Process Monitoring Research Articles

Design of a Web-Based Monitoring and Evaluation Application for Mentoring Using the Rapid Application Development Method at The Faculty of Computer Science UNIKA Santo Thomas Medan

The academic and thesis supervision processes at the Faculty of Computer Science, UNIKA Santo Thomas Medan, currently face various challenges, including a lack of clear structure in scheduling and difficulties in monitoring student progress. To address these issues, this research aims to develop a web-based application that supports monitoring and evaluation of the supervision process. The application is designed using the Rapid Application Development (RAD) method, which facilitates flexible and responsive development tailored to user needs. With features such as supervision scheduling, student progress tracking, and automatic evaluation, this application is expected to enhance the effectiveness of supervision, facilitate communication between lecturers and students, and support more structured academic management within the faculty. The implementation of this application has successfully improved the efficiency of the supervision process and ensured that students receive more structured guidance.

Electroencephalographic depression after abruptly increasing partial pressure of end-tidal carbon dioxide: a case series

BackgroundProlonged electroencephalographic depression during surgery is associated with poor outcomes for patients. However, the published literature on electroencephalographic depression caused by a sudden increase in the partial pressure of end-tidal carbon dioxide (PETCO2) is lacking.Case presentationWe report four patients who were scheduled for laparoscopic liver surgery under general anesthesia. During the process of EEG monitoring with Sedline, four patients experienced electroencephalographic depression closely after a sudden increase in PETCO2. The four patients showed that electroencephalographic depression mainly manifested as a slow in EEG frequency, a reduction in the amplitude and power of EEG, and a decrease in spectral edge frequency. Patient state index was elevated in three cases.ConclusionsTo summarize, our patients showed EEG depression when PETCO2 suddenly increased, which suggests that clinical doctors should be alert to electroencephalographic depression when the PETCO2 abruptly increases. EEG monitoring devices should be applied in patients with possible hypercapnia. Anesthesiologists must comprehensively interpret the raw EEG, spectral edge frequency, and density spectral array data, in addition to patient sedation index values.

The Impact of Risk Management on Project Success: A Field Study at Humanitarian Organizations in Yemen

The study aimed to determine the impact of risk management (risk identification, risk analysis, risk response and risk monitoring and control) on project success (scope, schedule, cost and quality) at humanitarian organizations operating in Yemen (HOY). The study followed the analytical descriptive approach to meet its objectives. It utilized the simple random technique to select 15 organizations representing 127 HOY and the disproportional stratified random sampling technique to target the respondents. Data was collected through a printed questionnaire from 185 respondents who have roles in managing projects at the targeted organizations. The findings showed that risk management has a significant positive impact on project success. The findings also revealed that among 4 dimensions of risk management process, risk identification and risk monitoring and control were found as the most significant influencers that impact project success at HOY. The study recommends HOY to pay more attention to project risk management to attain higher project success level that ultimately contributes to the performance improvement and sustainability of their organizations. The study also recommends conducting more studies for further confirmations to the hypothesis of the study through testing the study’s variables at industries other than HOY such as banking industries and telecommunications companies.

Integrative approach to fluorescence-based oxygen sensing in polymer optical fibers with surface plasmon resonance

