Advanced Monitoring System Research Articles

PLC based laser scanning system for conveyor belt surface monitoring

This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system’s scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety.

AI-Driven Incident Management in Retail : A Case Study

This article examines the implementation of AI-driven incident management in a major U.S. retail corporation (Walmart) with annual revenues exceeding $500 billion. The company faced significant operational challenges, including high cart abandonment rates (69%), frequent system downtimes causing $3.5 million losses per incident, and slow incident response times with MTTR averaging 4 hours. Through the implementation of comprehensive AI solutions, including advanced monitoring systems processing over 1 terabyte of log data daily, anomaly detection with 95% accuracy, and automated incident management resolving 60% of issues without human intervention, the company achieved remarkable improvements. Key results include a 40% reduction in downtime, a 20% decrease in cart abandonment rates (recovering $3.6 billion in potential lost sales), and an 81.25% improvement in Mean Time to Resolution (MTTR). The article also reveals broader economic impacts, including creating 5,000 new jobs in the AI and ML fields, a 15% improvement in sector-wide operational efficiency, and a $180 billion contribution to U.S. GDP over three years. The successful implementation demonstrates the transformative potential of AI in retail operations while providing valuable insights for organizations seeking to enhance their incident management capabilities through technological innovation.

Assessing Crisis Management Tools for Sustainability of Industrial Safety

This study presents a comprehensive risk assessment of ammonia leaks, focusing on the quantitative modelling of hazardous area ranges, concentration dynamics, and thermal radiation effects under varying leakage scenarios using the ALOHA 5.4.7 software. The analysis involves two key scenarios: an ammonia gas leak and a pool fire, each modelled under distinct atmospheric conditions. For the gas leak scenario, ammonia concentrations were mapped across ERPG-defined hazard zones, ranging from low-level irritation zones (ERPG-1) to life-threatening exposure levels (ERPG-3), with maximum concentrations reaching 1500 ppm within a 110 m radius. The second scenario examined the impact of thermal radiation from a pool fire, identifying critical radiation zones where exposure to heat fluxes exceeding 10 kW.m−2 could cause fatal outcomes within 12 m. Despite ALOHA’s strengths in modelling acute exposure risks and providing valuable input for emergency response planning, the study identifies several limitations, particularly regarding the long-term environmental and health impacts of chemical releases and the effects of varying meteorological conditions. These findings suggest that integrating ALOHA with advanced real-time monitoring and AI-based prediction systems could significantly improve its capacity to manage dynamic, rapidly evolving industrial hazards.

Advanced image processing techniques for multi-level characterization of significant flame features in carbon-neutral combustion

This research presents an advanced image processing framework designed for the multi-level characterization of significant flame features in carbon-neutral combustion systems. The study introduces an optimized neural network based on a brainstorming algorithm and an enhanced support vector machine (SVM) algorithm, which utilizes an improved particle swarm optimization (PSO) technique. The framework is capable of accurately processing complex flame image data, achieving flame classification accuracy rates of up to 100 %. The proposed system combines detailed analysis of flame brightness, texture, and morphological features, contributing to the comprehensive monitoring of combustion states. Additionally, the paper describes the design of a clean burner that promotes the full combustion of zero-carbon gases. The integration of this burner with the advanced monitoring system significantly enhances safety and efficiency in industrial applications. This work addresses the challenges associated with the transition to renewable gases and provides a robust solution for maintaining stable and efficient combustion processes.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Achieving Air Pollution Control Targets with Technology‑Aided Monitoring: Better Enforcement or Localized Efforts?

Weak enforcement of environmental regulations remains a global issue due to inadequate monitoring and misaligned incentives. This paper examines the effects of automated monitoring on achieving air pollution control targets amidst China’s war on pollution. Utilizing the staggered rollout process and remote-sensing data, we find local governments respond to the advanced monitoring system by strategically targeting areas near monitors, resulting in a 3.2 percent decrease in pollution adjacent to automated monitors compared to areas farther away. Furthermore, we observe heterogeneity in response across cities with varying degrees of preexisting data manipulation and among officials facing different incentives and public pressure. (JEL O13, O18, P25, P28, Q53, Q55, R11)

