Real-time Quality Monitoring Research Articles

Welding Defect Monitoring Based on Multi-Scale Feature Fusion of Molten Pool Videos.

Real-time quality monitoring through molten pool images is a critical focus in researching high-quality, intelligent automated welding. However, challenges such as the dynamic nature of the molten pool, changes in camera perspective, and variations in pool shape make defect detection using single-frame images difficult. We propose a multi-scale fusion method for defect monitoring based on molten pool videos to address these issues. This method analyzes the temporal changes in light spots on the molten pool surface, transferring features between frames to capture dynamic behavior. Our approach employs multi-scale feature fusion using row and column convolutions along with a gated fusion module to accommodate variations in pool size and position, enabling the detection of light spot changes of different sizes and directions from coarse to fine. Additionally, incorporating mixed attention with row and column features enables the model to capture the characteristics of the molten pool more efficiently. Our method achieves an accuracy of 97.416% on a molten pool video dataset, with a processing time of 16 ms per sample. Experimental results on the UCF101-24 and JHMDB datasets also demonstrate the method's generalization capability.

Deep Learning-Adjusted Monitoring of In-Hospital Mortality after Liver Transplantation.

Background: Surgeries represent a mainstay of medical care globally. Patterns of complications are frequently recognized late and place a considerable burden on health care systems. The aim was to develop and test the first deep learning-adjusted CUSUM program (DL-CUSUM) to predict and monitor in-hospital mortality in real time after liver transplantation. Methods: Data from 1066 individuals with 66,092 preoperatively available data point variables from 2004 to 2019 were included. DL-CUSUM is an application to predict in-hospital mortality. The area under the curve for risk adjustment with Model of End-stage Liver Disease (D-MELD), Balance of Risk (BAR) score, and deep learning (DL), as well as the ARL (average run length) and control limit (CL) for an in-control process over 5 years, were calculated. Results: D-MELD AUC was 0.618, BAR AUC was 0.648 and DL model AUC was 0.857. CL with BAR adjustment was 2.3 with an ARL of 326.31. D-MELD reached an ARL of 303.29 with a CL of 2.4. DL prediction resulted in a CL of 1.8 to reach an ARL of 332.67. Conclusions: This work introduces the first use of an automated DL-CUSUM system to monitor postoperative in-hospital mortality after liver transplantation. It allows for the real-time risk-adjusted monitoring of process quality.

Frontiers in construction 3D printing: self-monitoring, multi-robot, drone-assisted processes

AbstractTo overcome productivity issues and revolutionize the stagnating construction industry, a large amount of research efforts has been devoted to robot-assisted construction technology. The advancements in robotics including mechanical system design, tool design, digital system design, and numerical control systems design enabled engineers to create complex geometries that are infeasible for conventional construction methods. In addition, innovative robotic systems that utilize mobile platforms, multiple robots, and unmanned aerial vehicles have demonstrated significant promise in fully automating the construction process. This work will provide a perspective on the state-of-the-art applications of robotics in the revolution of construction, where a comprehensive review of the current development of the relevant software and hardware, 3D concrete printing (3DCP), robot-assisted assembly of discrete prefabricated blocks, real-time quality monitoring and feedback control systems, and typical innovative robot-assisted structural designs are conducted. Finally, the limitations of existing robot-assisted construction technology are identified, which leads to several recommendations for future research toward fully automatic construction.