This study demonstrates a technique to enhance oxygen (O2) concentration measurement by combining surface plasmon resonance (SPR) and fluorescence-based sensor on polymer optical fiber (POF). The SPR was generated using silver (Ag) and the fluorescence was generated by organic dye. Three fluorescence dyes were used, Tris (4, 7-diphenyl-1, 10-phenanthroline) ruthenium (II) dichloride ([Ru(dpp)3]2+), platinum octaethylporphyrin (PtOEP) and 5,10,15,20-tetrakis (pentafluorophenyl) 21 H,23H-porphine palladium (II) (PdTFPP). The sensor was tested in a gas chamber with different concentration level of O2. Sensitivity values for the fluorescence dyes for [Ru(dpp)₃]²⁺, PtOEP, and PdTFPP were 0.0099 a.u/%, 0.0343 a.u/% and 0.05 a.u/% respectively. When SPR was implemented, the sensitivity values for Ag/[Ru(dpp)₃]²⁺, Ag/PtOEP, and Ag/PdTFPP were 0.0589 nm/%, 0.0345 nm/%, and 0.118 nm/% respectively. PdTFPP and Ag/PdTFPP exhibit highest sensitivity in each experiment. Therefore, the further analysis for the sensing performance of PdTFPP and Ag/PdTFPP were held. As a result, Ag/PdTFPP exhibits lower limit of detection (LOD), and limit of quantification (LOQ) with 3.054 % and 9.254 %, respectively, with higher R2 of 0.9971 and lower mean standard deviation of 0.174, compared to PdTFPP 16.496 % and 49.988 % respectively, with R2 of 0.9211 and higher mean standard deviation of 2.003. This improvement can benefit researchers and professionals in medical diagnostics, environmental monitoring, and industrial process control by providing a more sensitive and accurate method for measuring O2 levels.

Quality management of head and neck patient treatments using statistical process control techniques.

The treatment, planning, simulation, and setup of radiotherapy patients contain many processes subject to errors involving both staff and equipment. Cone-beam-CT (CBCT) provides a final check of patient positioning and corrections based on this can be made prior to treatment delivery. Statistical Process Control (SPC) techniques are used in various industries for quality management and error mitigation. The utility of SPC techniques to monitor process and equipment changes in our Head and Neck patient treatments was assessed by application to CBCT results from a quality-focused longitudinal study. Individuals and moving range (XmR) as well as exponentially-weighted moving average (EWMA) techniques were explored. The SPC techniques were sensitive to process changes and trends over the 12 years of data collected. A reduction in the random component of patient setup errors needing correction was observed. Systematic components of error remained more stable. An uptick in both datasets was observed correlating with the COVID-19 pandemic. Process control limits for use in prospective process monitoring were established. Challenges that arose from using SPC techniques in a retrospective study are outlined.

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

Craving to belong and to forgive in travel social media: a synthesis between goal progress and self-regulation perspectives

ABSTRACT This research improvises a model highlighting how tourists’ ruminative thinking from travel SNSs could be mitigated through a four-component approach rooted in self-regulation theory and the goal progress theory of rumination. Using a survey design, the model posits the need to belong as a motivational factor that drives travel SNS engagement and travel impulsion through the meditational monitoring processes of forgiveness and trust in SNSs. Two willpower factors – perceived secondary control and perceived enjoyment – were examined. This inquiry adds to the literature by building a synthesis between the two theories to showcase how tourists regulate themselves after traumatic events in cyberspace, and how this is juxtaposed with rumination through the lens of goal setting. The proposed model helps to explain the rumination of negative instances in social media and how tourists negotiate their conduct.

High-Fidelity Sensing Modality for Anomaly Detection in Inkjet Printing

Abstract Inkjet three-dimensional (3D) printing has emerged as a transformative manufacturing technique, finding applications in diverse fields such as biomedical, metal fabrication, and functional materials production. It involves precise deposition of materials onto a moving substrate through a nozzle, achieving submillimeter scale resolution. However, the dynamic nature of droplet deposition introduces uncertainties, challenging consistent quality control. Current process monitoring, relying on image-based techniques, is slow and limited, hindering real-time feedback in erratic droplet ejection. In response to these challenges, we present the zero-dimensional ultrafast sensing (0-DUS) system, a novel, cost-effective, in situ monitoring tool designed to assess the quality of drop-on-demand inkjet printing. The 0-DUS system leverages the sensitivity of the light-beam field interference effect to rapidly and precisely detect and analyze localized droplets. Two core technical advancements drive this innovation: first, the exploration of integral sensing of the computational light-beam field, which allows for efficient extraction of temporal and spatial information about droplet evolution, introducing a novel in situ sensing modality; second, the establishment of a robust mapping mechanism that aligns sensor data with image-based data, facilitating accurate estimation of droplet characteristics. We successfully implemented the 0-DUS system within a commercial inkjet printer and conducted a comparative analysis with ground truth data. Our experimental results demonstrate a detection accuracy exceeding 95%, even at elevated speeds, allowing for an impressive in situ certification throughput of up to 500 Hz. Consequently, our proposed 0-DUS system meets the stringent quality assurance requirements, thereby expanding the potential applications of inkjet printing across a wide spectrum of industrial sectors.

Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model

Abstract Laser directed energy deposition (L-DED) has emerged as a promising technique for rapid prototyping due to its cost-effectiveness and efficiency. However, the intricate and multi-scale physics of the process hinder its widespread application. This paper addresses the challenge by focusing on real-time identification of melt pool states to detect defects early and minimize resource wastage. To achieve this, a FixConvNeXt model was developed for fast and accurate monitoring of melt pool states. This model was trained using 5000 melt pool images captured during the printing of single-track deposits from a charge-coupled device. To evaluate its performance, FixConvNeXt was compared with other models using various metrics. Experimental results demonstrated that FixConvNeXt achieved superior performance in accurately identifying melt pool states with 99.1% accuracy, while also reducing computation burden and processing time. The mechanism of classification by FixConvNeXt was explained using gradient-weighted class activation mapping. The research findings highlight the potential application of online process monitoring in L-DED. This study lays the foundation for future development of an efficient deep learning network for automatic defect detection and feedback control.

Dynamic control of cardboard-blank picking by using reinforcement learning

Abstract Standard packaging lines with high output rates often struggle when dealing with uncertainties in the conditions of the handled materials. This paper focuses on a piece of machinery in an automatic packaging line, namely an automated apparatus that extracts cardboard blanks from a buffer and transfers them to the next section through suction cups. In this context, the success of the operation depends on various controllable parameters, disturbances, and time-dependent variables, whose mutual relationships are not easily identifiable and whose understanding has so far been entrusted to the experiential knowledge of human operators. Currently, drops in picking success rates require the machine to be stopped and operators to intervene on-site, making use of their expertise to identify the issue and recalibrate the machine. To address the problem, this paper presents an artificial-intelligence-enabled controller, capable of continuously and autonomously recalibrating the apparatus and compensating for disturbances, in order to avoid missed or incorrectly picked cardboard blanks. In particular, this work exploits experimental data to build a model of the system, on which a reinforcement-learning algorithm is trained. The controller is tasked with regulating the controllable parameters while monitoring process variables. The developed agent is tested on the real apparatus to assess its performance.

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.

New plant health monitoring framework

With the continuous advancement of technology and the expanding demands of the agricultural market, plant health monitoring holds significant importance in the agricultural sector. However, according to statistics, most current monitoring models exhibit low detection efficiency and inadequate accuracy. To address these issues and specifically enhance the efficiency and accuracy of plant health monitoring, this paper proposes a recognition strategy and plan composed of two parts: (1) identification of plant species and (2) health monitoring, diagnosis, and treatment recommendations. During the health monitoring process, issues such as plant diseases, pests, nutrient deficiencies, and inadequate environmental conditions can be detected. This strategy, implemented through a systematic automated monitoring system, will greatly benefit the large-scale development of agriculture.

Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.

Clinical Practice Guideline for Diabetes Management in the Post-Acute and Long-Term Care Setting