Risk Typologies and Their Mitigation in Banking: A Study of Azerbaijani Banks

Abstract This paper explores the typologies of risks in the banking sector and examines the strategies employed to mitigate them, with a focus on Azerbaijani banks. The study identifies key risk categories, including credit risk, market risk, liquidity risk, and operational risk, each of which plays a critical role in the financial health and profitability of banks. The research highlights that credit risk, driven by loan defaults, remains the most significant concern for Azerbaijani banks, while market risks, particularly from currency fluctuations, also pose major challenges. Effective risk mitigation strategies, including collateralization and diversified lending practices, are essential to maintaining stability in this emerging market. Additionally, the study emphasizes the importance of integrating modern risk management technologies and adhering to international regulatory standards. Azerbaijani banks that adopt advanced monitoring systems and proactive risk management frameworks demonstrate improved financial performance and resilience in the face of economic volatility. The findings underscore that well-implemented risk management practices are crucial for enhancing profitability and ensuring long-term sustainability in the banking sector.

Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin.

The Yellow River, China's second-largest river, is renowned for its high sediment content. In response to the potential impacts of climate change on Yellow River water resources and water environmental management, an advanced monitoring and forecasting system for water and sediment throughout the entire Yellow River basin-from its source to the sea-is urgently needed. In this paper, based on the current status of water and sediment monitoring technologies, we proposed an integrated remote sensing monitoring network that combines satellite remote sensing, drone remote sensing, and ground-based wireless automatic monitoring networks, aiming to achieve the digital monitoring of water and sediment across the entire Yellow River basin, from its upper reaches to its estuary in the Bohai Sea. By utilizing ground-based in situ hyperspectral stations for sediment source areas in the upper reaches, such as the Qingshui River basin in Ningxia, and satellite remote sensing for midstream processes in the Xiaolangdi reservoir before the flood season in 2023, as well as downstream monitoring at the Yellow River estuary, this paper demonstrates the novelty and efficiency of the space-air-ground integrated remote sensing monitoring technology.

E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring.

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

Advanced power quality monitoring with dilated convolutional neural networks for smart nanogrids

This study presents the development of an advanced power quality monitoring (PQM) system using dilated convolutional deep neural networks (DCDNN) for smart nanogrids. Traditional convolutional neural networks (CNNs) face challenges in efficiently handling high-dimensional data and identifying nuanced features due to their limited receptive field. To overcome these limitations, our DCDNN model employs a novel use of dilated convolutions to expand the receptive field without increasing network complexity, enabling the capture of long-range dependencies essential for accurate classification of power quality disturbances (PQDs). In addition, the integration of multi-channel time-series data processing, including voltage, current, and frequency measurements, is unique to our approach. The model processes multi-channel time-series data, including voltage, current, and frequency measurements, achieving an average accuracy of 98.9% across 29 PQD classes and 100% accuracy in 6 main classes, with a reduced detection time of 47 µs per segment. This approach not only improves real-time disturbance detection but also enhances the reliability and efficiency of electrical systems. The results demonstrate significant improvements over existing methods, underscoring the potential of DCDNNs in advancing PQM practices. Future work will focus on validating the model in real-world conditions and optimizing its computational efficiency.

Design and Implementation of an IoT-Based Intravenous Infusion Monitoring System Using Wireless Sensor Network

This research presents a Wireless Sensor Network (WSN) based infusion monitoring system that utilizes Internet of Things (IoT) technology to enhance infusion management in healthcare. Manual supervision often lacks the necessary accuracy and control, making automated solutions crucial. Our system employs optocoupler and load cell sensors to calculate the infusion drop rate per minute and assess the remaining fluid percentage. Tests conducted demonstrated that optocoupler sensors effectively measured the drop rate, while load cell sensors accurately determined fluid levels. Measurement results are easily monitored via smartphone, granting healthcare professionals rapid access to vital data. A key innovation of this system is its ability to enable remote monitoring through WSN, allowing a single nurse to oversee multiple infusions with just one smartphone. The test results indicate impressive accuracy, achieving an average precision rate of 97% for drop rate measurements and 93% for remaining fluid percentage measurements. Overall, this system offers an efficient and controlled approach to infusion fluid management, ensuring optimal care for patients. By integrating IoT and WSN technologies, this research lays the groundwork for developing more advanced and interconnected infusion monitoring systems, ultimately enhancing patient care in today's globalized healthcare environment.

