Real-time Monitoring Framework Research Articles

Characteristics of the East Asian Summer Monsoon Using GK2A Satellite Data

In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia.

Real-Time Monitoring of Data Pipelines: Exploring and Experimentally Proving that the Continuous Monitoring in Data Pipelines Reduces Cost and Elevates Quality

Data pipelines are crucial for processing and transforming data in various domains, including finance, healthcare, and e-commerce. Ensuring the reliability and accuracy of data pipelines is of utmost importance to maintain data integrity and make informed business decisions. In this paper, we explore the significance of continuous monitoring in data pipelines and its contribution to data observability. This work discusses the challenges associated with monitoring data pipelines in real-time, propose a framework for real-time monitoring, and highlight its benefits in enhancing data observability. The findings of this work emphasize the need for organizations to adopt continuous monitoring practices to ensure data quality, detect anomalies, and improve overall system performance.

Detecting potential cooperative network for tourist attractions in a destination using search data.

This study addresses the critical need for regional tourism integration and sustainable development by identifying cooperation opportunities among tourist attractions within a region. We introduce a novel methodology that combines association rule mining with complex network analysis and utilizes search index data as a dynamic and contemporary data source to reveal cooperative patterns among tourist attractions. Our approach delineates a potential cooperative network within the destination ecosystem, categorizing tourist attractions into three distinct communities: core, intermediary, and periphery. These communities correspond to high, medium, and low tourist demand scales, respectively. The study uncovers a self-organizing network structure, driven by congruences in internal tourist demand and variances in external tourist experiences. Functionally, there is a directed continuum of cooperation prospects among these communities. The core community, characterized by significant tourist demand, acts as a catalyst, boosting demand for other attractions. The intermediary community, central in the network, links the core and periphery, enhancing cooperative ties and influence. Peripheral attractions, representing latent growth areas within the destination matrix, benefit from associations with the core and intermediary communities. Our findings provide vital insights into the dynamics, systemic characteristics, and fundamental mechanisms of potential cooperation networks among tourist attractions. They enable tourism management organizations to employ our analytical framework for real-time monitoring of tourism demand and flow trends. Additionally, the study guides the macro-control of tourism flows based on the tourism network, thereby improving the tourist experience and promoting coordinated development among inter-regional tourist attractions.

Digital Twin Framework for Real-time Monitoring of Robot-Arm in Construction Site

Digital Twin Framework for Real-time Monitoring of Robot-Arm in Construction Site

Automated Online-Sampling Multidimensional Liquid Chromatography with Feedback-Control Capability as a Framework for Real-Time Monitoring of mAb Critical Quality Attributes in Multiple Bioreactors.

Real-time monitoring of biopharmaceutical reactors is becoming increasingly important as the processes become more complex. During the continuous manufacturing of monoclonal antibodies (mAbs), the desired mAb product is continually created and collected over a 30 day process, where there can be changes in quality over that time. Liquid chromatography (LC) is the workhorse instrumentation capable of measuring mAb concentration as well as quality attributes such as aggregation, charge variants, oxidation, etc. However, traditional offline sampling is too infrequent to fully characterize bioprocesses, and the typical time from sample generation to data analysis and reporting can take weeks. To circumvent these limitations, an automated online sampling multidimensional workflow was developed to enable streamlined measurements of mAb concentration, aggregation, and charge variants. This analytical framework also facilitates automated data export for real-time analysis of up to six bioreactors, including feedback-controlling capability using readily available LC technology. This workflow increases the data points per bioreactor, improving the understanding of each experiment while also reducing the data turnaround time from weeks to hours. Examples of effective real-time analyses of mAb critical quality attributes are illustrated, showing substantial throughput improvements and accurate results while minimizing labor and manual intervention.

Laser-based vibration measurement using diffractive beam samplers for vibration monitoring of large-scale bridges

This paper introduces an innovative design for a laser-based apparatus employing diffractive beam sampling technology, with the primary purpose of characterizing vibration frequencies induced within large-scale bridges. The functioning principle of the device hinges upon its ability to discern minute alterations in the relative positions of a subset of the laser beam, resulting from the oscillatory behavior of a laser pendulum mechanism. The captured dynamic variations are seamlessly integrated into a real-time monitoring framework through the utilization of an adept image sensor, complemented by sophisticated software functionalities. Noteworthy for its amalgamation of straightforward implementation and remarkable precision, the proposed methodology stands as a potent instrument poised to ensure the secure and robust surveillance of the structural well-being of large-scale bridges.

