Real-time Monitoring System Research Articles

Smart Building Application in Revitalization of Historic Buildings Case Study: The Museum Bahari, Jakarta

Historical buildings are cultural heritages that require preservation while adapting to modern demands. The Museum Bahari in Jakarta faces challenges in revitalizing its historical architectural elements while integrating adaptive technologies. This research examines the application of smart building systems in revitalizing the museum with minimal intervention to maintain historical integrity. A descriptive qualitative method is used, involving condition analysis, identification of smart technologies, and strategy development. Data collection includes observation, literature review, and case study analysis. Findings indicate that technologies such as humidity sensors, adaptive lighting, and environmental monitoring can enhance building performance and visitor experience without compromising heritage values. This study recommends placing devices in non-structural areas, integrating energy management with conservation needs, and using real-time monitoring systems. These strategies align with heritage conservation principles. The study concludes that smart building technology with minimal intervention offers a sustainable solution for revitalizing historic buildings like the Museum Bahari while enhancing functionality and relevance in the modern era.

New online beam intensity synchronous monitoring system in scanning transmission X-ray microscopy.

The scanning transmission X-ray microscopy (STXM) platform based on synchrotron radiation has achieved nanoscale imaging with chemical sensitivity using spectro-microscopy techniques. However, the quality of STXM imaging is affected by the stability of the beam intensity. The top-up operation mode of synchrotrons to maintain a constant electron beam intensity introduces periodic fluctuations in the X-ray beam intensity, leading to notable imaging noise that decreases both contrast and precision. To address this issue, a high-speed real-time beam intensity monitoring system was designed and implemented at the BL08U1A beamline of the Shanghai Synchrotron Radiation Facility. This system utilizes an yttrium-aluminium-garnet crystal along with dual detectors having an acquisition frequency of up to 1 MHz and a synchronization error of less than 20 ns between them. This system can precisely and synchronously monitor the X-ray beam intensity variations which are used to remove noise due to electron injection from STXM images, thereby markedly improving the quality of STXM imaging.

Prioritising elements of digitalisation for lean and green SME operations: an ISM-MICMAC study in the Indian context

PurposeThe purpose of this paper is to identify the key elements of digitalization for lean and green operations and develop a conceptual framework for their implementation. The paper focuses on small and medium-sized enterprises (SMEs) and aims to explore the role of digitalisation in enhancing their operational efficiency and sustainability. By identifying key factors and metrics related to digitalisation, the paper seeks to provide insights for strategic management to improve lean and green practices in SMEs.Design/methodology/approachInterpretive structural modeling (ISM) and Matrix of Cross-Impact Multiplication Applied to a Classification (MICMAC) approaches were deployed to classify the major dimensions of digitalisation. These methods were used to analyse the direct and indirect relationships among the identified elements of digitalisation. A comprehensive literature review and expert consultations were conducted to identify 13 key elements relevant to lean and green operations. The experts also assisted in determining the contextual relationships between the variables for the ISM model.FindingsThe analysis classified the 13 identified elements of digitalisation into different levels according to their driving power and dependence. The results from the ISM model indicated three levels of classifications. At level-1, Internet of things (IoT) and smart sensors (IoT & SS), automation and robotics directly influence lean and green operations. At level-2, real-time monitoring and control system and at level-3 fundamental elements of digitalisation such as big data analytics, predictive maintenance, cloud computing, energy management systems (EMSs), additive manufacturing, blockchain, digital workflow automation and digital collaboration platform.Originality/valueAll elements are interrelated and essential for making strategic decisions. This study emphasis the significance of prioritising these attributes to attain long-term excellence through digitalisation. For the industries that seek the reward of lean and green operations for their growth, this paper has great practical utility. Identifying the key factors of digitalisation would help strategic managers in handling lean and green environment of SMEs through these aspects.

The Role of Artificial Intelligence (AI) and Machine Learning (Ml) in the Oil and Gas Industry