REAL-TIME DETECTION AND CLASSIFICATION OF FISH IN UNDERWATER ENVIRONMENT USING YOLOV5: A COMPARATIVE STUDY OF DEEP LEARNING ARCHITECTURES

This article explores techniques for the detection and classification of fish as an integral part of underwater environmental monitoring systems. Employing an innovative approach, the study focuses on developing real-time methods for high-precision fish detection and classification. The implementation of cutting-edge technologies, such as YOLO (You Only Look Once) V5, forms the basis for an efficient and responsive system. The study also evaluates various approaches in the context of deep learning to compare the performance and accuracy of fish detection and classification. The results of this research are expected to contribute to the development of more advanced and effective aquatic monitoring systems for understanding underwater ecosystems and conservation efforts.

A Multifaceted AI-Driven Radio Network

This paper discusses a unique and revolutionary type of radio network which is driven by various AI techniques. Besides the traditional features of any FM radio network, Radio Logos has incorporated into its system a variety of automated features like weather forecasts, earthquake announcements, monetary exchange rates, listener song requester capability, and the monitoring of equipment. In addition, an advanced real-time defect monitoring system for RF signals has been developed and implemented to monitor the network 24/7. These methodologies are based upon AI techniques ranging from the simple to the advanced.

Integrating radiological technology in environmental health surveillance to enhance public safety

The integration of radiological technology into environmental health surveillance represents a significant advancement in enhancing public safety. Radiological technologies, including advanced imaging and monitoring systems, offer powerful tools for detecting and assessing environmental hazards that impact public health. This review explores the potential benefits, methodologies, and challenges associated with integrating radiological technology into environmental health surveillance. Recent advancements in radiological technology, such as high-resolution imaging systems and portable radiation detectors, have greatly improved the ability to monitor and analyze environmental radiation levels with precision. These technologies enable real-time detection of radiation exposure, providing critical data for assessing risks associated with radiation sources, such as industrial activities, nuclear power plants, and natural radioactive materials. By integrating these tools into environmental health surveillance frameworks, public health agencies can enhance their capacity to identify and address radiation-related hazards more effectively. The application of radiological technology in environmental health surveillance facilitates comprehensive monitoring of radiation levels across various environments, including urban, rural, and industrial areas. This data is crucial for identifying contamination hotspots, tracking changes in radiation levels over time, and evaluating the effectiveness of radiation control measures. Additionally, the integration of radiological technology supports the development of predictive models for assessing potential health impacts and guiding emergency response strategies in the event of radiation incidents. Despite its advantages, integrating radiological technology into environmental health surveillance presents several challenges. These include the high costs of advanced equipment, the need for specialized training for personnel, and the management of large volumes of data generated by monitoring systems. Addressing these challenges requires a coordinated approach involving government agencies, research institutions, and industry stakeholders to ensure effective implementation and sustainability. In conclusion, integrating radiological technology into environmental health surveillance has the potential to significantly enhance public safety by providing accurate and timely data on radiation exposure. Continued innovation and collaboration in this field will be essential for optimizing the benefits of radiological technology and addressing the challenges associated with its integration into environmental health monitoring systems. Keywords: Integrating; Radiological Technology; Environmental Health Surveillance; Enhance; Public Safety

Comparative analysis of data-driven electric vehicle battery health models across different operating conditions

The work covers the development of a data-driven algorithm and computes the performance of learning models for lithium-ion battery state of health (SOH) estimation. A wide range of environmental and temperature conditions (15 °C, 25 °C, and 35 °C) at different charging and discharging rates of 1C and 2C are used for electric vehicle battery health estimation. The result of the tested data of cell ‘a’ is validated with a different set of cell ‘b’ on identical test parameters, and the results are tabulated and compared. At 25 °C, the mean absolute errors for the regression algorithms decision tree (DT), k-nearest neighbor (KNN), and random forest (RF) are 3.78641E-03, 3.62524E-03, and 6.16931E-03. The mean absolute percent error for regression algorithms DT, KNN, and RF is 1.48921E-03, 1.40631E-03, and 2.40260E-03. The root mean square error for regression algorithms DT, KNN, and RF is 1.26813E-02, 9.73320E-03, and 1.17238E-02, and the mean squared error for regression algorithms DT, KNN, and RF is 1.60816E-04, 9.47351E-05, and 1.37448E-04. The results show that the KNN and DT methods accurately estimate the SOH under diversified operating conditions in comparison with RF methods and can foster advanced battery health monitoring systems.

Multi-Sensor Platform in Precision Livestock Farming for Air Quality Measurement Based on Open-Source Tools

Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors.

