Intelligent Monitoring System Research Articles

Enhancing ICU Outcomes Through Intelligent Monitoring Systems: A Comparative Study on Ventilator-Associated Events

Background: Using intelligent monitoring systems can potentially improve the identification and management of ventilator-associated events (VAEs). This single-center retrospective observational study evaluated the impact of implementing intelligent monitoring systems on the clinical outcomes of patients with VAEs in an ICU setting. Method: An intelligent VAE monitoring system was integrated into electronic medical records to continuously collect patient data and alert attending physicians when a ventilated patient met the criteria for a ventilator-associated condition, which was defined as an increase of at least three cm H2O in positive end expiratory pressure (PEEP), an increase of at least 0.20 in the fraction of inspired oxygen (FiO2), or the FiO2 being over baseline for at least two consecutive days. This study covered two phases, consisting of before using the intelligent monitoring system (2021–2022) and during its use (2023–2024). Results: The results showed that patients monitored with the intelligent system experienced earlier VAE detection (4.96 ± 1.86 vs. 7.77 ± 3.35 days, p < 0.001), fewer ventilator-associated condition (VAC) occurrences, and a shorter total duration of VACs. Additionally, the system significantly reduced ventilator days, antibiotic use, and 14-day mortality. Conclusions: Intelligent monitoring systems enhance VAE management, improve clinical outcomes, and provide valuable insights into the future of critical care medicine.

Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy

Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

A Support Vector Machine-Based Intelligent System for Real-Time Structural Health Monitoring of Port Tower Cranes

A Support Vector Machine-Based Intelligent System for Real-Time Structural Health Monitoring of Port Tower Cranes

An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring

ABSTRACTCurrently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.

Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

Research on Intelligent Agricultural Monitoring and Control System Based on Neural Networks in the Internet of Things Environment

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.

A New Self-Sensing Fiber Optic Anchor to Monitor Bolt Axial Force and Identify Loose Zones in the Surrounding Rock of Open TBM Tunnels.

TBM has been widely used in underground engineering and construction, but there is no precedent for the application of open TBM in the inclined shafts of coal mines, which brings new challenges to the support system. The distribution of the axial forces on anchors and the range of loosening of the surrounding rock are crucial considerations in tunnel support design. Existing methods for measuring the axial forces in anchors and determining the extent of loosening in the surrounding rock typically remain at the inspection level, lacking long-term and real-time monitoring capabilities. This paper presents a new self-sensing anchor with embedded optical fibers (made using an improved stirrer) and proposes an intelligent tunnel rock monitoring system. The paper also outlines a method for identifying loosening zones in surrounding rock based on monitoring data and theoretical analysis. Installing self-sensing anchors in the deep sections of the rock surrounding a tunnel provides three-dimensional, round-the-clock real-time monitoring of the axial forces acting on the anchors, using new technology and methods to recognize the deformation characteristics of loosening zones within the surrounding rock. This new self-sensing fiber optic anchor was first applied to an open TBM tunneling project in an inclined shaft in the Kekegai coal mine, and monitoring data indicate that self-sensing optical fiber anchors can accurately reflect stress patterns in real time. The axial force curve can be divided into four segments: the borehole area, the loosening zone, the stable zone, and the anchoring zone. Consequently, it accurately identifies the thickness of loosening zones at different positions within the tunnel's surrounding rock. This information is compared and verified against results obtained from bolt dynamometers and borehole inspection. On this basis, an intelligent monitoring system was established to provide a basis for making engineering construction decisions, which makes tunnel construction smarter and helps technicians timely adjust TBM driving and support parameters.

Monitoring system for physical activity and fitness based on service robots and biomechanics

Exercise is one of the important ways for people to exercise, with characteristics such as sociality and strong participation. Especially with the improvement of the level of economic development and the improvement of the quality of life of the people, more and more people begin to attach importance to the maintenance of their own health. Physical fitness monitoring, as an effective means, is widely used in daily life, especially among the elderly. However, most of the existing monitoring methods are relatively simple, lacking pertinence, and the data collection process is relatively cumbersome and unstable, which cannot meet current needs. Therefore, it is very necessary to explore a new type of equipment that can more comprehensively and accurately monitor various physiological parameters of the human body to replace existing traditional detection technologies. Service Robots are currently the most promising intelligent hardware products. They mainly provide personalized services centered on users, sensing user behavior and implementing intelligent decision-making based on their characteristics, thereby better meeting the needs of different groups of people. This article focused on the research and development of Service Robots, and designed a comprehensive solution for Service Robots based on theories such as the Internet of Things, cloud computing, big data technology, and artificial intelligence. This article compared existing intelligent monitoring systems with fitness monitoring systems based on Service Robots, and proved that the user experience of fitness monitoring with robot participation has improved by about 4.68%. Its application scenarios were richer and its effects were more significant, enabling it to better complete tasks such as analysis and prediction of physical fitness status, real-time warning, etc., reducing the risk of people suffering from diseases, and enhancing individual protection awareness.