Purpose: This study focused on the relevance of AI and ML in revolutionizing the Oil & Gas sector by innovation and rehabilitation. It investigated the role of AI and ML technologies in improving production efficiency, reducing environmental impact, and managing costs. Several end users noted considerable advancements in operation efficiency; Predictive analytics and real-time monitoring systems assisted in enhancing the effectiveness of predictive maintenance by as much as 40% lapse time. Thus, the digital twin technologies were discussed in the context of enhancing the design of production planning, as well as promoting more effective use of resources and their recycling. Methodology: The research applied integration of systematic qualitative methodologies to collect, analyze, and synthesize data from prior investigations. This methodology was designed to encompass the alignment of results, analysis, and conclusions within the literature findings. The application of Systematic Literature Review (SLR), Content Analysis, and Meta-Analysis of Qualitative Evidence effectively grounded the study objectives Findings: The Significance of AI is captured in the environmental management aspect of this study, whereby several companies’ emission control systems recorded a 30% improvement of Green House Gas (GHG), and the accuracy of compliance. Some of those that are of more economic advantage are the reduced cost of maintenance, low energy utilization and minimal wastage of resources. However, shortcomings like high implementation cost, integration difficulties, and infrastructural limitations remain some of the biggest threats to its adoption. Unique Contribution to Theory, Policy, and Practice: In addressing these challenges, this research suggests the promotion of partnerships, creating efficient innovations and establishing sustainable development initiatives. Thus, it offers valuable recommendations for policymakers, researchers, and other interested parties, stressing that AI and ML adoption demonstrate the potential to support operational excellence, environmentally sustainable practices, and increased profitability in the Oil and Gas industry.

Real-time paddy grain drying and monitoring system using long range-internet of things

Grain drying environmental parameters are an important issue throughout the paddy grain production process. A real-time monitoring system requires rapid, online, and accurate measurement results. In the paddy grain drying process, the heated air velocity, temperature, relative humidity, and moisture content have to be carefully monitored and maintained to ensure product quality and safety. This study aimed to propose a real-time paddy grain drying and monitoring system using a long-range internet of things (LoRa-IoT). The real-time monitoring system consisted of sensors, LoRa, and IoT platforms. The LoRa end node and gateway were utilized as a wireless radio communication platform of IoT for long-distance signal transmission. From the experiment, the gateway received data from the end node at a distance of 2 km with a time on air (ToA) of 981 ms. As a result, the proposed monitoring system succeeded in measuring and recording the heated air velocity, temperature, and relative humidity data during the paddy grain drying process from 25% moisture content down to 14%. Regarding moisture content, the accuracy of real-time monitoring information was confirmed with a direct measurement method, resulting in a root mean square error (RMSE) of 6.17%.

All-in-one self-powered wind speed sensor with a wide start-up range and high output power

Anemometers play an important role in environmental monitoring in remote and unique locations, such as forests, islands, and mines. Self-powered wind speed sensors offer a solution for long-term reliable monitoring in unmanned environments. However, current self-powered wind speed sensors suffer from limited integration, limited start-up range, and insufficient output power. Therefore, an all-in-one self-powered wind speed sensor (ASWS-sensor) with a wide start-up range and high output power is proposed. The ASWS-sensor, based on triboelectric nanogenerator and electromagnetic generation technologies, features a unique dual-cup structure that integrates wind speed sensing and wind energy harvesting. This design enables wind speed detection across a broad range from 1.5 to 15 m/s and achieves a high output power of 1.18 W. To meet the long-term, reliable wind speed monitoring needs in coal mine tunnels, a real-time wind speed monitoring and alarm system is developed combining the ASWS-sensor and a master computer. Notably, in this system, the use of the FIR filtering algorithm effectively suppresses significant noise encountered during the collection of single-electrode triboelectric signals with a microcontroller, thereby simplifying the wind speed calculation process. The results show that when the wind speed exceeds 7 m/s, the system can collect, process, wirelessly transmit, analyze, and display wind speed-related data without the need for an external power supply. This demonstrates the excellent application potential of the ASWS-sensor in unmanned monitoring in remote and unique scenarios.

A real time monitoring system for accurate plant leaves disease detection using deep learning

A real time monitoring system for accurate plant leaves disease detection using deep learning

Metal purity detection using non-target reflectivity plastic optical fiber displacement sensor

A fiber optic displacement sensor (FODS) that uses a fiber probe to detect metal purity based on intensity modulation technique is proposed and demonstrated. A He-Ne laser is used as the light source and three different types of metals which consists of nickel brass, cupronickel, and copper clad-steel are used as the target samples. It is observed that the voltages increase as the metal brightness changes from dark to bright, which consequently depends on the amount of nickel content in the metal. The highest sensitivity of 1.0459 for the front slope is achieved by nickel brass, thus making it useful for close-distance targets. The proposed system is environment friendly and low cost, with additional advantages of high sensitivity and easy installation. Besides its ability to determine the type of metal sample, this FODS can provide a real-time monitoring system suitable for broader applications in sensor field.

