Framework Of Monitoring System Research Articles

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

Cloud-edge-end-based aircraft assembly production quality monitoring system framework and applications

In recent years, the increasing complexity of quality control in aircraft assembly has necessitated the development of innovative and integrated solutions. This paper introduces a novel framework based on cloud-edge-end architecture to enhance quality monitoring systems in aircraft assembly production. The framework consists of five parts: cloud, edge, end, network, and the trained model. By leveraging the advantages of cloud computing, edge computing, and terminal devices, this framework aims to provide a comprehensive, real-time, and efficient quality monitoring solution. Key technologies within the cloud-edge-end framework are proposed, including data collection, storage, and transmission, deep learning methods based on Channel Attention Convolutional Neural Network (CACNN), and feedback mechanisms from the cloud layer to the lower layers. These innovations enable effective information sharing and utilization at various stages of the aircraft assembly process, providing operational managers with timely and accurate insights into assembly quality. A prototype system application has been implemented at an aircraft assembly site, facilitating process data monitoring and assembly status analysis. Experimental validation of the proposed CACNN algorithm has demonstrated its capability to monitor hole diameters online. Various case studies confirm the framework's effectiveness, showcasing substantial improvements in monitoring accuracy and operational efficiency.

Embedded monitoring system and teaching of artificial intelligence online drug component recognition.

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

AI/ML driven intrusion detection framework for IoT enabled cold storage monitoring system

AbstractAn IoT‐based monitoring system remotely controls and manages intelligent environments. Due to wireless communication, deployed sensor nodes are more vulnerable to attacks. An intrusion detection system is an efficient mechanism to detect malicious traffic and prevent abnormal activities. This article suggests an intrusion detection framework for the cold storage monitoring system. The temperature is the main parameter that affects the environment and harms stored products. A malicious node injects false data that manipulates temperature and forwards manipulated data. It also floods the data to neighbor nodes. In this work, data are generated and collected for intrusion detection. Two machine learning techniques have been applied: supervised learning (Bayesian rough set) and unsupervised learning (micro‐clustering). The proposed method shows better performance than existing methods.

A novel transformer-enhanced and acoustic-based approach for wind turbine blade fault detection with integrated system implementation

Rising global wind power capacity urgently requires effective real-time turbine monitoring. Complex installations, high maintenance costs, and the occasional need for turbine shutdown hinder conventional monitoring methods. Acoustic-based techniques, while cost-efficient for health assessments, currently face challenges with detection precision and signal processing capabilities in complex acoustic environments. To overcome these challenges, this study proposes a low-cost, AI-driven acoustic-based health monitoring system framework for wind turbine blades, featuring enhanced detection accuracy and signal processing capabilities in complex sound environments. Firstly, a microphone array is employed to capture blade sounds with rich spatial attributes, while beamforming algorithms with a fixed orientation integrate prior spatial information. Furthermore, to further strengthen signal processing and fault detection capabilities, the Transformer model based on self-attention mechanisms is employed for feature extraction and fault diagnosis. An actual wind turbine blade health monitoring system is designed and developed to validate the usability of the proposed framework. Experimental results based on a scaled-down wind turbine model validate the proposed algorithm's effectiveness and practicality, presenting an effective approach in the wind turbine blade health monitoring domain.

Air quality monitoring using multi node slave IoT

Jakarta is the city with the second poorest air quality in the world. IQAir data show that Jakarta's air quality is 159. In addition, the concentration of air particles in Jakarta is 14.2 times higher than the annual guidelines of the World Health Organization (WHO). According to the WHO, exposure to air pollution causes around 7 million premature deaths and millions of years of lost health time each year. Air pollution also stunts children's growth, impairs lung function, etc. Therefore, we need a system that can be used to combine air quality to determine how dangerous a place is with air quality. Knowing air quality, certain policies or actions being taken to overcome this danger. This research aims to build and test a prototype air quality monitoring system using multi-node slaves with the Internet of Things. The prototype development process was carried out by adapting the architectural framework of the air quality monitoring system with the Internet of Things. The testing of prototype results is carried out to sound sensor values and functional success. The results of the test show that the system can run well according to the design made. The DSM501A sensor device functions to detect particles of a size larger than one micrometer, which usually include cigarette smoke, house dust, ticks, spores, pollen, and mildew, and works well so that the controller can read the surrounding air conditions well.