An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.

Development of an intelligent geographic information system for mountain roads monitoring: Ground data collection and analysis

This study examines the development of an intelligent geographic information system for mountain road monitoring. To make well-informed and timely management decisions in the planning, construction, and maintenance of mountain roads, and to anticipate potential critical situations and their effects on road conditions, it is imperative to employ modern methodologies. This study involves data collection from a specific section of a mountain road situated near Almaty, Kazakhstan. We gathered data on the road section through visual assessment, measurements of the coefficient of adhesion, and the acquisition of material samples from the structural layers of the roadbed. Based on the visual assessment and surface adhesion measurements, it became evident that the deformation of the studied road section, which includes a network of cracks and "non-standard" transverse cracks, is attributed to mountain mass shifts and water flows from slopes. Laboratory analysis of the collected samples revealed that excessive water saturation could result in cracks during freeze-thaw cycles, while a low resistance coefficient would lead to softening during humid conditions. An Intelligent Geographic Information System (IGIS) will incorporate the collected data alongside open remote sensing and weather data. The objective is to utilize this integrated system in the future for forecasting road conditions and maintenance requirements for similar roads in mountainous regions of the area. The measurements are carried out according to the state standards. That makes the system useful for government management purposes.

Design of intelligent remote monitoring and diagnosis system for equipment

Design of intelligent remote monitoring and diagnosis system for equipment

High-performance triboelectric nanogenerator with aminated barium titanate composite nanoparticles for early Parkinson’s disease diagnosis

High dielectric constant nanoparticles into polymer matrices have attracted much attention because this process can improve the nanofriction generator performance. However, the dispersion of the developed materials remains a challenge. In this paper, we present a novel high-performance nanofriction generator (FBT-TENG) based on composite nanoparticles of Amino-functionalized barium titanate and functionalized graphite oxide (FGO/ABTO) with Polyimide (PI) as substrate, which can be used as a sensor for intelligent monitoring systems. Specifically, we introduced amino groups on the surface of barium titanate to improve its dispersion in the PI solution, and the dual effect composite with functionalized graphite oxide improved the TENG performance of the PI film. Due to the FGO/ABTO synergy, the FBT-TENG can generate high output voltages, currents and power densities of 75 V, 13 µA and 4.6 W/m2, respectively, which is a 9 times increase in power density compared to the original PI film. FBT-TENG is capable of powering capacitors. In addition, the FBT-TENG can be used as a sensor to build an early Parkinson’s disease detection system, which provides a more convenient option for the early diagnosis of Parkinson’s patients and has the potential for a wide range of medical applications.

Digital Twin Model Study of Piping Acoustic Induced Vibration Risk

Abstract The research and calibration of digital twin models are crucial for developing an intelligent monitoring system for pipeline acoustic induced vibration. This study addresses the rapid assessment of acoustic induced vibration risks during the design stage using a digital twin model. First, the likelihood of failure (LOF) due to acoustic induced vibration was calculated based on EI guideline, providing a quantitative risk assessment. Concurrently, numerical simulations were conducted to validate these results. The findings show a high consistency between the two methods, indicating their complementary and validating roles. Therefore, the integrated use of methods based on EI guidelines and numerical simulation provides robust technical support for early-stage assessment of acoustic induced vibration risk in pipeline design, enhancing both accuracy and reliability of the evaluation, and laying a solid foundation for pipeline design and safety assurance.