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms

Harmful algal and cyanobacterial blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world’s water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces Discrete Event System Specification-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with model-based system engineering and cyber-physical systems infrastructure for dynamic environments.

A framework for real-time monitoring, analysis and adaptive sampling of viral amplicon nanopore sequencing.

The ongoing SARS-CoV-2 pandemic demonstrates the utility of real-time sequence analysis in monitoring and surveillance of pathogens. However, cost-effective sequencing requires that samples be PCR amplified and multiplexed via barcoding onto a single flow cell, resulting in challenges with maximising and balancing coverage for each sample. To address this, we developed a real-time analysis pipeline to maximise flow cell performance and optimise sequencing time and costs for any amplicon based sequencing. We extended our nanopore analysis platform MinoTour to incorporate ARTIC network bioinformatics analysis pipelines. MinoTour predicts which samples will reach sufficient coverage for downstream analysis and runs the ARTIC networks Medaka pipeline once sufficient coverage has been reached. We show that stopping a viral sequencing run earlier, at the point that sufficient data has become available, has no negative effect on subsequent down-stream analysis. A separate tool, SwordFish, is used to automate adaptive sampling on Nanopore sequencers during the sequencing run. This enables normalisation of coverage both within (amplicons) and between samples (barcodes) on barcoded sequencing runs. We show that this process enriches under-represented samples and amplicons in a library as well as reducing the time taken to obtain complete genomes without affecting the consensus sequence.

Efficient Data Fusion Framework for Real-Time Monitoring of Air Pressure Systems in Scania Trucks

Monitoring air pressure systems in heavy-duty vehicles such as Scania trucks is a key driver for operational safety and efficiency in the automotive industry. However, the complex interaction of sensors and data sources makes it difficult to quickly detect potential system failures. This problem is solved in our paper where we present a special-purpose data fusion framework for real-time monitoring of Scania trucks’ air pressure systems. To achieve this, PCA is used to reduce the size of the dataset followed by a voting classifier which combines diverse models such as Decision Trees, Random Forests, Naive Bayes, and Linear Regression using ensemble learning. In particular, our comparative analysis shows that the Voting Classifier outperforms other ML methods in terms of prediction accuracy. These findings suggest that our fusion framework can be utilized for the early detection of air pressure anomalies in heavy-duty vehicles enhancing their safety record.

AWS IoT Core and Amazon DeepAR based predictive real-time monitoring framework for industrial induction heating systems

AWS IoT Core and Amazon DeepAR based predictive real-time monitoring framework for industrial induction heating systems

An Investigation of the Use of Personal Protective Equipment on the Jobsite using Transfer Learning Approach

Practicing engineers throughout the globe are very concerned about the safety at the construction sites. There are still dangers in construction workplaces and thus requires suitable detection and risk assessment. This research establishes a framework for real-time monitoring of Personal Protective Equipment (PPE) compliance by construction workers, which is meant to be incorporated into an organization's safety workflow. Using the Convolutional Neural Network (CNN) model which has been constructed using multiple transfer learning approaches, this inquiry study takes the existence of hard helmets and safety feature jackets as a part of its analysis. Four types of compliances are predicted by the model, including; 1. Red Sign (NOT SAFE) 2. Green Sign (SAFE) 3. Yellow Sign (No Hard Hat or Gloves and something missing) 4. Dark Yellow (No Jacket and many protectives missing). Overall, the research shows that computer vision-based approaches through transfer learning may be used to automate safety-related compliance procedures at construction sites. It is also clear that real-time performance is better, with a higher accuracy rate after review and study of the obtained consequences by different transfer learning approaches.

An innovative framework for real-time monitoring of pollutant point sources in river networks

Simultaneous identification of the location and release history of pollutant sources in river networks is an ill-posed and complicated problem, particularly in the case of multiple sources with time-varying release patterns. This study presents an innovative method for solving this problem using minimum observational data. To do so, a procedure is proposed in which, the number and the suspected reaches to the existence of pollutant sources are determined. This is done by defining two different types of monitoring stations with an adaptive arrangement in addition to real-time data collection and reliable flow and transport mathematical models. In the next step, the sources’ location and their release history are identified by solving the inverse source problem employing a geostatistical approach. Different scenarios are discussed for different conditions of number, release history and location of pollutant sources in the river network. Results indicated the capability of the proposed method in identifying the characteristics of the sources in complicated cases. Hence, it can be effectively used for the comprehensive monitoring of river networks for different purposes.