Monitoring system framework design for floating wind turbine using the deep learning technology and tower response identification considering sensor optimization

Comparing with the fixed wind turbines, the dynamic characteristics of floating wind turbines (FWTs) are more complex and resulting in distinctions in the monitoring system for structural responses. An integrated monitoring system framework concept design for FWT is proposed combing the dynamic responses correlations of different structural parts and the deep learning technology, in which the intact and partial sensor failure operation modes are considered separately. The monitoring system consists of four vital structural components, including the rotor and nacelle, tower, floating foundation, and mooring system. In order to improve the tower response identification accuracy, a new method for the sensor optimization is proposed to utilize more structural information of the structure based on the effective independence method (EIM) and modal assurance criteria (MAC) methods. A multi-perception (MLP) neural network model based on the input-output coupling mechanism is established to identify the acceleration responses at the tower top and forces at tower root. The optimized identification model can greatly improve the identification accuracy of critical sensors by more than 50%, especially for bending moments.

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.

Challenges in human centric intelligent systems for wireless sensor networks: A state of art

AbstractHuman centric computing is a technique that is gaining more attention nowadays and integrates innovative processing methods for analyzing extensive data collection. Human centric intelligent systems focus on handling the interactions between customers, companies, communities and systems of computing to represent social and institutional concepts effectively. However, this interaction between the companies and customers will lead to various privacy challenges in human centric intelligent systems. This paper briefly studies cyber‐physical systems and various privacy challenges in human centric intelligent systems. In parallel, this article enunciates the human‐centric pipe water monitoring system framework in detail. It also discusses the smart body area network (SBAN) as a typical example of space cooperation. SBAN employs low‐power wireless devices in a compact form that can be incorporated inside the human body for health monitoring. This study examines the challenges of privacy, propagation, and trust assessment that human‐centric systems face.

Smart Micro Grid Architecture for Realtime Monitoring of Solar Photovoltaic Based on Internet of Things

The use of PV solar energy as an alternative renewable energy source has increased worldwide. The smart grid monitoring system is applied on a micro scale for the fulfilment of household electricity needs or Smart Home Micro Grid (SHMG) using Artificial Intelligent (AI) and Internet of Things (IoT) approaches. Problems in solar PV systems include uncertainty and non-linearity which have an impact on the increasingly complex structure of the energy data generated and accelerate control and decision making. The forecasting process carried out using the AI approach is indispensable for the utilization of PV systems, especially for distributed residential PV that is operated and maintained independently. Furthermore, the use of IoT technology is also important to accelerate information processing and improve performance in the monitoring system. The purpose of this research is to develop a solar PV monitoring system framework at SHMG to optimize the performance of the electricity supply sourced from solar PV basen on IoT.

Development and engineering application of integrated safety monitoring systems for China's high-speed trains

Abstract With the improvement of the running speed of China's high-speed trains, the demands for running status monitoring and security assurance of High-speed Electric Multiple Units (EMU) have increased significantly. However, the current safety monitoring systems are independent, which is not conducive to the comprehensive monitoring and information sharing of the whole vehicle. The temperature monitoring of running gear is insensitive to early failures. How to develop a train operation safety monitoring system with strong engineering implementation and high integration is a key problem to be solved. For the monitoring of running stationarity, frame stability and running gear health of China's high-speed trains, an integrated safety monitoring system framework is designed, and the logic and algorithm for the diagnosis of stationarity, stability and health states of rotating parts are constructed. Monitoring software which fused the temperature, high and low frequency vibration data is developed, and the design and installation of the vibration temperature composite sensors are completed. The research results have realized the integration and comprehensive processing of multiple monitoring systems, completed the improvement from single component and vehicle-level safety monitoring to multi-system, train-level and interactive monitoring. In the process of real vehicle application, the developed monitoring system acquires the vehicle operation status data accurately and in real time. The constructed diagnosis algorithm and logic evaluates the vehicle operation status accurately and in a timely manner, and avoids the progression from fault to accident. The research results show that the integrated safety monitoring system can provide technical support for train operation safety.

"IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management"

Curbing road accidents havealways been one of the utmost priority of nations worldwide. In Malaysia, the Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents haveincreased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidentsis the driver’s behaviors. However, to regulate drivers’ behavior by the enforcement team or fleet operatorsischallenging, especially for heavy vehicles. In our research, we have proposed the Internet of Things (IoT) scalability framework and its’ emerging technologies to monitor and alert driver’s behavioral and driving patterns to reduceroad accidents. To prove this work, we have implementeda lane tracking,and iris detection algorithm, to monitor and alert the driver’s behavior when the vehicle sways away from the lane, and to detect if the driver is feeling drowsy. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as the hardware for an intelligent system in the vehicle. We also appliedface recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioral activities in real-time through the user access portal. We have validated it successfully on Malaysian roads. The outcome of this pilot project ensuresthe safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioral in the future.