Intelligent beehive monitoring system based on internet of things and colony state analysis

Bees play a crucial role in terrestrial ecosystems. However, beekeepers are unable to monitor the state of beehives (bees and environment) all the time, which often results in bees escaping or even dying. Currently, some researchers provided the scheme of intelligent beehive monitoring system equipped with the Internet of Things (IoT), There remain two challenges: accurately monitor the environmental status around the hive and accurately track and monitor bees in real time. With the development of the IoT and computer vision algorithms, we hope to provide an automated and efficient system to meet the above challenges. In this paper, we proposed a hive monitoring system, and build a visualization module in the cloud to monitor the activity of bee colonies and the environmental dynamic changes. (1) We proposed a multi-bee tracking algorithm to solve the problem of monitoring bees at the door of the hive; (2) we constructed a dataset containing various complex scenes, named BEE22, for training and testing the performance of our algorithm; (3) we designed a bee counting rule, based on results of multi-bee tracking algorithm, to reasonably count the bees entering or leaving the beehive; (4) we have deployed multiple sensors around(center, margin, door, and environment) the hive to accurately reflect the changes in the environment around the hive. Experimental results demonstrate the effectiveness and excellence of our system. In particular, the tracking performance of the multi-bee tracking algorithm reaches 83.5 % ± 0.7 % Multiple Object Tracking Accuracy (MOTA) and 77.3 %±0.2 % Multiple Object Tracking Precision (MOTP), speeds up to 16 frames per second, compared with other algorithms, MOTA and Identity F1 Score (IDF1) are improved by 5.4 % and 8.2 % respectively. Moreover, our counting algorithm also achieved excellent results, with root mean square error (RMSE) of 1.3 ± 0.1, 0.2 ± 0.0, and 1.6 ± 0.1 in counting the number of bees current, entry, and out scene in an episode, respectively. After that, the system will be deployed and monitored for a long time in the actual scenario, it was found that the activity of bees decreased significantly under heavy rainfall conditions. Additionally, the activity of the bee colony will also increase accordingly, when the amplitude is 500 dB to 2000 dB, the temperature of the center of the beehive is 25 °C to 37 °C, and the humidity is 48 % to 67 %. In summary, our system can provide valuable information for bee farmers to make control decisions on hives.

Multifunctional Human-Computer Interaction System Based on Deep Learning-Assisted Strain Sensing Array.

Continuous and reliable monitoring of gait is crucial for health monitoring, such as postoperative recovery of bone joint surgery and early diagnosis of disease. However, existing gait analysis systems often suffer from large volumes and the requirement of special space for setting motion capture systems, limiting their application in daily life. Here, we develop an intelligent gait monitoring and analysis prediction system based on flexible piezoelectric sensors and deep learning neural networks with high sensitivity (241.29 mV/N), quick response (66 ms loading, 87 ms recovery), and excellent stability (R2 = 0.9946). The theoretical simulations and experiments confirm that the sensor provides exceptional signal feedback, which can easily acquire accurate gait data when fitted to shoe soles. By integrating high-quality gait data with a custom-built deep learning model, the system can detect and infer human motion states in real time (the recognition accuracy reaches 94.7%). To further validate the sensor's application in real life, we constructed a flexible wearable recognition system with human-computer interaction interface and a simple operation process for long-term and continuous tracking of athletes' gait, potentially aiding personalized health management, early detection of disease, and remote medical care.

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Automated monitoring of natural parameters of renewable energy production systems

The paper discusses the urgent issues of the need to automate the monitoring of natural parameters of renewable energy production systems. Monitoring involves the collec- tion and analysis of various parameters of the natural environment that affect the efficiency and environmental friendliness of systems powered by renewable energy. Based on analytical materials and practical work related to techno-economic and ecological studies concerning the assessment of water reserves in indi- vidual wells, the study focused on the development of technologies and technical means for monitoring. It was found that, in practice, hydrogeological research on groundwaters use outdated methods and technical means for measuring and storing data. A local automated system for monitoring well parameters was developed and constructed. It ensures the operation of the hydrothermal heat pump system at the experimental site of the Institute of Renewable Energy of the National Academy of Sciences of Ukraine. This system was tested in our research. The benefits of this study are revealing the need to create an intelligent monitoring system with the possibility of automated decision-making regarding the management of renewable energy generation systems, depending on changes in the am- bient parameters. A network automated system for monitoring natural parameters was developed, consisting of several wireless sensor subsystems that communicate with each other via wireless connection and transmit information to the gateway. The gateway, in turn, is connected to the computer with a graphical interface for interaction with the system. The results of work indicate that the proposed structures for automated water monitoring are simple and easy to implement. Consequently, the study revealed that one of the prospects is the development of automated monitoring systems with intelligent features, capable of making optimal individual decisions regard- ing the management of renewable energy generation systems under variable environmental parameters. As a result, the data obtained from this study have significant scientific and practical implications for advancing the design methodology of geothermal heat pump wells, focusing on long-term forecasts and assessments of their ecological and economic efficiency.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

Automatic Disease Detection from Strawberry Leaf Based on Improved YOLOv8.

Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model's target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.