Exploring the Potential of Pectin as a Source of Biopolymers for Active and Intelligent Packaging: A Review

The use of fossil-based plastics in food packaging poses a serious environmental concern. Pectin, a natural biodegradable polymer, offers a potential solution for environmentally friendly and sustainable food packaging to replace fossil-based plastics. This article reviews the applications of pectin in active and intelligent packaging and analyzes the latest research trends. Bibliometric analysis was used to review the existing literature on pectin in food packaging. Data were collected from the Scopus database, which covers research on film manufacturing and pectin-based coating. Pectin-based active packaging contains antimicrobial and antioxidant compounds such as ascorbic acid and essential oils, which effectively prevent bacterial growth while absorbing oxygen and water vapor. In contrast, pectin-based intelligent packaging allows real-time monitoring of food quality through integrated color-changing indicators, eliminating the need for open packaging. Research trends have shown a significant increase in publications on pectin-based packaging, reflecting the growing interest in sustainable packaging solutions. With a focus on innovation and sustainability, pectin can replace conventional plastics and provide safer and more durable packaging solutions, thereby supporting global efforts to reduce the environmental impact of plastic waste.

Transforming and Modernizing Population and Housing Censuses in Africa: Experiences from the 2020 Round (2015-2024)

The objective of our organization is to assist African National Statistical Offices (NSOs) to transition from manual to digital systems, use better methods and new technologies, and increase the reliability and accessibility of their statistics. Innovative approaches adopted to support the transformation to digital censuses include: Use of hand-held devices at the enumeration and dissemination stages to improve data collection and usability respectively. Use of GIS, GPS and digital remote-sensing to enable census-mapping. Provisioning of tablets – sourcing and sharing. Electronic operations dashboards to enable real-time monitoring of progress and quality of enumeration. Automatic data linkage algorithms for census to post-enumeration survey matching. Capacity building initiatives for NSO staff. As a result, to date 29 censuses have been carried out in the 2020 Round, with another 19 planned for 2024/25. Delays have been caused by economic, security and political constraints, as well as the Covid-19 pandemic. The implications of adopting new technologies are clear. Electronic data collection improves quality, reduces processing time and enabled enumeration during the pandemic by minimizing face-to-face contact. We will support documentation of experiences and lessons learnt in the current census round to contribute to the 2030 round. In conclusion, the challenge of working during the pandemic gives NSOs an opportunity to ‘build back better’ and transition to full digital systems. There is a need to focus on the types of results that make the most difference e.g., on disaggregation, small area statistics, Sustainable Development Goals (SDGs) reporting, Agenda 2063 reporting, and monitoring of national development plans.

A novel low-cost sensors system for real-time multipollutant indoor air quality monitoring – Development and performance

Indoor air pollution poses a significant health risk, thus demanding effective indoor air quality (IAQ) monitoring strategies. Low-cost sensors (LCS) have gained attraction for IAQ monitoring, but their data accuracy and robustness remain key challenges. This study addresses gaps in the literature by developing and evaluating a novel low-cost monitor prototype for IAQ monitoring. A detailed design methodology based on requirements analysis was used to create two identical low-cost monitors (LCM) prototypes for multipollutant monitoring, including PM2.5, CO2, CO, O3, NO2, temperature and relative humidity. The LCM were deployed in an in-field monitoring campaign alongside research-grade instruments. The uncorrected sensor signals showed linear response compared to research-grade instruments with high Pearson Correlation Coefficients for 1-min mean: PM2.5 (0.97), CO2 (0.81–0.89), CO (0.95–0.98), and O3 (0.80–0.85). The concentration range of pollutants was observed to play a crucial role in the response and accuracy of the LCS. Kalman filter was implemented with optimised parameters to reduce noise from the signal of ozone sensors. Univariate and multivariate linear models were used to field calibrate the sensors. Linear regression models, with high coefficients of determination (>0.8) and low error values, imply that the developed LCM prototypes can be reliably used for indicative monitoring. In most of the cases, univariate linear models using only the sensor response of LCS as explanatory variables showed significant improvement. Future work will involve longer monitoring campaigns in different microenvironments, evaluation of calibration drift and to assess failure episodes in long-term use.