Battery Sentiment Analysis and Recommention System

- The increasing reliance on battery technology across industries such as renewable energy, electric vehicles, and portable electronics underscores the importance of robust analysis and optimization of battery systems. Battery-centric analysis focuses on understanding key parameters such as temperature, current, voltage, state of charge (SOC), and state of health (SOH) to enhance performance, safety, and longevity. These parameters are critical in identifying potential failures, improving efficiency, and predicting battery life cycles. This study employs advanced methodologies, including machine learning algorithms such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN), to analyse battery performance data. By training and testing these models on extensive datasets, the research provides accurate predictions of battery behavior under diverse operating conditions. Key metrics, including accuracy, root mean square error (RMSE), and mean absolute error (MAE), are used to evaluate the effectiveness of these algorithms, ensuring reliable insights into battery health and operation. The integration of real-time monitoring systems with predictive analytics enhances the safety and efficiency of batteries, reducing risks such as overheating, overcharging, and premature failure. Additionally, this approach supports the development of sustainable energy solutions by identifying areas for material optimization and improving the design of energy storage systems. Key Words: Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN).

Developing Cost-Effective AI Algorithms for Resource-Constrained Devices

Resource-restrained gadgets pose sizable challenges for deploying synthetic intelligence (AI) packages, which include restricted computational power, reminiscence, and electricity resources. This research pursuits to broaden value-effective AI algorithms that cope with these limitations whilst retaining high overall performance and accuracy. The examine leverages superior optimization strategies, such as version pruning, quantization, and dynamic strength control, to layout light-weight models appropriate for low-strength environments. Experiments conducted on gadgets like the Raspberry Pi 4 and NVIDIA Jetson Nano screen giant improvements in inference time, electricity efficiency, and accuracy compared to conventional processes. The proposed algorithms acquire up to 50% reduction in energy consumption and 20% improvement in accuracy at the same time as lowering typical computational charges. These findings reveal the feasibility of deploying green AI solutions on constrained hardware without compromising on functionality or nice. The practical implications of this paintings make bigger to various applications, along with real-time healthcare monitoring, clever agriculture, and commercial IoT systems. The have a look at concludes by means of highlighting areas for destiny studies, which includes improving algorithmic adaptability and expanding trying out to embody diverse eventualities. This work gives a sturdy basis for advancing the deployment of AI in resource-restricted settings, bridging the gap between technological innovation and practical implementation.

Worker Safety Monitoring System in the Telecommunications Industry Based on the Internet of Things

Occupational Health and Safety (OHS) is a protective measure aimed at ensuring workers remain safe and healthy while performing their duties in the workplace. OHS planning aims to create a comprehensive occupational safety and health management system within the workers' scope. The telecommunications industry is one of the sectors that implements OHS principles. Workers in the telecommunications industry face higher occupational accident risks due to direct interaction with heavy objects, sharp materials, electricity, and working at heights. This research aims to design a smart helmet prototype integrated with the Internet of Things (IoT) to monitor safety for telecommunications industry workers. The system to be developed consists of a PPE (Personal Protective Equipment) helmet equipped with an IoT system. The PPE helmet is integrated with an ESP32 microcontroller, LDR sensor, gyroscope sensor, and GPS module, utilizing wireless transmission media connected to an IoT Cloud server that links the microcontroller system with users. The designed system can monitor whether workers are wearing helmets properly, and monitoring results are transmitted in real-time via Telegram, enabling early warnings if potential hazards threaten workers. The research will be tested at PT. Mitra Globalindo Nusantara, a company engaged in fiber optic telecommunications construction. From the discussion results, it was found that the IoT-based safety monitoring system for telecommunications industry workers using LDR sensors, gyroscope sensors, GPS modules, and Telegram monitoring was successfully designed and implemented as a well-functioning prototype. The Telegram application successfully sends notifications regarding the worker's safety condition, including alerts when the helmet is not worn, tilted, or has fallen. The response time for sending and receiving notifications via Telegram was recorded between 1.5 to 2.2 seconds, which is considered acceptable for real-time monitoring systems.