The newly revised Clinical Practice Guideline (CPG) for Diabetes Management in the Post-Acute and Long-Term Care (PALTC) Setting is an extremely useful resource that incorporates new evidence and perspectives related to overall goals in diabetes care. The CPG provides detailed information in usable Tables for how to use and manage the increasing number of medication alternatives for PALTC patients with diabetes. In addition, the CPG addresses the critical roles of all members of the interprofessional team for the optimal management of older adults living with diabetes.The primary goal of care as described in the CPG is to make diabetes management individualized and consistent with the patient's/resident's goals of care, and to optimize quality of life and the ability to live in the least restrictive setting possible. To achieve this, the CPG emphasizes the importance of understanding the patients’ overall health status, what their preferences and values are, their life expectancy, the clinical benefit of tight versus more liberal control of glucose, risk of hypoglycemia, and impact of treatment on quality of life and ability to live in a certain setting of care (e.g., if injectable therapy is possible). As with other PALTmed CPGs, this new CPG takes the team through the process of recognition, assessment, treatment and monitoring. Implementation of the Diabetes Management in PALTC Settings CPG can help those working in these settings ensure that their patients and residents are getting the most appropriate and current evidence-based treatment for this challenging clinical problem.

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Phenylalanine-embedded carbazole-based fluorescent ‘turn-off’ chemosensor for the detection of metal ions

Fluorescent chemosensors are highly important for various applications including medical diagnostics, environmental monitoring, and industrial processing. Significant advancements have been made to produce sensors capable of detecting biologically and environmentally relevant ions. Specifically, carbazole-derived fluorophores are chemically stable agents with the ability to detect anions, cations, and small bioorganic molecules. However, most carbazole-based fluorescent probes for the detection of metal ions are Schiff bases and require stringent pH control to prevent hydrolysis. On the other hand, amide-based sensors that utilize stable amino acid scaffolds provide a robust sensing platform as well as a soft-chemical environment for detecting both soft and heavy metal ions. Herein, we explored an aromatic amino acid Phe-containing carbazole-based “turn-off” fluorescent chemosensor to improve the sensor specificity using π-conjugation and additional binding sites. The structure of the novel chemosensor was characterized by electrospray ionization mass spectrometry (ESI-MS) and nuclear magnetic resonance (NMR) spectroscopy. In addition, the sensing properties towards metal ions were studied using UV–vis and fluorescence spectroscopy. Among the various metal ions tested, the chemosensor showed high selectivity and sensitivity towards Co2+, Ni2+, and Cu2+ ions. The detection limits for Co2+, Ni2+, and Cu2+ ions were found to be 4.78 µM, 3.50 µM, and 5.17 µM respectively. Furthermore, the interaction of Phe-amino-carbazole with the various tested metal ions resulted in a flakes-like supramolecular structure, similar to the native Phe-amino-carbazole, whereas the interaction of the designed chemosensor with the Pb2+ metal ion resulted in a uniform 3D-circular disc-like supramolecular structure, as confirmed by electron microscopy experiment. This highlights the potential of the Phe-containing carbazole-derived chemosensor for the detection of multiple cations with a decrease in the fluorescence response with a lower detection limit.

Combinatorial Order Pre-processing Search (COPS): A new pre-processing strategy for large-scale interpretable data analysis in process analytical technologies

Combinatorial Order Pre-processing Search (COPS), a novel approach for optimizing data pre-processing is proposed in this work. Unlike simultaneous hyperparameter optimization, COPS employs a priori optimization to reduce computational time while refining the search space for preprocessing sequences and combinations. It allows for setting a maximum number of pre-processing methods, while efficiently searching through combinations of methods with chemically relevant knowledge. In this work, 67 calibration datasets across various analytical techniques, including fluorescence spectroscopy, gas chromatography (GC), near-infrared spectroscopy (NIR), mid-infrared spectroscopy (MIR), visible-near-infrared spectroscopy (Vis-NIR), Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and voltammetry were evaluated. COPS yielded significant improvements over existing methodologies based on design of experiment and compounded pre-processing approaches. The COPS outperformed the other methods, resulting in an average root mean square error of prediction (RMSEP) reduction of 31.7%, while also reduced the complexity (number of latent variables) of the model which allows for easier interpretation. This underscores the importance of combinatorial order set theory for the search of pre-processing method combinations (without fixing the sequence of pre-processing methods) to enhance model performance and interpretation. The novel COPS approach can be employed in process analytical technology (such as inline, online or at-line chemical sensing analytics) to enhance predictive accuracy and operational efficiency, fundamentally transforming the quality and reliability of chemical process monitoring and control.