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Real-time Vertical Air Quality Monitoring System Development for Urban Scale

Datadog with Zigbee Wireless Communication Network Protocol for an Internal Implementation in an Educational Institution

Wireless Sensor Networks are the most powerful technique for monitoring Environmental factors. As the role of WSN relies totally on the service life of the sensor nodes, it is necessary to have complete monitoring of networks. Energy efficiency has always been a key concern for Wireless sensor networks. This paper presents a detailed literature review that examines how an artificial intelligence networking tool(datalog)can be used with ZIGBee-based network protocol for continuous Real-time data quality monitoring to detect bad data quality issues in an educational organization It also Tracks and improves application speed by following requests from beginning to end and monitoring application performance. By using AI algorithms for routing choices and optimization methods, the study aims to improve network performance, energy efficiency, and system scalability. The analysis of different routing strategies with AI implementations will be covered, emphasizing the potential advantages and challenges of this innovative approach in an organization.

Machine-learning-based sampling inspection under OQC capacity for real-time quality monitoring in the TFT-LCD industry

ABSTRACT In the thin film transistor-liquid crystal display (TFT-LCD) industry, the competition is fierce, and the external failure cost is high. To improve the quality of released products, sampling inspection under outgoing quality control (OQC) is conducted before products are released. Owing to the variety and complexity of inspection items, the task must be carried out manually while realising automation is difficult. Traditional random sampling inspection is limited by sampling quantity, manpower, and material resources. To effectively improve acceptable quality levels, this paper proposes a quality predictive monitoring framework tailored to the production and inspection characteristics of the TFT-LCD industry. This framework includes real-time automatic quality monitoring based on an optimal machine learning prediction model with two-phase feature creation, and it also addresses OQC inspection capacity constraints. Experimental results show the prediction performance based on proposed model is better than the traditional random sampling process. In practice, superior average outgoing quality improves by more than 70% at the same sampling level. In addition, the features of the process can be further controlled and managed based on the prediction model to monitor the key parameters in real time and respond to abnormalities quickly, thereby further improving the quality of released products.

Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging

The quality of glutinous rice (GR) is susceptible to deterioration and losses due to biological or environmental factors during storage. Traditional quality assessment techniques are often time-consuming and challenging. In this study, a rapid and reliable hyperspectral imaging (HSI) technique is utilized to monitor GR quality during storage. Paddy samples were dried at 50 °C, 60 °C and 70 °C. Subsequently, these samples were milled and stored under three conditions: freeze storage (−10 °C), cold room (6 °C) and ambient (∼26 °C) for 6 months. The methodology involved data acquisition from both HSI and standard references methods, with data on hyperspectral reflectance, head rice yield (HRY), broken rice yield (BRY) and milled rice yield (MRY) collected every two weeks. Five machine learning (ML) models were evaluated for quality prediction using Python3, with Random Forest (RF) identified as the best performer, achieving a coefficient of determination (R2) of 0.995. Hyperparameter tuning (HPT) further improved the RF model’s R2 by 0.3 %. Parity plot analysis confirmed the accuracy of the RF model in describing GR quality during storage. The study demonstrates the significant impacts of different storage and drying temperatures on HSI data and GR quality attributes. Significant differences in reflectance were observed, with higher reflectance for samples dried at 60 °C and freeze-storage, while lower reflectance for samples dried at 70 °C and cold-room storage. These findings align with reference method results and ML predictions, revealing that drying paddy at 60 °C and storing it under freeze conditions enhances HRY and increases the commercial value of GR. Overall, this study highlights the potential of the HSI for real-time quality monitoring of GR and its applicability to other grains.

