Process Monitoring Research Articles

Automated rice mapping using multitemporal Sentinel-1 SAR imagery using dynamic threshold and slope-based index methods

Rice cultivation plays a crucial role in food security and economic development, particularly in regions like India, due to its vast population and position as the top rice producer globally. This work introduces a novel framework, the Rice Mapping Method (RMM), which leverages Multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery for automated rice mapping. Contrary to the traditional approaches, RMM combines the Dynamic Threshold Method (DTM) for robust rice field identification and a slope-based index for classifying single and double cropping practices. By analyzing VH backscatter patterns and employing specific thresholds, DTM separates rice pixels from the other background pixels. The DTM, which relies on VH backscatter values during the growing season, has been tested across various rice cultivation landscapes, demonstrating high accuracy upto 0.95. DTM is also tested on different rice-growing areas such as the hilly Kodagu district, with an F1 Score of 0.96, and in the flooded delta region of Kuttanad, achieving an F1 Score of 0.93. The Slope-based Index I(r,c) is introduced to differentiate the single and double cropping pixels by calculating the index for the second season of cropping and gives F1 Score of 0.81. The DTM’s effectiveness in rice field identification is evaluated by comparing it to the classification of the Bi-directional Gated Recurrent Unit (Bi-GRU) network. Similarly, the Slope-based Index is compared with other established automated rice mapping methods to assess its accuracy in distinguishing cropping patterns. RMM was successfully applied in mapping rice-growing areas in the Udupi district for 2021, estimating Kharif and Rabi season areas, the estimated rice area is compared to official statistics by the Directorate of Economics and Statistics, Karnataka State. The proposed RMM approach offers a robust solution for mapping rice fields, particularly in regions with complex cropping landscapes, and enhances agricultural monitoring and decision-making processes contributing to sustainable rice production and food security initiatives.

Cointegration stacked autoencoder model based on stationary features reconstruction for non-stationary process monitoring

Cointegration stacked autoencoder model based on stationary features reconstruction for non-stationary process monitoring

Evaluation and process monitoring of jujube hot air drying using hyperspectral imaging technology and deep learning for quality parameters

Timely and effective detection of quality attributes during drying control is essential for enhancing the quality of fruit processing. Consequently, this study aims to employ hyperspectral imaging technology for the non-destructive monitoring of soluble solids content (SSC), titratable acidity (TA), moisture, and hardness in jujubes during hot air drying. Quality parameters were measured at drying temperatures of 55 °C, 60 °C, and 65 °C. A deep learning model (CNN_BiLSTM_SE) was developed, incorporating a convolutioyounal neural network (CNN), bidirectional long short-term memory (BiLSTM), and a squeeze-and-excitation (SE) attention mechanism. The performance of PLSR, SVR, and CNN_BiLSTM_SE was compared using different preprocessing methods (MSC, Baseline, and MSC_1st). The CNN_BiLSTM_SE model, optimized for hyperparameters, outperforms PLSR and SVR in predicting jujube quality attributes. Subsequently, these best prediction models were used to predict quality attributes at the pixel level for jujube, enabling the visualization of the Spatio-temporal distribution of these parameters at different drying stages.

Ankle Moment Estimation Based on A Novel Distributed Plantar Pressure Sensing System.

Ankle moment plays an important role in human gait analysis, patients' rehabilitation process monitoring, and the human-machine interaction control of exoskeleton robots. However, current ankle moment estimation methods mainly rely on inverse dynamics (ID) based on optical motion capture system (OMC) and force plate. These methods rely on fixed instruments in the laboratory, which are difficult to be applied to the control of exoskeleton robots. To solve this problem, this paper developed a new distributed plantar pressure system and proposed an ankle plantar flexion moment estimation method using the plantar pressure system. We integrated eight pressure sensors in each insole to collect the pressure data of the key area of the foot and then used the plantar pressure data to train four neural networks to obtain the ankle moment. The performance of the models was evaluated using normalized root mean square error (NRMSE) and cross-correlation coefficient (ρ). During experiments, eight subjects were recruited for the overground walking tests, and OMC and force plate were used as the gold standard. The results indicate that the Genetic algorithm - Gated recurrent unit estimation algorithm (GA-GRU) was the best estimation model which achieved the highest accuracy in generalized ankle moment estimation (NRMSE = 7.23%, ρ = 0.85) compared with the other models. The designed novel distributed plantar pressure system and the proposed method could serve as a joint moment estimation approach in wearable robot control and human motion state monitoring.

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects.

Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable intelligent process control, analysis, and management, such as complex equipment maintenance, product quality management, and dynamic process monitoring. Traditional methods face challenges in obtaining latent insights due to the growing complexity of IIoT. Recently, the latest development of deep learning provides innovative solutions for IIoT time-series prediction. In this survey, we analyze the existing deep learning-based time-series prediction methods and present the main challenges of time-series prediction in IIoT. Furthermore, we propose a framework of state-of-the-art solutions to overcome the challenges of time-series prediction in IIoT and summarize its application in practical scenarios, such as predictive maintenance, product quality prediction, and supply chain management. Finally, we conclude with comments on possible future directions for the development of time-series prediction to enable extensible knowledge mining for complex tasks in IIoT.

Characterizing strength and location of continental oil shale with drilling process monitoring in southern Ordos Basin, China

Characterizing strength and location of continental oil shale with drilling process monitoring in southern Ordos Basin, China

Investigating the effect of dynamic laser beam oscillations in remote fusion cutting process

The latest research on laser beam fusion cutting has revealed significant improvements in process productivity and cut quality through the use of dynamic beam shaping techniques. While many studies have investigated dynamic beam shaping for proximity cutting, the influence of laser beam oscillations on the remote fusion cutting process remains unexplored. The present work aims to study the effect of dynamic beam shaping on the remote fusion cutting process through analytical modeling, experimental investigations, and in situ high-speed monitoring. Initially, an analytical model based on thermodynamic analysis was developed to assess the influence of circular oscillations on the process zone. This model facilitates the evaluation of process performance from an energetic perspective, providing an estimate of the maximum achievable cutting speed for the remote fusion cutting process across various operating conditions. A significant increment in process productivity could be achieved through beam oscillations. Furthermore, based on theoretical findings, the effect of circular laser beam oscillations superimposed on the processing feed direction was experimentally investigated using a 1 mm thick AISI304 stainless steel material. A 6 kW fiber laser was utilized, alongside a high-speed camera-based system for in situ process monitoring. The experimental results demonstrate a significant increase in the process productivity under dynamic beam shaping conditions, consistent with theoretical findings. Specifically, the maximum achievable cutting speed could be increased from 0.13 to 0.20 m/s. Furthermore, the cut quality of produced samples was evaluated in terms of kerf morphology and profile.

The relationship between audit committees, external auditors, and internal control systems: a literature review and a research agenda

Purpose This paper aims to focus on the relationship between audit committees, external auditors and internal control systems (ICS) and strives to point out mutual influences between the instances to provide an integrated perspective for firms’ multilateral monitoring mechanisms. Furthermore, this study emphasizes the incorporation of sustainability and fraud considerations into the traditional roles of audit committees and auditors. Design/methodology/approach This structured literature review is based on 71 empirical-quantitative studies published in high-quality journals between 2005 and 2022. Considering the classification of ICS into ICS quality and internal audit function, the studies are analyzed with regard to audit committees’ and external auditors’ characteristics, divided into incentives and competencies, as well as their mutual relationships. Findings This study highlights a dynamic trilateral network of relationships between monitoring authorities and primarily shows that audit committees equipped with adequate competencies generate a substitutive effect for external auditors by reducing their efforts, whereas ICS quality serves as a possible mediator in this network of relationships. The establishment of an integrative three-party coalition of competent and adequately incentivized monitoring parties is essential to guarantee sufficient and appropriate ICS and overall corporate governance quality. Practical implications The findings should prompt legislators and firms to ensure a deeper collaboration between audit committees, internal auditors and external auditors to generate synergy effects and economies of scale within the integrative monitoring process. Legislators should develop stricter requirements for competencies of audit committees and auditors. These should include a holistic triad of sustainability, fraud and digital expertise as well as mandatory forensic procedures performed by all monitoring bodies. Originality/value The authors contribute to prior research by highlighting the importance of an integrative three-party coalition of monitoring authorities to ensure corporate governance quality and to generate synergy effects within a dynamic multilateral monitoring process. Furthermore, the authors offer cutting-edge implications by stressing the need for consideration of sustainability and fraud aspects in the traditional work and profiles of audit committees and auditors.

Follow-Up of Neonatal Hearing Screening in the Risk Factor Group for Hearing Loss: Results from a Tertiary Medical Center.