Gold–Silver Bimetallic Alloy Nanoparticles in a Covalent Organic Framework for Real-Time Monitoring of Hydrogen Peroxide from Live Cells

An efficient and simple way has been described to prepare gold and silver bimetallic alloy nanoparticles (Au-AgNPs) in an organic framework with a metal-free core. The growth of alloy Au-AgNPs was monitored by UV–visible spectroscopy (UV–vis) and confirmed using various spectral, microscopy, and electrochemical techniques. The field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) results revealed that the covalent organic framework (COF) had a uniform flake-like morphology, and the alloy-based Au-AgNPs had a flower-like structure. The results of X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) indicated that Au-AgNPs are metallic in nature and highly crystalline. The surface of a glassy carbon electrode (GCE) was then modified with Au-AgNPs-COF, which was subsequently employed for enzyme-free electrochemical reduction of H2O2. The electrocatalytic cyclic voltammetry performance of the different modified electrodes was in the following order: COF (−14.82 μA) < AgNPs-COF (−26.95 μA) < AuNPs-COF (−31.78 μA) < Au-AgNPs-COF (−46.15 μA). The Au-AgNPs-COF/GCE displayed an excellent electrocatalytic activity toward reduction of H2O2, over a dynamic range of 2.0 nM–1.0 mM with a limit of detection (LOD) of 0.44 nM (S/N = 3). Furthermore, the present sensor showed appreciable selectivity, stability, and reproducibility against the reduction of H2O2. Practicality was demonstrated in fetal bovine serum (FBS), cat blood serum (CBS), and living cells (RAW 264.7).

A Blockchain-Based IoT Framework for Oil Field Remote Monitoring and Control

The oil and gas industry involves a high level of operational expenditure and often faces high risks of asset safety and operational failures. It has never been so important to monitor and control the oil field operations remotely in real-time to ensure safety and efficiency. The traditional monitoring and control systems for oil field operations are typically centralized, prone to failure, and lack efficiency. Blockchain technology mitigates the centralization problem by creating a decentralized, immutable and transparent control environment for automatic monitoring and control of industrial operations. In this study, we propose a blockchain-based IoT framework for real-time monitoring and control to increase oil field operation and asset efficiency and safety. We present the key components of the framework, including the system architecture, operation flows, algorithms, and smart contracts. As a proof-of-concept modeling, a smart contract is developed and validated on a blockchain test platform. A comparative analysis shows the advantages of using blockchain technology and smart contract to provide trustworthy and automatic monitoring and control for oil field operations.

A Real-Time Monitoring Framework for Wafer Fabrication Processes With Run-to-Run Variations

Real-time process monitoring is a vital technique to maintain safety and quality in the wafer fabrication processes. Hence, statistical process monitoring methods such as statistical process control chart, principal component analysis have been widely applied in wafer fabrication. However, these methods often suffer from the performance degradation problem caused by run-to-run (R2R) variations such as uneven duration and R2R drifts. In this paper, we propose a real-time monitoring framework for continuous wafer fabrication processes with uneven duration and R2R drifts. In this framework, processes are divided into several phases and further aligned in a real-time manner to solve the uneven duration problem. Then, R2R drifts are compensated by a down-sampling seasonal autoregressive integrated moving average model and a moving window multi-batch forecast strategy. Finally, multiphase multiway principal component analysis models are built on the compensated data to monitoring the processes. The efficiency of this framework is demonstrated by a case study on a plasma etch process. The results indicate that the proposed framework can diminish the influence of R2R variations and improve the monitoring model performance.