Online prediction of joint mechanical properties of FSW thick AA2219-T8 based on multi-source information fusion using 1DCNN

High strength AA2219-T8, Al-Cu alloy of 18 mm plate is welding by friction stir welding (FSW) technology and used for heavy-lift launch vehicles tank. During FSW thick AA2219-T8 plate, joint tensile strength and microhardness are affected by multiple interconnected variables including welding force, temperature, and geometric quantity of the butt face. The joint mechanical properties are difficult to predict and ensure. This paper proposed a hybrid predicting approach based on multi-source information fusion, called Down-Sampling and One-Dimensional Convolutional Neural Network (DS-1DCNN). Initially, FSW experiments were conducted to collect the axial force, welding surface temperature at advancing and retreating side, gap, and mismatch signals, which were fused through DS technology. To extract temporal information and features from signals, a 1DCNN prediction model was established, with the fused multi-source signals as input and the tensile strength and Vickers hardness as output. Subsequently, the optimal model architecture and hyper-parameters of 1DCNN were determined through experiments and grid search combined with K-fold cross-validation, and an online prediction system was developed. Finally, effective validation experiments were conducted and the experimental results agreed with the DS-1DCNN prediction results with an acceptable error of approximately 5%. Parameter sensitivity analysis revealed that the number of convolutional layers and learning rate were identified as critical parameters affecting the model performance. Additionally, time analysis showed that the calculation time of the prediction system was less than 200 ms which can meet the time requirements of real-time thick-plate FSW application. The research lays a foundation on the real-time quality monitoring in the process of FSW thick-plate butt joint.

A novel camera-based sensor for real-time wastewater quality monitoring

ABSTRACT Recent advancements have significantly improved turbidity and absorbance measurement techniques, crucial for municipal and industrial wastewater quality monitoring. This experimental system utilizes image analysis and machine learning on monochrome-camera images of real secondary wastewater effluent samples, irradiated with six LEDs, to classify turbidity and predict absorbance in the visible range. It focuses on low turbidity measurements (0–15 nephelometric turbidity units [NTUs]), the hardest challenge for conventional turbidity sensors. Specifically, this camera-based technique was able to classify within a 2 NTU class, 96 turbidity samples collected from a real wastewater treatment plant with precision and accuracy of over 96%. Additionally, it effectively predicted turbidity and absorbance with a neural network, achieving R-squared coefficients of 0.76 and 0.72, respectively. This innovative monitoring system, deployable in several locations of a wastewater treatment plant, not only addresses the limitations of the existing methods for the low turbidity range but also brings the potential for plant-wide process monitoring. Further testing is in progress to validate the proposed approach in other wastewater applications, such as combined sewer overflow monitoring and waste-activated sludge upset detection where more extreme and rapid changes are expected to occur.

Electric buses as an air pollution and meteorological observation network: Methodology and preliminary results

Many local agencies in the United States and other countries are tasked to install air pollution monitoring systems of highly accurate sensors that have high acquisition, operating, and maintenance costs. The need for expanded coverage of air quality measurements across Salt Lake County (SLCO), Utah is being met by mounting air quality and temperature sensors on an expanding fleet of battery electric buses (BEBs). Monitoring air quality from a mobile sensor network provides real-time insights into air pollution patterns at high temporal and spatial resolution. Mobile measurements contribute to assessing residents' exposure to air pollution, facilitating the implementation of cost-effective public health policies and highlighting disparities. The Electric Bus Air Quality Observation Project was launched in SLCO during July 2021 and has collected millions of observations to date. A BEB traveling at typical traffic speeds (~10 m s−1) can provide multiple measurements along city block lengths of up to ~200 m. With careful analysis that factors in the time response of the differing sensors, variability from block-to-block may be attributed to fine-scale factors (e.g., pollution and heat sources, tree shading and urban vegetation, etc.). Preliminary findings showcase the value of increased coverage and resolution. During an extreme heat event in July 2023, both the morning and afternoon temperature readings showed differences of over 6.5 °C (12 °F), primarily as an east-west gradient with similar gradients in ozone. We conclude that temperature and pollutant concentration readings, at fine spatial and temporal resolutions, will facilitate future health studies and equitable policy and mitigation strategies. Our study demonstrates that our partnerships established with governmental, non-profit, and transit agencies facilitate the successful transfer of research and development to operational real-time mobile air quality monitoring.