Universal newborn hearing screening has been successfully implemented in many places around the world, and it is recommended that cases with risk factors for hearing loss be followed-up regardless of hearing screening results. However, there is a need for clarity regarding the recommended rate of follow-up and which tests should be performed. The aim of this study was to assess the audiologic follow-up program for the group with risk factors. Our retrospective study involved children of various ages with a risk factor for hearing loss who passed the initial neonatal hearing test but were later diagnosed with hearing loss. Out of 113,708 children born at Hadassah University Medical Center during the years 2013-2021, 6763 were at risk of hearing loss, and their follow-up audiologic test results were studied. Audiologic testing including ABR, OAE, tympanometry and behavioral audiometry was performed in 1534 of 6763 (23%) of the risk factor group that returned to the hospital. In total, 73 children (4.7%) were diagnosed with hearing loss, 54 of whom failed the initial screening and 19 who had passed it. Further examination of the children that passed the initial screening and were later diagnosed with a hearing loss revealed that four cases had been missed in screening (one familial mild hearing loss, one familial progressive loss, one premature infant with a high tone loss, and one NICU graduate with CNS involvement). Another nine cases had late-onset hearing loss (three meningitis, five CMV, and one with a mitochondrial disease). An additional six cases were diagnosed late, and the age of onset of the hearing loss was unknown (two intubated, two with hydrocephalus, one with Cerebral Palsy, and one with general developmental delay). These results reveal the importance of implementing a refined protocol for monitoring hearing in the high-risk group of children that pass neonatal hearing screening with respect to which hearing tests should be conducted, at what age, and the duration of follow-up. Also, barriers to follow-up must be dealt with, and parents should be more involved in the monitoring process.

A Systematic Review of Traceability and Monitoring in Port Transportation Processes for Halal Compliance and Certification

This systematic review provides a comprehensive analysis of halal transportation practices at ports, with a focus on the critical aspects of traceability and monitoring mechanisms. As the global halal market continues to expand rapidly, ensuring the integrity of halal products across the entire supply chain, particularly during port clearance and transportation—has become increasingly vital. This article synthesizes current research on the challenges faced in transportation, technological advancements, and the regulatory frameworks governing halal logistics at ports. It evaluates existing methods for tracking and verifying the halal status of goods from their origin to the destination, emphasizing the pivotal role of traceability and monitoring in upholding halal integrity. The review identifies significant gaps in the literature, particularly concerning the standardization of halal transport protocols and the integration of advanced tracking technologies. Additionally, it examines the policies and regulatory bodies responsible for overseeing the halal transport process. The findings underscore the urgent need to develop comprehensive, globally recognized guidelines that can adapt to the rapidly evolving technological landscape and address the diverse needs of the halal market. This study is instrumental in fostering more efficient, transparent, and reliable halal shipping practices at ports, ultimately bolstering consumer confidence and supporting the continued growth of the global halal market.

Population Dynamics of Nile Tilapia (Oreochromis niloticus, L. 1758) in Lake Nubia, Sudan

This study aims to investigate the population dynamics of Nile tilapia (Oreochromis niloticus, Linnaeus, 1758) in Lake Nubia, Sudan. Monthly samples were collected from fishermen from January to December 2021, and recorded total lengths (TL) of 996 specimens nearest to 1.0 mm using a standardized measuring board; the lengths (TL) exhibited a range from 5.2 to 41.3 cm. Population parameters were assessed using the ELEFAN I routine within FiSAT II software, which produced von Bertalanffy growth parameters as follows: an asymptotic length (L∞) of 43.05 cm, a growth curvature (K) of 0.230 yr⁻¹, and a theoretical length at age zero (t0) of -0.1816. The growth performance index (Φ') was calculated to be 2.639. The instantaneous mortality rates were evaluated, revealing total mortality (Z) at 0.65 yr⁻¹, natural mortality (M) at 0.63 yr⁻¹, and fishing mortality (F) at 0.02 year⁻¹; While the exploitation rate (E) was determined to be 0.04 yr⁻¹. The size at first capture (Lc) was obtained as 5.2 cm, while L50 was estimated at 10.07 cm, both being below the size at first maturity (Lₘ) of 13.5 cm, and the longevity (Tmax) was noted to be 12.86 years. The results indicated poor utilization of the Nile tilapia population in Lake Nubia, and its size at first capture and L50 was below the size at first maturity. This situation is an indication of the need for a continuous process of monitoring and managing to reduce the harvesting of smaller individuals and to improve productivity toward achieving levels that optimize sustainable yield.

Explaining the impact of design choices on model quality in predictive process monitoring

Explaining the impact of design choices on model quality in predictive process monitoring

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

Diagnosis and prognosis of soybean roaster failures using particle swarms

In industry, the monitoring and diagnosis of production processes are crucial issues in ensuring plant reliability, performance and quality. In particular, food processing operations, such as coffee roasting, are subject to numerous risks of failure that can impact on productivity and the quality of the final product. In this context, the main objective of this study was to develop an innovative method for the diagnosis and prognosis of failures in a coffee roasting process. The proposed method differs from standard approaches by using the particle swarm optimization (PSO) algorithm applied to the analysis of signatures of key process variables. This new approach has improved fault detection, with a recognition rate of over 90% for the main types of fault identified, such as heating problems, air obstructions or leaks. In addition to diagnostics, the method has also demonstrated its effectiveness in prognosticating the state of health of the process, with an average error on the prediction of remaining service life reduced to 15%, compared with 35% for fixed-threshold methods. This work has therefore enabled us to develop an innovative method offering superior performance to standard approaches for the diagnosis and prognosis of failures in the roasting process.