Detection, control and mitigation system for secure vehicular communication

The increase in the safety and privacy of automated vehicle drivers against hazardous cyber-attacks will lead to a considerable reduction in the number of global deaths and injuries. In this sense, the European Commission has focused attention on the security of communications in high-risk systems when receiving a cyber-attack such as automated vehicles. The project SerIoT comes up as an possible solution, providing a useful open and reference framework for real-time monitoring of the traffic exchanged through heterogeneous IoT platforms. This system is capable of recognize suspicious patterns, evaluate them and finally take mitigate actions. The paper presents a use case of the SerIoT project related to rerouting tests in vehicular communication. The goal is to ensure secure and reliable communication among Connected Intelligent Transportation Systems (C-ITS) components (vehicles, infrastructures, etc) using the SerIoT's system capabilities to detect and mitigate possible network attacks. Therefore, fleet management and smart intersection scenarios were chosen, where vehicles equipped with On Board Units (OBU) interact with each other and Road Side Units (RSU) to accomplish an optimal flow of traffic. These equipments use the SerIoT systems to deal with cyber-attacks such as Denial of Service (DoS). Tests have been validated in different scenarios under threats situations. It shows the great performance of the SerIoT system taking the corresponding actions to ensure a continuous and safety traffic flow.

A monitoring framework based on exergetic analysis for sustainability assessment of direct laser metal deposition process

With the constant increase of energy costs and environmental impacts, improving the process efficiency is considered a priority issue for the manufacturing field. A wide knowledge about materials, energy, machinery, and auxiliary equipment is required in order to optimize the overall performance of manufacturing processes. Sustainability needs to be assessed in order to find an optimal compromise between technical quality of products and environmental compatibility of processes. In this new Industry 4.0 era, innovative manufacturing technologies, as the additive manufacturing, are taking a predominant role. The aim of this work is to give an insight into how thermodynamic laws contribute at the same time to improve energy efficiency of manufacturing resources and to provide a methodological support to move towards a smart and sustainable additive process. In this context, a fundamental step is the proper design of a sensing and real-time monitoring framework of an additive manufacturing process. This framework should be based on an accurate modelling of the physical phenomena and technological aspects of the considered process, taking into account all the sustainability requirements. To this end, a thermodynamic model for the direct laser metal deposition (DLMD) process was proposed as a test case. Finally, an exergetic analysis was conducted on a prototype DLMD system to validate the effectiveness of an ad-hoc monitoring system and highlight the limitations of this process. What emerged is that the proposed framework provided significant advantages, since it represents a valuable approach for finding suitable process management strategies to identify sustainable solutions for innovative manufacturing procedures.

Framework for Real-Time Monitoring of Battery Performance in Electric Vehicles and Locating Charging Facilities Nearby

Electric vehicles (EVs) are becoming more prevalent in the market. In a world where all vehicles are electric, it will be necessary to provide infrastructure on average highways that is similar to the energy consumption of the automobiles on that highway. As a result, a large transition from one type of energy carrier to electrical energy would be required. In comparison to where we are now, this translates into a significant increase in the carrying capacity of the power grid. Electric vehicles are in increasing demand because they have several advantages over gasoline-powered vehicles. On the other hand, limited battery power stations and a lack of infrastructure giving real-time vehicle performance parameters such as battery efficiency, durability, total distance travelled before being totally depleted, and so on should be considered. As a result, an embedded system is being developed in our project to address the aforementioned restrictions. The suggested research demonstrates all of the mathematical calculations of battery characteristics (including but not limited to battery efficiency and percentage durability), which aids in evaluating real-time batter performance. The system was tested and verified in laboratories using acceptable methodologies. In addition, a mobile application for the end-user is being created to provide information on battery parameters as well as the maximum distance it can travel till full discharge.

Measurement Setup Based on Zynq SoC FPGA for Real Time Monitoring of Grid System Parameters

The present-day grid is dynamic with bidirectional power flow because of the integration of Distributed Generation Units with conventional power generation units. Because of the grid dynamics and complexity, real-time monitoring of the grid system parameters is a major challenge. Conventional phasor measurement units (PMU) based on Fast Fourier Transform (FFT) do not give an accurate estimate of grid parameters under dynamic varying conditions. This paper proposes a System-on-Chip compact framework for real-time monitoring of various grid parameters. The designed unit is adaptive and hence suited for dynamically varying conditions. Also, as the monitoring algorithm is implemented on SoC hardware, it gives real-time performance. The response time is slightly greater than three-fourth of a cycle, while measurement delay is less than three-fourth of a cycle. The Total Vector Error of the designed system is less than 1% during measurements and hence the unit conforms with M class requirements of the IEEE standard on synchrophasor measurements. The monitoring unit occupies less than 45% of the programmable fabric of the SoC FPGA and is power efficient. It can adapt to any grid voltage and current levels and can be used in real-time SoC PMU.

Mobility-based real-time economic monitoring amid the COVID-19 pandemic

Mobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries’ economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities.