Intelligent Monitoring System Research Articles

An intelligent climate monitoring system for hygrothermal virtual measurement in closed buildings using Internet-of-things and artificial hydrocarbon networks

Studies analyzing indoor thermal environments comprising temperature and humidity may be insufficient when obtaining data from sensors, which may be susceptible to inaccurate or failed information from internal and external factors. Therefore, this study proposes an intelligent climate monitoring using a supervised learning method for virtual hygrothermal measurement in enclosed buildings used to predict temperature and relative humidity when a sensor failure is detected. The methodology comprises the data collection from a wireless sensor network, the building of the learning model for predicting the dynamics of environmental variables, and the implementation of a sensor failure detection model. We use an artificial hydrocarbon network as the learning model for their simplicity and effectiveness under uncertain and noisy data. The experiments use data acquired in two settings: (1) a laboratory office and (2) a museum storage room. The first scenario has multiple workstations, and the staff turns on or off the air conditioning depending on the feeling of comfort, generating an uncontrolled environment for the variables of interest. The second scenario has controlled temperature and humidity to ensure the conservation conditions of the museum pieces. Both scenarios used 12 sensors that acquired data for one month, providing an average of 58,300 values for each variable. Results of the proposed methodology provide 95% of accuracy in terms of sensor failure detection and identification, and less than 0.22% of tolerance variability in temperature and humidity after sensor accommodation in both scenarios.

Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

An intelligent air quality monitoring system using quality indicators and Transfer learning based Lightweight recurrent network with skip connection

<p>Rapid industrialization and urbanization have resulted in poor air quality, which poses a risk to human health by causing a variety of lung diseases. The precise forecast of air quality is of practical importance. Consequently, the development of an automated air pollution monitoring system based on environmental toxicology is required. Although advanced machine learning approaches can yield reasonable results in air quality prediction, they require more historical data collection. In order to address this problem, a lightweight recurrent network based on transfer learning with skip connection (LRN-SC) is proposed for air quality prediction. LRN-SC pretrains the model using data from an available station. The features that were learned from the previous station are retained, and the pre-trained model is then adjusted to fit the new one. After that, Transfer learning-based light weight recurrent network with skip connection (TL2RN-SC) is trained, and the model is tested using data from the new station. The proposed model reduces the decoding burden by adding skip contacts between the decoder and the linear forecasting layer. The simulation results show that the proposed model outperforms the existing models by attaining average RMSE and MAE of 0.974 and 2.63 respectively.</p>

Research Status and Development Trend of Coal Spontaneous Combustion Fire and Prevention Technology in China: A Review.

Coal seam spontaneous combustion fire is not only one of the main forms of the five major mine disasters, but also the main cause of secondary disasters such as mine gas and coal dust explosions. In recent years, with the advancement of mechanization, automation, and intelligent mine construction, spontaneous coal fires in mines have presented a series of new characteristics, and the prevention and control of spontaneous coal fires are also facing new challenges. On the basis of literature research, this paper summarizes and discusses the basic theory of coal spontaneous combustion, monitoring and early warning methods, and prevention and control technology, summarizes the development process of coal spontaneous combustion theory, reviews the research progress of coal spontaneous combustion monitoring and early warning methods and prevention and control technologies, and discusses the future development direction. In terms of the basic theory of spontaneous combustion of coal, from the initial hypothesis of spontaneous combustion of multielement coal to the unified understanding of coal-oxygen composite theory, a complete set of theoretical systems have been established, and a lot of macro and micro studies have been carried out and analyzed from multiple perspectives. In terms of coal spontaneous combustion monitoring and early warning, from the initial single indicator gas early warning to the construction of gas index system, the hierarchical early warning system is studied, and gradually tends to be perfect. With the development of automation and intelligence technology, the monitoring of coal spontaneous combustion disasters has also formed a new monitoring technology with beam tube monitoring as the traditional method, distributed optical fiber, wireless AD hoc network temperature measurement, and a coal spontaneous combustion multiparameter wireless monitoring system. In terms of fire prevention and control, the traditional "prevention" and "treatment" have changed to the "prevention-control-extinction" technical system based on hierarchical early warning, and the focus has gradually shifted to "prevention", and a large number of antifire materials have been developed, including blocking materials and fire-fighting materials. However, the precise inhibition and control of coal spontaneous combustion disasters, the evolution model of coal spontaneous combustion under the conditions of multifactor coupling in the field, the reliability and stability of intelligent monitoring system, the noncontact detection method of fire source, and the collaborative adaptation of multiple prevention and control techniques are not yet clear. In the future development, the mechanism of spontaneous coal combustion and its evolution process and other basic theories should be deeply studied. On the basis of the mechanism optimization early warning method of spontaneous coal combustion process, flame retardant and fire prevention materials should be targeted and developed. On the basis of the spatiotemporal evolution of spontaneous coal combustion, monitoring and monitoring system equipment with high speed, high precision, and high stability should be developed, which should accelerate the realization of accurate dynamic sensing and intelligent early warning of coal spontaneous combustion, and form an active hierarchical collaborative prevention and control system based on the trinity of "prevention-control-extinction" of coal spontaneous combustion. The conclusions and prospects of this paper can be used for reference in the future research direction, and have a certain role in promoting the exchange of research results of coal science and technology workers.