Research on Real-Time Groundwater Quality Monitoring System Using Sensors around Livestock Burial Sites

Research on Real-Time Groundwater Quality Monitoring System Using Sensors around Livestock Burial Sites

Smart grid power quality monitoring and regulation based on photoelectric sensors and optical system signal processing

With the rapid development of smart grids, power quality monitoring and regulation have become an important task to ensure the stable operation of the power grid. Traditional power quality monitoring methods have problems such as low monitoring accuracy and poor real-time performance. Therefore, this article proposes a smart grid power quality monitoring and adjustment method based on photoelectric sensors and optical system signal processing to improve monitoring accuracy and real-time performance. This article uses photoelectric sensors to monitor the power quality in the power grid in real-time, and transmits the monitoring data to the optical system. Signal processing of monitoring data through optical systems to extract key parameters related to power quality. Finally, by adjusting the relevant equipment of the smart grid, the regulation and optimization of power quality can be achieved. The experimental results show that the smart grid power quality monitoring and adjustment method based on photoelectric sensors and optical system signal processing can effectively improve monitoring accuracy and real-time performance. Through real-time monitoring and regulation of power quality, the stability and reliability of the power grid have been significantly improved.

A Comparison of the Validity of Online Courses Quality Evaluation Models

The large scale expansion of online courses has led to the crisis of course quality issues. In this study, we first established an evaluation index system for online courses using factor analysis, encompassing three key constructs: course resource construction, course implementation, and teaching effectiveness. Subsequently, we employed factor analysis and entropy weight TOPSIS multi-attribute decision analysis methods to comprehensively evaluate 541 courses. Later on, we conducted correlation and regression analyses between the evaluation results of these two methods and that of experts. The results reveal that the comprehensive evaluation scores derived from both factor analysis and entropy weight TOPSIS models exhibit a significant positive correlation with the experts'. Furthermore, the factor analysis model outperforms the entropy weight TOPSIS model in predicting experts' evaluation scores. The application of this model will help achieve timely and accurate course evaluation, and provide novel idea and approach for real-time monitoring of online courses quality.

Development of Non-Invasive miRNA Markers for Assessing the Quality of Human Induced Pluripotent Stem Cell-Derived Retinal Organoids.

Human retinal organoids (ROs) have emerged as valuable tools for studying retinal development, modeling human retinal diseases, and screening drugs. However, their application is limited primarily due to time-intensive generation, high costs, and low reproducibility. Quality assessment of RO differentiation is crucial for their application in research. However, traditional methods such as morphological evaluation and immunohistochemical analysis have limitations due to their lack of precision and invasiveness, respectively. This study aims to identify non-invasive biomarkers for RO differentiation quality using exosomal microRNAs (miRNAs), which are known to reflect cell-specific functions and development in the retina. We differentiated ROs from human induced pluripotent stem cells (hiPSCs) and classified them into 'superior' and 'inferior' groups based on morphological and immunohistochemical criteria. Exosomes from the conditioned media were isolated and analyzed for miRNA content. Our findings revealed distinct miRNA profiles between superior and inferior ROs, with superior ROs exhibiting higher miRNA diversity and specifically up- or down-regulated miRNAs. Gene ontology and pathway enrichment analyses indicated that the target genes of these miRNAs are involved in neuron proliferation and differentiation. The study suggests the potential of exosomal hsa-miR-654-3p and hsa-miR-451a as non-invasive biomarkers for real-time monitoring of RO quality, facilitating the development of standardized, efficient, and cost-effective culture methods.

Recent advancements in chitosan-based intelligent food freshness indicators: Categorization, advantages, and applications

With an increasing emphasis on food safety and public health, there is an ongoing effort to develop reliable, non-invasive methods to assess the freshness of diverse food products. Chitosan-based food freshness indicators, leveraging properties such as biocompatibility, biodegradability, non-toxicity, and high stability, offer an innovative approach for real-time monitoring of food quality during storage and transportation. This review introduces intelligent food freshness indicators, specifically those utilizing pH-sensitive dyes like anthocyanins, curcumin, alizarin, shikonin, and betacyanin. It highlights the benefits of chitosan-based intelligent food freshness indicators, emphasizing improvements in barrier and mechanical properties, antibacterial activity, and composite film solubility. The application of these indicators in the food industry is then explored, alongside a concise overview of chitosan's limitations. The paper concludes by discussing the challenges and potential areas for future research in the development of intelligent food freshness indicators using chitosan. Thus, chitosan-based smart food preservation indicators represent an innovative approach to providing real-time data for monitoring food quality, offering valuable insights to both customers and retailers, and playing a pivotal role in advancing the food industry.

PH-indicator based on delignified jute fiber and red cabbage anthocyanins for monitoring fish spoilage using a smartphone application

Halochromic materials that show visible color changes in response to changes in pH are suitable for the real-time monitoring of fish spoilage. In this study, an easy-to-use, simple, inexpensive, and non-toxic fish freshness indicator was fabricated by combining delignified jute (Corchorus olitorius) fibers and anthocyanins (halochromic materials) from red cabbage (DFA: Delignified jute fibers incorporated with anthocyanins). A single-step decolorization/delignification using solar irradiation along with NaOH and H2O2 treatment was used for modifying the jute fibers. This method helps to overcome the self-color, mitigates the lack of affinity of jute fibers towards anthocyanins and preserves the lignin so that the strength of the fiber is not impacted. A smartphone-based color analysis was used for real-time fish quality monitoring using DFA. To the best of our knowledge, there are no reports on the use of jute fibers as substrates to incorporate anthocyanins for food spoilage monitoring. The indicator displayed an observable color response to the pH and varying concentrations of amine compounds. During the storage of fish (mackerel), the colorimetric indicator showed a visible color change from pink (for fresh fish) to blue (for spoiling fish) and then to green (for spoiled fish), corresponding to changes in pH and total volatile basic nitrogen. To offer a straightforward quantitative assessment of color changes, we utilized the freely available Android application Color Grab to measure the color using RGB and L*, a*, and b* indices. The DFA indicator providing naked-eye analysis has the potential to be an effective tool for real-time monitoring of on-site food spoilage by non-specialized personnel in resource-limited areas.

Fast and sustainable production of smart nanofiber mats by solution blow spinning for food quality monitoring: Potential of polycaprolactone and agri-food residue-derived anthocyanins

The shelf life of perishable foods is estimated through expensive and imprecise analyses that do not account for improper storage. Smart packaging, obtained by agile manufacturing of nanofibers functionalized with natural pigments from agri-food residues, presents promising potential for real-time food quality monitoring. This study employed the solution blow spinning (SBS) technique for the rapid production of smart nanofiber mats based on polycaprolactone (PCL), incorporating extracts of agricultural residues rich in anthocyanins from eggplant (EE) or purple cabbage (CE) for monitoring food quality. The addition of EE or CE to the PCL matrix increased the viscosity of the solution and the diameter of the nanofibers from 156 nm to 261–370 nm. The addition of extracts also improved the mechanical and water-related properties of the nanofibers, although it reduced the thermal stability. Attenuated total reflectance Fourier-transform infrared spectroscopy confirmed the incorporation of anthocyanins into PCL nanofibers. Nanofiber mats incorporated with EE or CE exhibited visible color changes (ΔE ≥ 3) in response to buffer solutions (pH between 3 and 10), and ammonia vapor. Smart nanofibers have demonstrated the ability to monitor fish fillet spoilage through visible color changes (ΔE ≥ 3) during storage. Consequently, smart nanofibers produced by the SBS technique, using PCL and anthocyanins from agro-industrial waste, reveal potential as smart packaging materials for food.