structures in healthcare is more important than ever. Although data quality has been the point of interest in many studies, none of them has focused on the development of quantitative and explainable methods for data imputation. In this work, we propose a "smart" imputation workflow to address missing data across complex data structures in the context of in silico clinical trials. AI algorithms were utilized to produce high-quality virtual patient profiles. A search algorithm was then developed to extract the best virtual patient profiles through the definition of a profile matching score (PMS). A case study was conducted, where the real dataset was randomly contaminated with multiple missing values (e.g., 10 to 50%). In total, 10000 virtual patient profiles with less than 0.02 Kullback-Leibler (KL) divergence were produced to estimate the PMS distribution. The best generator achieved the lowest average squared absolute difference (0.4) and average correlation difference (0.02) with the real dataset highlighting its increased effectiveness for data imputation across complex clinical data structures.

In-field one-step measurement of dissolved chemical oxygen demand with an integrated screen-printed electrochemical sensor

The increasing awareness of the importance of water for human and environmental health has prompted the need for developing fast, user-friendly and cost-effective analytical approaches for quality control and management. This work reports the fabrication and analytical assessment of a very simple sample-to-result electrochemical sensor for discrete and precise measurements of dissolved chemical oxygen demand (COD) in water samples of urban wastewater treatment plants (UWWTP). The device is fabricated by screen printing and shows a three-electrode cell configuration including a working electrode made of a composite material comprising a porous carbon matrix and copper nanoparticles, synthesized by a customized one-pot sol-gel process. The key feature of this device is the implementation of a paper disk loaded with sodium hydroxide covering the electrochemical cell area that allows sample filtering and pH conditioning. Thus, analytical measurements are carried out by casting a 20 µL sample drop without requiring any further user intervention. The overall sensor fabrication process can be scaled up for cost-effective mass manufacturing. The feasibility and performance of this integrated device are first evaluated using glucose as a standard analyte. A linear range up to 400 mg L− 1 O2 and a limit of detection of around 26 mg L− 1 O2 is achieved, this being well below the EU legal concentration limit of 125 mg L− 1 set for effluents of UWWTPs. Three real samples are analyzed in the lab as well as on-site and COD sensor values are in agreement with those obtained with the standard dichromate method at a certified laboratory, within the error limits. This miniaturized novel sensor platform greatly simplifies the standard analytical procedure for measuring COD, facilitating rapid decentralized analysis for precise control of urban wastewater treatment.

Plant available potassium and phosphorus in arable soil: a comparative study on testing methods

Determination of soil fertility status is based on determination of plant available nutrients which occur in several forms. This strongly challenges the choice of extracting solution and detection technique. Deriving a mathematical model for data recalculation would allow relating the results of different methods without loss of agronomical validation data. This comparative study was aimed at assessing the relationship between two methods for determination of plant available potassium and phosphorus in arable soil. Soil K and P were extracted followed a diluted double acid (Mehlich 1) and an acetate/lactate methods (ALM). Potassium in the obtained extracts was determined by flame AES and phosphorus - by molybdenum blue spectrophotometry. To compare the results obtained by the both extraction methods, the uncertainty of measurement of each method was assessed following the single laboratory validation and quality control approach. The expended uncertainty was found to be 2 mg/100g as P2O5 and 7 mg/100g as K2O (at k=2). The results from the comparative analysis of 93 arable soil samples showed a correlation between Mehlich-1 and ALM extracted P and K within uncertainty limits (at k=3). Based on the recalculated values of extractable K and P, the fertility status of 60 % of soil samples were correctly classified, 30 % with acceptable error and 7 % could not be classified. The study showed that Mehlich-1 and ALM methods provided comparable results for studied range of soil samples. However, determination of soil fertility status based on recalculated results should be made with caution as it could be highly influenced by soil type.

A Dual-Band High-Sensitivity THz Metamaterial Sensor Based on Split Metal Stacking Ring

Terahertz (THz)-detection technology has been proven to be an effective and rapid non-destructive detection approach in biomedicine, quality control, and safety inspection, among other applications. However, the sensitivity of such a detection method is limited due to the insufficient power of the terahertz source and the low content, or ambiguous characteristics, of the analytes to be measured. Metamaterial (MM) is an artificial structure in which periodic sub-wavelength units are arranged in a regular manner, resulting in extraordinary characteristics beyond those possessed by natural materials. It is an effective method to improve the ability of terahertz spectroscopy detection by utilizing the metamaterial as a sensor. In this paper, a dual-band, high-sensitivity THz MM sensor based on the split metal stacking ring resonator (SMSRR) is proposed. The appliance exhibited two resonances at 0.97 and 2.88 THz in the range of 0.1 to 3 THz, realizing multi-point matching between the resonance frequency and the characteristic frequency of the analytes, which was able to improve the reliability and detection sensitivity of the system. The proposed sensor has good sensing performance at both resonant frequencies and can achieve highest sensitivities of 304 GHz/RIU and 912 GHz/RIU with an appropriate thickness of the analyte. Meanwhile, the advantage of multi-point matching of the proposed sensor has been validated by distinguishing four edible oils based on their different refractive indices and demonstrating that the characteristics obtained in different resonant frequency bands are consistent. This work serves as a foundation for future research on band extension and multi-point feature matching in terahertz detection, potentially paving the way for the development of high-sensitivity THz MM sensors.

Artificial Neural Network and Near Infrared Light in Water pH and Total Ammonia Nitrogen Prediction

Water quality plays an important role in aquaculture. The operation of a freshwater aquaculture fish farming is highly dependent on the ability to understand, monitor, and control the physical and chemical constituents of the water. pH and total ammonia nitrogen (TAN) levels are two critical water quality parameters that affect fish growth rate and health. However, pH and TAN levels are affected by uncontrollable factorse.g.weather, temperature, and biological processes occurring in the water. Therefore, it is important to monitor changes in pH and TAN levels frequently to maintain optimal conditions for freshwater habitats. Near infrared spectroscopy (NIR) has been extensively investigated as an alternative measurement approach for rapid quality control without sample preparation. Therefore, this research aims to evaluate the feasibility of machine learning combined with NIR light in predicting the water pH and TAN values of a fish farming system. The proposed system contains three main components i.e.a multi-wavelength light emitting diode (LED), a light sensing element, and a machine learning model i.e.artificial neural network (ANN). First, the transmitted NIR light with different wavelengths of water samples was measured using the proposed system. Then, the actual pH and TAN values of the water samples were quantified using conventional methods. Next, ANN was used to correlate the measured NIR transmittance with the pH and TAN values. The results show that ANN with four hidden neurons achieved the best prediction performance with a mean square error (MSE) of 0.1466 and 0.3136 and a correlation coefficient (R) of 0.8398 and 0.9560 for the pH and TAN predictions, respectively. These results show that ANNcoupled with NIR light can be promisingly developed for in situ water quality prediction without sample preparation.

Opportunities and challenges for the use of common controls in sequencing studies.

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.