Surface Wear Monitoring System of Industrial Transformer Tap-Changer Contacts by Using Voice Signal

Surface wear of the tap-changer contacts of industrial transformers (due to frequent switching times) easily leads to operation failure of industrial transformers, which affects the safety and stability of the transmission network. In this paper, an intelligent voice signal monitoring system was proposed for the abnormal condition (surface wear) of tap-changer contacts. This monitoring system was composed of a voice signal acquisition system, voice analysis system and voice processing system. First, the voice signal of the tap-changer contacts was collected, and the collected voice signal was analyzed in the time domain and the frequency domain. Secondly, the characteristic curve of the voice signal was proposed, and the voice curve was compared with that of the normal operation state. In this case, the running state and surface wear abnormal situation of the tap changer could be monitored and determined, and the cause of the abnormal state could also be further analyzed. This method solved the surface wear problem of the tap changer in industrial transformers, which could be not monitored effectively in real time. This method improved the operational reliability of industrial transformers and had high economic and social benefits.

Smart Water Quality Monitoring with IoT Wireless Sensor Networks.

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

IoT-based Design and Implementation of an Intelligent Water Quality Monitoring System

Abstract: Water is an essential requirement for human survival, and a scarcity of water renders us unable to sustain our lives. The issue of water pollution and shortage is a pressing worldwide concern that necessitates the adaptation of water resources and the establishment of guiding principles at both the international and individual levels. Water contamination is the primary contributor to global disease outbreaks. In the past, the process of monitoring water quality relied on the physical collection of data, lacking the implementation of a smart sensor system. This resulted in inefficiencies, particularly in the areas of real-time data retrieval, device surveillance, and less precise data gathering. The integration of a real-time water quality monitoring method is important in order to guarantee the provision of a secure drinking water supply. Therefore, the concept of the Internet of Things (IoT), which facilitates the interconnection of diverse devices for the purpose of data interchange and collection, has emerged

Safe Operation and Intelligent Management of Tunnels in Coastal Cities

This paper focuses on the safety operation and intelligent management of tunnel in coastal cities, and deeply discusses the challenges and opportunities faced by tunnel as an important part of urban traffic under the background of accelerating urbanization process. This paper first analyzes the particularity of the coastal city tunnel in geological environment, climatic conditions and traffic flow, and points out the complexity and importance of its safe operation. Then, the paper elaborates the application of intelligent management in tunnel safety operation, including intelligent monitoring system, automatic early warning system, emergency management system and other aspects, and emphasizes the key role of intelligent technology in improving the efficiency and safety of tunnel operation. In addition, the paper also discusses the development trend of tunnel safety operation and intelligent management in coastal cities, and puts forward suggestions on strengthening technological innovation, improving laws and regulations, and improving management level, in order to provide useful reference for the future safety operation and intelligent management of tunnel in coastal cities.

Wireless Sensing System Based on Biodegradable Triboelectric Nanogenerator for Evaluating Sports and Sleep Respiratory.