The Effects of Seismic Behavior on High Ground Stress Soft Rock Tunnel: A Review

The purpose of this review is to critically assess seismic activity's effects on soft rock tunnels under high ground stress scenarios. The paper seeks to identify key novelties and research gaps in the existing literature, offering new insights into analytical techniques, excavation methods, support systems, and monitoring technologies. A comprehensive review of recent studies was conducted, focusing on seismic behavior, analytical techniques, and mitigation strategies for soft rock tunnels. Case studies were selected based on their relevance to high ground stress conditions and their contribution to understanding seismic resilience. Significant findings include the identification of specific geological conditions that exacerbate seismic risks and the comparative effectiveness of various analytical techniques and support systems. Novel insights into the interaction of structural reinforcements and monitoring systems are also discussed. The review highlights new analytical techniques and advanced monitoring systems that improve predictive accuracy and early detection of seismic risks. It also proposes a refined approach to integrating mitigation strategies for enhanced tunnel resilience. Doi: 10.28991/CEJ-2024-010-09-020 Full Text: PDF

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

Sourcing renewable energy components: building resilient supply chains, reducing dependence on foreign suppliers, and enhancing energy security

The transition to renewable energy is essential for mitigating climate change and ensuring long-term energy security. As global demand for renewable energy technologies grows, establishing resilient supply chains becomes crucial. This paper examines strategies to build robust supply chains, reduce reliance on foreign suppliers, and enhance energy security. A major challenge in renewable energy is the dependence on a limited number of international suppliers, which risks supply disruptions due to geopolitical tensions, trade restrictions, or natural disasters. Diversifying suppliers can mitigate these risks by encouraging alternative suppliers and fostering competition (Smith, 2020). Local manufacturing is another critical strategy, promoting supply chain resilience, economic growth, and job creation. Countries like the United States and Germany have incentivized local manufacturing of solar panels and wind turbine components (Jones, 2021), ensuring a steady supply of parts and aligning with broader industrial policies. Maintaining strategic reserves of critical materials, such as rare earth elements and lithium, is essential for buffering against supply disruptions and price volatility, ensuring continuous production of renewable energy technologies (Brown, 2019). Strategic planning and investment in reserves are vital for a comprehensive energy security strategy. Technological innovation enhances supply chain resilience. Advances in materials science, manufacturing processes, and recycling technologies can reduce dependence on scarce materials and improve renewable energy system efficiency. Developing alternative materials for battery storage, for example, can reduce reliance on lithium and cobalt sourced from unstable regions (White, 2022). Public-private partnerships (PPPs) are crucial for advancing the renewable energy sector. By leveraging both sectors' strengths, PPPs can accelerate technology deployment, improve infrastructure, and foster innovation. Successful examples include government-private collaborations on offshore wind farms and smart grid technologies (Green & Associates, 2021). Favorable trade policies facilitate the global exchange of renewable energy technologies and components. Reducing tariffs, streamlining customs, and fostering international cooperation can enhance supply chain efficiency and reliability (Lee, 2020). This study advocates for trade agreements tailored to the renewable energy sector's needs. Integrated energy systems and smart grid technologies optimize renewable energy use and distribution. Advanced monitoring and control systems improve grid stability, reduce energy waste, and enhance renewable source integration (Adams, 2019). Investment in smart grid infrastructure is crucial for a resilient energy strategy. Robust policy frameworks are necessary to support these initiatives. Governments must enact policies providing clear guidance, incentives, and support for resilient renewable energy supply chains, including ambitious renewable energy targets, financial incentives for R&D, and regulatory stability (Martinez, 2021).

Innovations in Aquaponics Technology and Building Sustainable Infrastructure for Agriculture

Aquaponics, a symbiotic integration of aquaculture and hydroponics, presents a promising approach to sustainable agriculture by maximizing resource utilization and minimizing environmental impact. This study explores recent innovations in aquaponics technology and the development of sustainable infrastructure to support its widespread adoption. A systematic literature review is conducted, showing the fundamentals of aquaponics and its key components such as fish tanks, grow beds, and biological filters. Innovations in technology are examined, including advanced monitoring and control systems, integration of renewable energy sources, automation, and novel growing techniques. Sustainable infrastructure for aquaponics is analyzed, focusing on water management strategies, energy efficiency measures, material selection, and integration with urban agriculture. Furthermore, the alignment of aquaponics technology and sustainable infrastructure development with the United Nations Sustainable Development Goals (SDGs) is studied, particularly in achieving goals related to zero hunger, clean water and sanitation, responsible consumption and production, and climate action.