Energy-efficient Methods for Controlling Biofouling in Water Treatment Membranes and Future Directions

This paper aims to address the critical issue of biofouling in water treatment membrane systems, which decreases operational efficiency and increases energy consumption. The study explores energy-efficient biofouling control methods, focusing on established and emerging technologies. The review examines various approaches, including surface modification, antimicrobial nanomaterials, and photocatalytic membranes. Techniques using zwitterionic polymers, amphiphilic coatings, silver nanoparticles (nAg), and nanodiamonds (UDD) are analyzed for their effectiveness in mitigating biofouling. Photocatalytic membranes employing Metal-Organic Frameworks (MOFs) are also evaluated for their sustainable microbial inhibition capabilities. Surface modification techniques demonstrated the potential to alter membrane hydrophilicity, effectively preventing microbial adhesion and biofilm formation. Incorporating antimicrobial nanomaterials such as nAg and UDD disrupted microbial cell membranes and enhanced hydrophilicity, significantly increasing biofouling resistance. Photocatalytic membranes utilizing MOFs produced reactive oxygen species (ROS) under light exposure, providing a sustainable and energy-efficient approach to biofouling control. However, the integration of real-time monitoring systems and AI-based predictive models remains necessary to further optimize membrane performance and reduce energy consumption. The development of multifunctional membranes that combine biofouling resistance with resource recovery capabilities is essential to tackle the global water crisis and ensure access to clean water. Continued research in integrating advanced technologies such as AI and sustainable materials will be pivotal in advancing energy-efficient biofouling control.

IMPLEMENTASI BIG DATA ANALYTICS DALAM KLASIFIKASI KUALITAS UDARA MENGGUNAKAN ALGORITMA GRADIENT-BOOSTED TREE CLASSIFIER PADA PYSPARK

This study aims to classify air quality based on PM1.0, PM2.5, and PM10 parameters using a Big Data Analytics approach with the Gradient-Boosted Tree Classifier (GBT) algorithm implemented on the PySpark framework. The dataset used was downloaded from OpenAQ, covering the period from April 14, 2021, to April 16, 2023, with a total of 1,048,154 entries, representing a large and complex volume of data. The research process includes data preprocessing to address data imbalance, dataset splitting for training and testing, and hyperparameter tuning using grid search and cross-validation to optimize model performance. By leveraging PySpark’s advantage in parallel processing of large data, the GBT model achieved an accuracy of 98.87%, precision of 99.00%, recall of 98.87%, and an F1-Score of 98.90%. This study demonstrates how Big Data Analytics can enhance efficiency and accuracy in air quality classification, contributing significantly to the development of real-time monitoring systems that support air pollution mitigation and data-driven policy-making.

Combatting fraud in real-time payments: Strategies and technologies for securing instant payment systems

This comprehensive article explores the evolution and security challenges of real-time payment systems in the modern financial landscape. The article examines advanced fraud prevention strategies, including machine learning algorithms, artificial intelligence implementation, and real-time monitoring systems. It explores the transformation of Know Your Customer (KYC) processes through AI-powered identity verification and discusses the critical balance between security measures and user experience. The article further delves into emerging technologies such as quantum-resistant cryptography and edge computing, highlighting their role in shaping the future of payment security. The article demonstrates how financial institutions are adapting to new threats while maintaining transaction efficiency and regulatory compliance.

Design and Implementation of a Low-cost IoT based real-time emission monitoring system for a Thermoelectric Generator-integrated Biomass Cookstove

Design and Implementation of a Low-cost IoT based real-time emission monitoring system for a Thermoelectric Generator-integrated Biomass Cookstove

Banking Frauds and Their Impact: Harnessing Technology for Early Detection

Fraud in the banking sector poses significant risks, including financial losses, operational disruptions, increased compliance costs, and reputational damage, particularly in India’s rapidly digitalizing financial landscape. This research examines the impact of fraud on banks and highlights the role of advanced technologies like artificial intelligence (AI), machine learning (ML), and big data analytics in mitigating these risks. By leveraging early warning systems for real-time monitoring, predictive analytics, and automated fraud detection, banks can enhance their resilience against evolving threats. The study offers actionable insights through case studies and industry practices to strengthen fraud management frameworks, safeguard assets, and rebuild customer trust in an increasingly digital environment.