The rapid growth of the Internet of Things and wearable sensors has led to advancements in monitoring technology in the field of health. One such advancement is the development of wearable respiratory sensors, which offer a new approach to real-time respiratory monitoring compared to traditional methods. However, the energy consumption of these sensors raises concerns about environmental pollution. To address the issue, this study proposes the use of a triboelectric nanogenerator (TENG) as a sustainable energy source. The electrical conductivity of the TENG is improved by incorporating chitosan and carbon nanotubes, with the added benefit of chitosan's biodegradability reducing negative environmental impact. A wireless intelligent respiratory monitoring system (WIRMS) is then introduced, which utilizes a degradable triboelectric nanogenerator for real-time respiratory monitoring, diagnosis, and prevention of obstructive respiratory diseases. WIRMS offers stable and highly accurate respiratory information monitoring, while enabling real-time and nondestructive transmission of information. In addition, machine learning technology is used for sleep respiration state analysis. The potential applications of WIRMS extend to wearables, medical monitoring and sports monitoring, thereby presenting innovative ideas for modern medical and sports monitoring.

“Research on the construction of internet based aerobics competitive ability assessment model”

A vast field that focuses on restoring motor abilities through exercise is known as sports rehabilitation training, which is also sometimes referred to as recovery athletic exercise. A background in sporting recovery can assist competitors in getting back into peak physical condition, recovering from injuries, preventing additional damage, and better coping with the demands of regular practice. The importance of sports rehabilitation teaching for physical activity is analyzed and discussed at length throughout this research. The definition of sports therapy is presented first, followed by an examination of how it might be used to fitness. Make use of this information as a reference to discover how to include physical therapy in the workout plan you use for sports conditioning. Many tried-and-true algorithms for monitoring patients' vitals over time have emerged as a result of widespread use and advancements in IHM technology, which has led to an increase in the number of such algorithms. The purpose of this research is to investigate the ways in which intelligent health monitoring systems can be applied to the process of developing rehab methods for athletes. It not only provides the structure for a smart wellness terminal, but it also offers the usual ways for monitoring a moving object. Methods such as this include the optical flow strategy, the frame contrast technique, and the background difference method, to name just a few examples. The process of rehabilitation that an athlete went through in X city is presented as an example to show the processes that are included in an ideal study. According to the findings of the study, 92.6 percent of the athletes with injuries were able to make a full recovery with the support of rehabilitative physical exercise. The researchers were able to determine this by keeping close track on the athletes' vital signs in real time using sophisticated health monitoring equipment.

Smart Infant Incubator

We propose a solution called "Smart Infant Incubator" which aims at enhancing the maintenance of new born in various hospitals to make it much better and safer. The main idea of the project is to implement an ESP32-based intelligent environment monitoring system in the incubator from which it measures the humidity and temperature data on the spot where the incubator is placed. The system uses sensors like DHT11 for live temperature and humidity readings, an intelligent alert system and it is integrated with the Rainmaker platform for remote monitoring. The power system is given special attention which includes a Li-ion cell and a charging module to charge the cell. Safety features such as overcharge protection for the battery are also added. The report gives a clear picture of the steps that were performed in the project to accomplish the goals and also the results and analysis of the project. The prototype can be very useful in improving the nursery wards where we will consider this technology to be integrated with the incubators which will be very useful in maintaining the newborns safely.

Optimization analysis of distributed energy consumption based on dynamic data synchronization and intelligent control

SummaryWith the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real‐time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real‐time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in‐depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller‐led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non‐processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real‐time data in the process of parallel processing, as well as the evaluation and detection of real‐time data replacement.

Application and comparative analysis of Intelligent Monitoring Technology for Grouted Pile Construction based on abaqus

In order to realize the intelligent monitoring of the high-precision positioning of the hole position and the real-time control of the verticality of the pile, an intelligent monitoring system was developed based on the combination positioning technology of BDS and UWB and the biaxial tilt sensor, and the numerical simulation and comparative analysis of the verticality of the pile were carried out by abaqus. The deviation of pile foundation in different directions and the deviation of pile body are controlled by the monitoring system, and abnormal warning is made when the deviation exceeds the permissible range.Through the application of intelligent monitoring system in the pile foundation engineering area of Changshui Airport, it is found that the plane offset and perpendicularity of all piles meet the standard requirements and the construction error is controlled at a small value. The results show that the application of intelligent inspection system can not only ensure the construction quality of pile foundation, but also meet and improve the level of digitization and information technology of smart construction site.