Intelligent Monitoring System of Access Equipment in Dispatching Exchange Network Based on Multi-layer Immune Model

To visualize the soft switch traffic and traffic flow of the whole network scheduling, it is necessary to monitor the scheduling of the switching network intelligently. The original system uses fixed-point mode to monitor devices, which can not anchor multiple groups of access devices simultaneously, resulting in errors in monitoring results. In the hardware design of the system, a multilayer immune model is used to establish a three-level visualization partition, and a monitoring system framework for access devices is constructed. In the software design, the detection module, the prompt module, and the automatic decoding module are set up, respectively, and the monitoring process of the dispatching switching network is configured. The access equipment of the dispatching exchange network is intelligently monitored and controlled by an immune model with a three-layer structure. The experimental results show that the data obtained by this system is consistent with the real data and has an application effect by taking the communication data of power dispatching in a provincial power system as the test object and monitoring the communication data of multiple links in a year.

A hybrid deep learning for patient activity recognition (PAR): Real time body wearable sensor network from healthcare monitoring system (HMS)

Today’s healthcare sectors are driven and work to rescue patients as soon as possible by giving them the right care and treatment. A healthcare monitoring system works in two ways: by keeping track of the patient’s activities and overall health. For prompt treatment, such as giving the right and suitable medication, administering an injection, and providing additional medical help, nursing supervision is required. Wearable sensors are fixed or connected to the patient’s body and can follow their health. These IoT medical gadgets let clinicians diagnose patients and comprehend the processes from remote. However, the amount of data produced by IoT devices is so large that it cannot be handled manually. A model for automated analysis is required. Convolution Neural Network with Long-Short Term Memory (CNN-LSTM) was therefore suggested in this study as a Hybrid Deep Learning Framework (HDLF) for a Patient Activity Monitoring System (PAMS) that brings all healthcare activities with its classes. To incorporate medical specialists from all over the world and enhance treatment outcomes, the framework offers an advanced model where patient activities, health conditions, medications, and other activities are distributed in the cloud. An effective architecture for Wearable Sensor Network-based Human Action Recognition that combines neural network Simple Recurrent Units (SRUs) and Gated Recurrent Units (GRUs). For assessing the multimodal data input sequence, deep SRUs and a variety of internal memory states is utilized in this research. Furthermore, for addressing the concerns about accuracy oscillations or instability with decreasing gradients, a deep GRUs to store and learn the knowledge is conveyed to the future state. The analysis suggests that CNN-LSTM is then contrasted with some of the currently used algorithms, and it is found that the new system has a 99.53% accuracy rate. The difference between this accuracy result and the current value is at least 4.73%.

A Framework for IoT-based Greenhouse Monitoring System

A Framework for IoT-based Greenhouse Monitoring System

Nursing Effect of Health Monitoring System on Elderly Patients with Osteoporosis.

Health monitoring can provide scientific and reasonable care for elderly users, professionally monitor the health parameters of the human body, and timely understand the user's own physical condition. By installing sensors with different functions in the rooms where the elderly often move and by installing vital signs sensors on their bodies, the data detected by the sensors are collected and analyzed in real time. Nursing refers to the fact that nursing staff must strictly follow the nursing system and operating procedures in the nursing work, accurately implement the doctor's orders, implement the nursing plan, and ensure that the patient is physically and mentally safe during treatment and recovery. Osteoporosis is a systemic bone disease in which bone density and bone quality are decreased due to various reasons, and the microstructure of bone is destroyed, resulting in increased bone fragility, which is prone to fractures. Osteoporosis is divided into two main categories: primary and secondary. Primary osteoporosis is divided into postmenopausal osteoporosis (Type I), senile osteoporosis (Type II), and idiopathic osteoporosis (including adolescent forms). This paper aims to study the healthcare effect of health monitoring system on elderly patients with osteoporosis, expecting to use the health monitoring system to provide more scientific care for the elderly and reduce the pain caused by osteoporosis. This paper proposes a study from the users of the elderly health monitoring products and the elderly home health products and analyzes the influencing factors of the usability design of the elderly home health monitoring system. This paper designs the overall framework of the elderly health monitoring system and designs the main components and application functions of the system. The experimental results in this paper show that there are 20 patients with osteoporosis due to lack of light, accounting for 16%. There are 10 patients with osteoporosis due to excessive coffee intake, accounting for 8%. There are 90 people who normally eat eggs, accounting for 75%, and 66 people who eat meat normally, accounting for 55%. According to the data, the health monitoring system can effectively control the diet of patients with osteoporosis.