Application of Internet of Things (IoT) on Monitoring EC and pH Values for Fertigation System

This paper introduces a novel Internet of Things (IoT) based fertigation control system designed to enhance traditional agricultural practices in Malaysia, particularly in rural areas. The system integrates a fertigation approach, combining fertilization and irrigation, with smart devices and a NodeMCU ESP32 microcontroller to automate the tracking of data from liquid fertilizer mixtures. By utilizing EC and pH sensors, the system accurately monitors the nutrient concentration and pH levels of the fertilizer mix, with data displayed on an IoT platform for real-time observation and control. This approach mitigates common issues such as overfertilization and inefficient nutrient use, offering significant improvements over manual data collection methods. The system's performance was validated, showing successful monitoring and control of the fertilization process, leading to enhanced plant health and crop quality. Key findings indicate that the system's integration with IoT technology provides a more efficient, automated solution for managing fertilizer applications, reducing labor and time while ensuring optimal plant nutrient levels. The study also highlights the potential for future development, including the integration of additional sensors and advanced data analytics to optimize fertigation practices further. The implementation of this IoT-based system represents a significant advancement towards sustainable agriculture, offering practical benefits for Malaysian farmers and setting a foundation for further innovation in fertigation technology.

INOVASI E-COMMERCE UNTUK OPTIMALISASI PROSES KONSUMEN, PRODUKSI, DAN PEMASARAN BAGI PELAKU UMKM

E-commerce technology enables efficient online transactions, providing customers with a convenient and efficient purchasing experience. In terms of production, e-commerce information systems can be integrated with production management, enabling monitoring of the production process, optimizing production efficiency and reducing the risk of stock shortages. E-commerce technology also opens up opportunities for more sophisticated marketing strategies, as MSMEs can utilize consumer data analysis, increasing customer appeal and ordering and sales. E-commerce technology facilitates direct interaction between consumers, fostering closer relationships and improving customer experience. The development of e-commerce technology has significantly increased the effectiveness and efficiency of MSMEs specifically in the fried chicken sector, enabling them to compete in the digital business era. The purpose of this study is to develop an E-Commerce information system in the "Maju Makmur" Kalasan fried chicken industry center that manages MSMEs fried chicken. The research method is a prototype model. This model starts from data collection, system design and testing. The stages of the prototyping development model consist of: communication, rapid planning, rapid modeling, construction, and delivery. The results of this study are a web-based system to optimize consumer, production, and marketing processes in order to increase operational efficiency and market access through an online platform.

Monitoring of insurers' business processes in conditions of digitalization

Monitoring of insurers' business processes in conditions of digitalization

Physiological Predictors of Operator Performance: The Role of Mental Effort and Its Link to Task Performance.

The present study investigated how pupil size and heart rate variability (HRV) can contribute to the prediction of operator performance. We illustrate how focusing on mental effort as the conceptual link between physiological measures and task performance can align relevant empirical findings across research domains. Physiological measures are often treated as indicators of operators' mental state. Thereby, they could enable a continuous and unobtrusive assessment of operators' current ability to perform the task. Fifty participants performed a process monitoring task consisting of ten 9-minute task blocks. Blocks alternated between low and high task demands, and the last two blocks introduced a task reward manipulation. We measured response times as primary performance indicator, pupil size and HRV as physiological measures, and mental fatigue, task engagement, and perceived effort as subjective ratings. Both increased pupil size and increased HRV significantly predicted better task performance. However, the underlying associations between physiological measures and performance were influenced by task demands and time on task. Pupil size, but not HRV, results were consistent with subjective ratings. The empirical findings suggest that, by capturing variance in operators' mental effort, physiological measures, specifically pupil size, can contribute to the prediction of task performance. Their predictive value is limited by confounding effects that alter the amount of effort required to achieve a given level of performance. The outlined conceptual approach and empirical results can guide study designs and performance prediction models that examine physiological measures as the basis for dynamic operator assistance.

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

Deployment of Artificial Intelligence in Radiology: Strategies for Success.

Radiology, as a highly technical and information-rich medical specialty, is well-suited for artificial intelligence (AI) product development, and many FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end-user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.