Concentration of traffic air pollutants and influencing metrological factors in Hawassa City roadways, Ethiopia

IntroductionThe traffic air pollution caused by transportation is a growing global problem that contributes to millions of deaths each year. Despite its importance, information on pollutant concentration is limited in many developing cities, especially in Ethiopia. This study aimed to determine the concentration levels and spatial and temporal variations of traffic air pollutants in Hawassa and to investigate the influence of metrological parameters on the concentration of traffic air pollutants.MethodsA real-time monitoring system of Aero-Qual Series 300/500 was used to monitor pollutants, and 24 monitoring sites were included on both heavy and low-traffic volume roads. The study monitored morning and afternoon times over 24 days to comprehensively characterize the temporal variations.ResultsThe results showed that the mean PM2.5 concentration on heavy- and low-traffic volume roads was 161.6 ± 26.1 μg/m3 and 95 ± 14.2 μg/m3, respectively, whereas the PM10 concentration was 178.7 ± 20.3 μg/ m3 and 102.3 ± 17.6 μg/m3, respectively. Similarly, the mean NO2 concentrations on roads with heavy and low traffic volumes were 86.4 ± 14.4 μg/m3 and 61.7 ± 14.2 μg/m3, respectively. Significantly higher, concentrations were recorded on traffic light roads, followed by main asphalt roads, for both types of traffic air pollutants. The ratio of PM2.5/PM10 was higher (0.924), in which the pollution sources attributed to anthropogenic sources. Kendall’s tau-b correlation analysis suggested that Meteorological parameters (temperature and relative humidity) were positively correlated with traffic air pollutants. Likewise, stepwise multiple linear regression analysis confirms that the concentrations of traffic air pollutants had a positive relationship with metrological parameters.ImplicationsThe findings of this study therefore showed the need for regular air quality monitoring of the urban areas to copping out the adverse public health impacts. And, it highlighted an urgent need for long-term monitoring of traffic air pollution and the development of emission control programs that can be readily implemented to decrease the emissions from anthropogenic sources. Also, it brings a sense of collaboration among stakeholders to tackle the effects of air pollution by providing an inclusive and sustainable development agenda for Hawassa.

Development of Maintenance Management System for Real-Time Monitoring of Power Transformer to Improve Availability Performance: The Case of Tagamenda TANESCO Grid Substation

Power transformers are critical assets in TANESCO's grid substations, playing a vital role in ensuring a reliable and uninterrupted power supply. However, the growing challenges of ageing infrastructure, increasing energy demand, and reactive maintenance practices often lead to unplanned outages, higher operational costs, and reduced transformer availability. This dissertation focuses on the Development of a Maintenance Management System for Real-Time Monitoring of Power Transformers at TANESCO grid substations, with the goal of improving availability performance. The study explores the design and implementation of a Real-Time Monitoring Management System (RTMMS), leveraging advanced sensors, data analytics, and predictive maintenance techniques. The RTMMS continuously monitors critical parameters such as temperature, oil levels, dissolved gases, and load conditions, providing real-time insights into transformer health. By integrating predictive analytics, the system identifies potential faults early, enabling timely interventions and reducing downtime. Additionally, it supports data-driven maintenance planning, enhances operational reliability, and extends transformer lifespan. The proposed system addresses challenges such as high initial costs, data management complexity, and resistance to technological change through phased deployment, secure cloud solutions, and comprehensive training programs. The expected benefits include improved transformer availability, cost efficiency, enhanced grid sustainability, and a shift from reactive to proactive maintenance practices. This research underscores the transformative potential of real-time monitoring systems in modernizing maintenance management and aligns TANESCO with global best practices in energy sector innovation and operational excellence.

Identification of gas outburst precursors based on outburst percolation theory

Uniaxial compression experiments were conducted on coal rock utilizing a computed tomography (CT) scanning system for real-time monitoring to explain the issue of gas volume significantly exceeding reservoir capacity during coal and gas outbursts. A percolation factor a which can make a significant contribution to the research on premonitory information of gas outbursts is introduced to determine whether percolation occurs in coal rock, and supports the outburst percolation theory. It was found that percolation probability and correlation length increase with greater porosity, and that the number of pore clusters decreases as porosity increases. All three parameters exhibit explosive increases or decreases before the porosity reaches the percolation threshold. The percolation threshold for the coal varies at different stress levels; as the coal approaches its stress limit, the percolation threshold decreases, making percolation transformation more likely to occur and increasing the risk of a coal and gas outburst. When a > 1, percolation transformation occurs within the coal, and large-scale gas migration channels are formed. This stage completes the latent phase of the outburst, significantly elevating the likelihood of an outburst incident. This approach establishes quantitative relationships between porosity and various percolation parameters at different stress levels. It contributes to the research on precursory information of coal and gas outbursts.

Optimization and sensitivity analysis for developing a real-time non-contact physiological parameters measurement and monitoring system using IPPG signal for biomedical applications

Optimization and sensitivity analysis for developing a real-time non-contact physiological parameters measurement and monitoring system using IPPG signal for biomedical applications