Real Monitoring System Research Articles

Performance simulation study of plastic scintillator arrays for stability monitoring applications

The characteristics of high energy cosmic-ray muons make them strongly penetrative in materials. It has great potential to monitor the stability of buildings by reconstructing the tracks of cosmic-ray muons. In this work, a buildings stability monitoring system based on plastic scintillator strips is proposed. The monitoring system consists of a single-plane upper telescope to record the incoming track of a muon, and a three-plane lower telescope to track its outgoing trajectory. Every detector plane is formed by orthogonally placed scintillator strips. The impacts of the scintillator geometry and spatial placement on the stability monitoring precision are carefully studied using Geant4 simulations. A sub-millimeter position resolution is achievable by selecting a proper data taking time. The triangular scintillator strips always outperform rectangular ones of the same size and placement. Two empirical formulas have been derived to quantitatively estimate the position resolution with respect to the abovementioned influential factors. In addition, the comprehensive effects of multiple parameters are also studied. Accordingly, triangular scintillator strips will be utilized in the construction of a real monitoring system with better performance and less detection channels. The simulation study in this work can provide good guidance for the manufacturing of triangular strips and their experimental configurations.

Insights 4.0: Transformative learning in industrial engineering through problem‐based learning and project‐based learning

AbstractThis paper describes a methodological study carried out between 2018 and 2022, at Rey Juan Carlos University, focused on the subject monitoring and control systems within a master's program in Industrial Engineering. The study proposes an innovative teaching strategy using problem‐based learning and project‐based learning methodologies. The projects undertaken are based on Internet of Things (IoT) systems aimed at enhancing weather stations, services and facilitating real‐time decision‐making. Inspired by our experience in the development of Industry 4.0 projects, we have designed a methodological strategy for this subject that focuses on providing students with the necessary knowledge and skills in the field of Control and Monitoring Systems and the IoT to develop real monitoring and control systems. The approach emphasizes interdisciplinary problem‐solving, with students working collaboratively in stable teams. Throughout the 16‐week course, tasks of increasing complexity are completed, resulting in the development of a complete system. The practical approach of the course and its relation to real applications motivates students, resulting in better performance. The acquired techniques and skills from the course are broadly applicable across engineering disciplines.

Parallel Intelligent Monitoring System of Port Water Quality Based on the ACP Method

With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

Scripting Scenarios of Pedestrian Behavior in a Computer Simulator of Security Monitoring System: A Practitioner’s Perspective

Abstract The subject of this paper is the AvatarTraffic simulator – a computer system capable of modelling in real time environments such as subway stations or airport halls populated with tens or hundreds of moving figures, which, in addition to pedestrian traffic typical for this type of objects, can perform predefined sequences of events and actions formulated as a simulation scenario. Thanks to the integration with a real monitoring system, the simulator, in addition to providing data streams (including video) generated by the virtual scene, is also able to dynamically respond to actions taken by the system’s staff. Using the Unity simulation engine as the implementation platform, a number of practical problems had to be solved during the development, two of which are the subject of this article: a) supervising and correcting the work of AI algorithms used in Unity to simulate the pedestrian movement of avatars, and b) a textual description of the scenario of events taking place on the stage in a way editable for experts planning tests of the monitoring system. Some more challenging cases of people movement are discussed (including creating queues and passing through doors) and the paper presents original algorithms correcting the work of the Unity’s built-in methods in the situations when the coordinated behaviour of people groups is required. Because of the specifics of the simulator environment the scenario needed to be expressed in a JSON text file, and the article presents the implemented mechanisms of its compilation directly to the C# runtime environment and discusses the original command language which was created to model sequences of events and actions making up the scenario.

Krabi's renewable energy transition towards sustainable energy: drivers, barriers, and challenges.

The present study was conducted to investigate key drivers, barriers, and challenges towards Krabi's sustainable energy. The provincial electricity demand was found to be continuously increasing, while its supply has relied on national grid which is not a sustainable practice. Krabi Goes Green roadmap has set a target to be self-reliance and 100% electricity from renewable energy (RE). This is a key driver for Krabi's RE transition. Even though high RE resources potential was reported, installation of RE power plants can supply only about half of its annual demand. What would be key barriers and challenges to overcome the barriers were investigated via stakeholders' interviews. Challenges or key success recommendation for each RE are briefly described. Krabi has high potential of both biomass and biogas from wastes of palm oil industry in the province. By the way, most of biomass and biogas power plants are VSPPs having non-firm-type power purchasing agreement (PPA) leading to unsecure electricity buying and selling. The PPA reconsidering was recommended by interviewees. In addition, new biomass power plants are public opposed due to emissions of solid biomass combustion pollutants. Waste-to-energy (WtE) is also high potential, but facing with public opposing as well. Some interviewees suggested installation of high efficiency end-of-pipe treatment facilities together with real-monitoring system must be compulsory for both biomass and WtE power plants. Solar PV is also high potential, but facing a big barrier of land limitation for solar farm installation. Therefore, solar rooftop has become the only option, but facing with a quota system. Reconsideration on the quota system policy as well as more simplify and transparent approval process are highly recommended for promoting solar energy both in the province and the whole country.