A Modular Smart Ocean Observatory for Development of Sensors, Underwater Communication and Surveillance of Environmental Parameters

The rapid growth of marine industries has emphasized the focus on environmental impacts for all industries, as well as the influence of key environmental parameters on, for instance, offshore wind or aquaculture performance, animal welfare and structural integrity of different constructions. Development of automatized sensors together with efficient communication and information systems will enhance surveillance and monitoring of environmental processes and impact. We have developed a modular Smart Ocean observatory, in this case connected to a large-scale marine aquaculture research facility. The first sensor rigs have been operational since May 2022, transmitting environmental data in near real-time. Key components are Acoustic Doppler Current Profilers (ADCPs) for measuring directional wave and current parameters, and CTDs for redundant measurement of depth, temperature, conductivity and oxygen. Communication is through 4G network or cable. However, a key purpose of the observatory is also to facilitate experiments with acoustic wireless underwater communication, which are ongoing. The aim is to expand the system(s) with demersal independent sensor nodes communicating through an “Internet of Underwater Things (IoUT)”, covering larger areas in the coastal zone, as well as open waters, of benefit to all ocean industries. The observatory also hosts experiments for sensor development, biofouling control and strategies for sensor self-validation and diagnostics. The close interactions between the experiments and the infrastructure development allow a holistic approach towards environmental monitoring across sectors and industries, plus to reduce the carbon footprint of ocean observation. This work is intended to lay a basis for sophisticated use of smart sensors with communication systems in long-term autonomous operation in remote as well as nearshore locations.

In-profile monitoring on univariate profile with application to industrial busbar

In the current research on profile monitoring, most studies treat each profile as a whole to design monitoring statistic. These monitoring methods can only detect whether there exist some anomalies in the process after a complete profile sample is collected. This leads to a lag between the occurrence of a shift and the signaling of an alarm, which hinders engineers from promptly intervening in the out-of-control process. To address this limitation, an in-profile monitoring scheme is proposed in this paper, in which the dynamic influence mechanism of covariates on the response variable is considered. In phase-I, a random varying-coefficient model is utilized to model the dynamic time-varying relationship between covariates and the response variable, and the model parameters are estimated. In Phase-II, for the sequential observations generated by the monitored process, a new monitoring scheme based on the generalized likelihood ratio test is designed. This scheme can adapt to within-profile autocorrelation and arbitrary design points. To enhance online computational efficiency, recursive formulas for calculating the charting statistic are developed. Numerical studies demonstrate that the proposed scheme exhibits satisfactory and robust monitoring performance. Finally, an application to industrial busbar running process monitoring is given to demonstrate the implementation of the scheme.

Stream of Variation Modeling and Monitoring for Heterogeneous Profiles in Multi-Stage Manufacturing Processes

Advanced manufacturing systems typically involve multiple operating stages, where in-process data are observed in the form of profiles that contain rich information for process monitoring. Emerging methodologies have been developed for multi-stage profile monitoring. However, the cases where different stages’ profiles are heterogeneous and variations propagate over stages have not been addressed so far. To this end, this article proposes a heterogeneous profiles-based stream of variation (HP-SoV) framework for multi-stage manufacturing process monitoring. It uses functional decomposition to extract features of heterogeneous profiles from different stages, where the decomposition coefficients are regarded as latent states that propagate along consecutive stages to capture the variation propagation. HP-SoV includes many existing models as its special cases, and enjoys an efficient inference algorithm via maximum likelihood estimation. Based on the one-step-ahead forecast errors of HP-SoV, a group monitoring scheme is further developed for online monitoring. Extensive numerical studies explore the modeling robustness and monitoring effectiveness of HP-SoV under different settings. A case study demonstrates the applicability of HP-SoV in multi-stage process monitoring for heterogeneous profiles.