Improved PID Algorithm-intelligent Detection System of Tightening Machine

As the level of automation in manufacturing industry continues to improve, intelligent tightening technology is widely used in many fields. Research on torque, force and angle control technology for tightening machines focuses on designing sensitive detection systems, constructing control models and implementing accurate control of tightening parameters. This article proposes an optimized PID algorithm to improve tightening accuracy and stability. At the same time, an intelligent tightening machine monitoring system will be designed and developed to achieve online monitoring and control, ensuring tightening quality and stability. The system will use the latest technologies, including big data, cloud computing and artificial intelligence, to further optimize the tightening effect. The overshoot of PID control optimized by PSO-GA hybrid algorithm is 8.367% , which is far less than that of PID control alone, it is shown that the PSO-GA hybrid algorithm has higher accuracy and better robustness.

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

The author proposes an intelligent early-warning system for preventing external damage of transmission lines. The system uses TI-TMS320DM6446 as the core, the peripheral circuit configuration and video monitoring; The moving objects captured by PTZ camera are detected and identified by using mixed Gaussian model, background difference and minimum tangent matrix, and alarm signals are provided. According to the particularity of the power supply of the equipment on the transmission line, the basic power management of the system is analyzed in detail. A reliable power management scheme is designed. The practical application proves that this method has good real-time performance and has obvious effect on preventing power cable damage by force.

Development Status of Intelligent Traffic Management System

The intelligent traffic management system is the most important part of the intelligent traffic system. The research and analysis of the development status of the intelligent traffic management system can provide a research basis for its future development. This paper studies the three subsystems of the intelligent traffic management system: the intelligent traffic monitoring system, the By comparing the research methods used by scholars in different time periods for intelligent information service systems and signal control systems, it is found that with the application of technologies such as the Internet of Vehicles, big data, Internet of Things, and deep learning, the level of intelligent traffic management system management is constantly rising.

Docker Container-Based Framework of Apache Kafka Node Ecosystem: Vehicle Tracking System by License Plate Recognition on Surveillance Camera Feeds

AbstractVehicle tracking and license plate recognition (LPR) over video surveillance cameras are essential intelligent traffic monitoring systems. Due to the enormous amount of data collected each day, it would be difficult to track vehicles by license plate in a real-world traffic setting. Large volumes of data processing, real-time request responses, and emergency scenario response may not be possible using conventional approaches. By combining license plate recognition with the docker container-based structure of the Apache Kafka node ecosystem, the suggested solution takes a novel approach to vehicle tracking. The primary components of our suggested framework for reading license plates are the identification of license plates and text data queries. License plate localization is performed with You Only Look Once version 3 (YOLOv3) and character recognition with Optical Character Recognition (OCR). The detected vehicle images with license plate results are published on related topics with Apache Kafka. Apache Kafka is a publish-subscribe (producers-consumers) messaging system and one of the most popular architectures used for streaming data. For each license plate search, a topic will be created in the framework where producers publish and consumers receive data. Thus, the workload of the operators will be reduced and they will be able to pay attention to more important events in traffic.

Intelligent Monitoring System for the Elderly based on Posture Recognition

In recent years, due to the gradual increase in the elderly population, elderly care has become an increasingly urgent issue, and the most common risk faced by the elderly is accidental falls. In this paper, an intelligent monitoring system for the elderly is studied, which includes a communication module, a sensor module and a state detection module. It can monitor the status of the elderly in real time and feed back to the server to activate the buzzer alarm function. In the communication module, WI-FI communication between esp32 microcontroller and PC is established to realize real-time transmission of pressure sensor data. The C/S communication method between Raspberry PI and PC was established to realize the transmission of real-time video data. In the sensor module, the pressure sensor is used for data acquisition, and the RF algorithm is used to realize the binary classification prediction of whether the human body is in bed or out of bed. In the state detection module, the yolov8 algorithm combined with the GSRF algorithm is used to classify the image data into five action categories, namely, falling, waving, sitting, standing and walking, to predict the state of the human body in the real-time video stream with an accuracy of 90%. After the sensor module detects the state of the human body out of bed, the state detection module is started, and the results are transmitted back to the back-end server and uploaded to the Web page to realize the function of real-time monitoring of the state of the elderly.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.