Conjoint analyses of patients’ preferences for primary care: a systematic review

BackgroundWhile patients’ preferences in primary care have been examined in numerous conjoint analyses, there has been little systematic effort to synthesise the findings. This review aimed to identify, to organise and to assess the strength of evidence for the attributes and factors associated with preference heterogeneity in conjoint analyses for primary care outpatient visits.MethodsWe searched five bibliographic databases (PubMed, Embase, PsycINFO, Econlit and Scopus) from inception until 15 December 2021, complemented by hand-searching. We included conjoint analyses for primary care outpatient visits. Two reviewers independently screened papers for inclusion and assessed the quality of all included studies using the checklist by ISPOR Task Force for Conjoint Analysis. We categorized the attributes of primary care based on Primary Care Monitoring System framework and factors based on Andersen’s Behavioural Model of Health Services Use. We then assessed the strength of evidence and direction of preference for the attributes of primary care, and factors affecting preference heterogeneity based on study quality and consistency in findings.ResultsOf 35 included studies, most (82.4%) were performed in high-income countries. Each study examined 3–8 attributes, mainly identified through literature reviews (n = 25). Only six examined visits for chronic conditions, with the rest on acute or non-specific / other conditions. Process attributes were more commonly examined than structure or outcome attributes. The three most commonly examined attributes were waiting time for appointment, out-of-pocket costs and ability to choose the providers they see. We identified 24/58 attributes with strong or moderate evidence of association with primary care uptake (e.g., various waiting times, out-of-pocket costs) and 4/43 factors with strong evidence of affecting preference heterogeneity (e.g., age, gender).ConclusionsWe found 35 conjoint analyses examining 58 attributes of primary care and 43 factors that potentially affect the preference of these attributes. The attributes and factors, stratified into evidence levels based on study quality and consistency, can guide the design of research or policies to improve patients’ uptake of primary care. We recommend future conjoint analyses to specify the types of visits and to define their attributes clearly, to facilitate consistent understanding among respondents and the design of interventions targeting them.Word Count: 346/350 words.Trial registrationOn Open Science Framework: https://osf.io/m7ts9

Smart streetlight framework for collision prediction of vehicles

Road accidents cause a large number of public fatalities in developing countries affecting them economically. It has been more than a decade since automobile industries are into developing intelligent vehicles with a major objective of road safety. In order to warn or assist the drivers, various automobile manufacturers have been focusing on developing autonomous or semi-autonomous vehicles, but not much effort has been drawn towards developing the smart infrastructure to supplement the development of intelligent vehicles. This paper proposes a framework for a smart vehicle monitoring system to provide smooth mobility of vehicles using roadside units such as streetlights. Such a system aims to eliminate the necessity of equipping each vehicle with smart sensors, which is typically an expensive scheme in most of the developing and underdeveloped countries around the world. This paper attempts to approach the problem of vehicle collision prediction by designing the system based on the concepts from science and engineering such as relative motion and vectors and implementing it using a machine learning-based model. These concepts led to the generation of datasets over which the prediction model was trained. The performance of the proposed model has been simulated using the software - Virtual Crash.

Real-Time Cyber-Physical System for Healthcare Monitoring in COVID-19

The Cyber Physical System (CPS) is used to integrate physical processes with computation and communication. Recent advances in internet of things; cloud computing and machine learning is making cyber physical system an emerging technology in healthcare system. This paper present real time cyber physical system framework for healthcare monitoring system plays major role in pandemics such as Covid-19. The system processes real time patient’s critical data such as blood sugar, blood pressure, temperature used to save life of patients. The real time processing done at the sensor node using real time tasks scheduling. The sensor node data get analyze on fog nodes taking time critical actions. The healthcare data then send using Kafka real time streaming using Kafka pipeline. The processing of healthcare data using Spark in real time done at Hadoop Distributed File System (HDFS) on cloud .

Design of Cloud Storage-Oriented Sports Physical Fitness Monitoring System.

In order to improve the accuracy and response speed of sports fitness monitoring results and make the monitoring results more comprehensive, a new cloud storage-oriented sports fitness monitoring system is designed. Based on cloud storage technology, the overall framework of the sports fitness monitoring system is established; the function of the hardware module of the monitoring system is analyzed, and distributed database is established. The ray-casting image feature scanning method was used to collect the physical condition monitoring image and generate a high-quality human body target frame to realize the physical condition monitoring. Based on the monitoring data, the fitness method recommendation method is designed according to the user's physical condition. The experimental results show that the monitoring results of the proposed system have higher accuracy, faster system response speed, and higher comprehensiveness of the monitoring results, which verifies the application value of the proposed system.