Dynamic Risk Advisor – real time bowtie risk assessment monitoring system

This paper describes the development of a Dynamic Risk Advisor to improve safety of operations in fleet marine construction and production oil and gas vessels, well, and subsea equipment in process safety management, providing rig management onshore and offshore tools to assess health, safety, and environment (HSE) performance in real time. This tool uses the bowtie methodology, and provides a useful framework to present a risk ‘picture’ and to satisfy management of health and safety at work regulations. The focus of the methodology is the relationship between safeguards (control systems/equipment, process controls, and human factors) and an overview of the safety process monitoring in a simple and visual form. As facilities and equipment age, those safeguards may become less effective or degraded (different from the condition at the moment of risk assessment creation/revision), risks may increase and that is why they must be monitored using an effective, continuous, and temporal-aware method. Ocyan and Viasat/Intelie created a solution to identify and analyse the hazards dynamically (in real time) using the LIVE streaming data platform, providing an interface that allows the client to manage the current risk profile of their assets using a bowtie diagram. Additionally, this also provides a way for the user to assess the effect of degraded safeguards on complex operations in real time, by monitoring the actual residual risk. The study started in 2019 and was developed together with the Ocyan HSE team and was extended to all fleet due to the positive results. The tool can be accessed from mobile phones and tablets.

Fully convolutional networks for structural health monitoring through multivariate time series classification

We propose a novel approach to structural health monitoring (SHM), aiming at the automatic identification of damage-sensitive features from data acquired through pervasive sensor systems. Damage detection and localization are formulated as classification problems, and tackled through fully convolutional networks (FCNs). A supervised training of the proposed network architecture is performed on data extracted from numerical simulations of a physics-based model (playing the role of digital twin of the structure to be monitored) accounting for different damage scenarios. By relying on this simplified model of the structure, several load conditions are considered during the training phase of the FCN, whose architecture has been designed to deal with time series of different length. The training of the neural network is done before the monitoring system starts operating, thus enabling a real time damage classification. The numerical performances of the proposed strategy are assessed on a numericalbenchmark case consisting of an eight-story shear building subjected to two load types, one of which modeling random vibrations due to low-energy seismicity. Measurement noise has been added to the responses of the structure to mimic the outputs of a real monitoring system. Extremely good classification capacities are shown: among the nine possible alternatives (represented by the healthy state and by a damage at any floor), damage is correctly classified in up to 95 % of cases, thus showing the strong potential of the proposed approach in view of the application to real-life cases.

English

English

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

Now a day’s water pollution is one of the biggest fears for the green globalization. To prevent the water pollution, first we have to estimate the water parameters like pH, turbidity, temperature and TDS as the variations in the values of these parameters point towards the presence of pollutants. In this project we design and develop a low cost system for real time monitoring of the water quality in IoT. Nowadays Internet of Things (IoT) and Remote Sensing (RS) techniques are used in different area of research for monitoring, collecting and analysis data from remote locations. This project proposes a Sensor-Based Water Quality Monitoring System which is used for measuring physical and chemical parameters of the water. The parameters such as Temperature, pH, TDS and water level of the water can be measured and the system provides a visual image of interior part of water container using raspberry pi camera. The measured values from the sensors can be processed by the core controller. The Raspberry Pi model can be used as a core controller. Finally, the sensor data can be viewed on internet using cloud storage (Thingspeak). The system also provides an alert to a remote user, when there is a deviation of water quality parameters from the predefined set of standard values. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered.

The Real Time Water Quality Monitoring System Based On IoT Platform

The Real Time Water Quality Monitoring System Based On IoT Platform

Real Estate Monitoring System Based on Remote Sensing and Image Recognition Technologies

Geoinformation are changing fast, therefore a change detection of real estate must be processed in short time. The increasing resolution of sensed geospatial data creates critically important to develop high performance computing solutions to process geospatial information.The topic of scientific work is the real estate monitoring system based on image recognition and remote sensing technologies. System's practical application is automatic building recognition from LiDAR data using saliency based method, vector map generation and change detection in actual cadastral maps. The scientific work describes high performance computing solution and gives its performance comparison with traditional method.

Conditions for Long-term Monitoring of Safety in Operation of Pipelines

The safety of the high-pressure pipeline systems can negatively affect occurrence of non-standard operational states. This can primarily occur in operation of turbo-compressors at the compressor stations, where we can expect an unwanted vibrating due to existence of the dynamic loading. Let us have another case, the line parts of such pipelines, where the walls could sometimes be weakened by the unwanted corrosion defects. In both cases, the safety of operation can be exposed by additional bending loading of the pipeline e.g. due to decreasing of sub-soil. In this contribution will be introduced monitoring systems which can monitor such negative events in a real time. More closely will be explained selection of suitable sensors and their deployment along the critical place as well as evaluation of measured data and conditions for valuation of the operational safety. Furthermore, there will be presented results obtained from measurements of existing real monitoring systems of safety for given pipeline systems.

Smart home simulation model for synthetic sensor datasets generation

World population is ageing due to longer life expectancy worldwide. There is a trend in elderly people to live alone in their habitual residences in spite of health and safety risks. Smart Homes, intelligent environment systems deployed at elderly homes can act as early warning systems trying to forecast the worsening or exacerbation of the resident chronic conditions. Access to sensor datasets is essential for the development of an efficient real smart home. Procurement of such datasets is subject to several restrictions and difficulties. This paper describes the generation of synthetic datasets by means of a simulation model as a suitable alternative previous to the deployment of a real monitoring system. The collection of synthetic datasets will be used during the next project step to train and evaluate activity recognition methods and algorithms.

Simulation concept of NICA-MPD-SPD Tier0-Tier1 computing facilities

The simulation concept for grid-cloud services of contemporary HENP experiments of the Big Data scale was formulated in practicing the simulation system developed in LIT JINR Dubna. This system is intended to improve the efficiency of the design and development of a wide class of grid-cloud structures by using the work quality indicators of some real system to design and predict its evolution. For these purposes the simulation program is combined with a real monitoring system of the grid-cloud service through a special database (DB). The DB accomplishes acquisition and analysis of monitoring data to carry out dynamical corrections of the simulation. Such an approach allows us to construct a general model pattern which should not depend on a specific simulated object, while the parameters describing this object can be used as input to run the pattern. The simulation of some processes of the NICA-MPD-SPD Tier0-Tier1 distributed computing is considered as an example of our approach applications.

Abstract #267: Use of a Real Time Continous Glucose Monitoring System as an Educational Tool for Patients with Gestational Diabetes

Abstract #267: Use of a Real Time Continous Glucose Monitoring System as an Educational Tool for Patients with Gestational Diabetes

RELATIONSHIP BETWEEN AE SIGNAL STRENGTH AND ABSOLUTE ENERGY IN DETERMINING DAMAGE CLASSIFICATION OF CONCRETE STRUCTURES

The most efficient tools in real monitoring system is acoustic emission (AE). This technique can be used to identify the damage classifications in RC structure. This research paper will mainly focus on the utilization of signal strength and Absolute energy (AE signal) in determining on the damage quantification for RC beam subjected to cyclic load test. The beam specimens size (150 X 250 X 1900) mm were prepared in the laboratory and tested with the four point bending test using cyclic loading together with acoustic emission monitoring system. The results showed that the analysis of AE data parameters is capable of determining the damage classification in concrete structures and the data corresponded to the visual observations during the increased loading cycle.

Real Time Vehicle Monitoring and Tracking System for School Bus via Beagle Bone

Real Time Vehicle Monitoring and Tracking System for School Bus via Beagle Bone

The effect of real time continuous blood glucose monitoring system versus intermittent blood glucose monitoring in critically ill patients under the intensive insulin therapy: a Meta-analysis

Objective To evaluate the value of real time continuous blood glucose monitoring system (RT-CGMS) versus intermittent blood glucose monitoring (IGM) in the critically ill patients under intensive insulin therapy (IIT) . Methods A systematic searching randomized and controlled trials (RCT) in databases was performed for meta-analysis by Review Manager 5.2 software. Outcomes were hypoglycemia episode, alteration of mean blood glucose level, the percentage of time at a blood glucose level within optimal target range, and the early mortalities. Results Six studies, totally 531 patients, were included in this meta-analysis. The pooled SMD of mean blood glucose level was =-0.21 (95% SMD: -0.43 - 0.01, P =0.07) . The pooled SMD of percentage of time at a blood glucose level within optimal target range was 0.20 (95% SMD: -0.09 -0.49, P =0.18) . The pooled OR of hypoglycemia episode frequency was 0.20 (95% CI: 0.09 -0.43, P <0.01) . The pooled OR of early mortalities was 0.35 (95% CI: 0.14 -0.89, P =0.03) . Conclusions In critically ill patients under the intensive insulin therapy, RT-CGMS had obvious beneficial effect on reducing hypoglycemic events. RT-CGMS had no obvious beneficial effect on keeping blood glucose level within optimal target range. Key words: Critically ill patients; Real time continuous glucose monitoring system; Intensive insulin therapy; Meta-analysis

Instantiated First Order Qualitative Choice Logic for an efficient handling of alerts correlation

Intrusion Detection Systems (IDS) are necessary and important tools for monitoring information systems. However they produce a huge quantity of alerts. Alerts correlation is a process that reduces the number of alerts reported by intrusion detection systems. In this paper, we propose a new algorithm for a logical-based alerts correlation approach that integrates: security operator's knowledge and preferences. The representation and the reasoning on these knowledge and preferences are done using a new logic called Instantiated First Order Qualitative Choice Logic (IFO-QCL). Our modeling views an alert as an interpretation which allows us to have an efficient algorithm that performs the correlation process in a polynomial time. This paper also provides experimental results which are achieved on datasets issued from a real monitoring system.

Web-Service Development of the Grid-Cloud Simulation Tools

Abstract A new system for grid and cloud services simulation is presented. The system is focused on improving efficiency of the grid/cloud systems development by using quality indicators of some real system to design and predict its evolution. For this purpose the simulation program are combined with real monitoring system of the grid/cloud service through a special database. The database development and web interface are described.