Search results for: remote monitoring system

Authors: Alisa Salimova, Jiane Zuo, Christopher Homer

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The objective of this study is to analyse the North Canal riparian zone land use with the help of remote sensing analysis in ArcGis using 30 cloudless Landsat8 open-source satellite images from May to August of 2013 and 2017. Land cover, urban construction, heat island effect, vegetation cover, and water system change were chosen as the main parameters and further analysed to evaluate its impact on the North Canal water quality. The methodology involved the following steps: firstly, 30 cloudless satellite images were collected from the Landsat TM image open-source database. The visual interpretation method was used to determine different land types in a catchment area. After primary and secondary classification, 28 land cover types in total were classified. Visual interpretation method was used with the help ArcGIS for the grassland monitoring, US Landsat TM remote sensing image processing with a resolution of 30 meters was used to analyse the vegetation cover. The water system was analysed using the visual interpretation method on the GIS software platform to decode the target area, water use and coverage. Monthly measurements of water temperature, pH, BOD, COD, ammonia nitrogen, total nitrogen and total phosphorus in 2013 and 2017 were taken from three locations of the North Canal in Tongzhou district. These parameters were used for water quality index calculation and compared to land-use changes. The results of this research were promising. The vegetation coverage of North Canal riparian zone in 2017 was higher than the vegetation coverage in 2013. The surface brightness temperature value was positively correlated with the vegetation coverage density and the distance from the surface of the water bodies. This indicates that the vegetation coverage and water system have a great effect on temperature regulation and urban heat island effect. Surface temperature in 2017 was higher than in 2013, indicating a global warming effect. The water volume in the river area has been partially reduced, indicating the potential water scarcity risk in North Canal watershed. Between 2013 and 2017, urban residential, industrial and mining storage land areas significantly increased compared to other land use types; however, water quality has significantly improved in 2017 compared to 2013. This observation indicates that the Tongzhou Water Restoration Plan showed positive results and water management of Tongzhou district had been improved.

Keywords: North Canal, land use, riparian vegetation, river ecology, remote sensing

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Authors: Svilen Dimitrov, Manthan Pancholi, Norbert Schmitz, Didier Stricker

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This paper presents the end-to-end development of a wearable motion sensing harness consisting of computational unit and four inertial measurement units to control three smartphone therapeutic games for children. The inertial data is processed in real time to obtain lower body motion information like knee raises, feet taps and squads. By providing a Wi-Fi connection interface the sensor harness acts wireless remote control for smartphone applications. By performing various lower body movements the users provoke corresponding game state changes. In contrary to the current similar offers, like Nintendo Wii Remote, Xbox Kinect and Playstation Move, this product, consisting of the sensor harness and the applications on top of it, are fully wearable, which means they do not rely on the user to be bound to concrete soft- or hardwareequipped space.

Keywords: wearable harness, inertial measurement units, smartphone therapeutic games, motion tracking, lower-body activity monitoring

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Zheng DianXun, Cheng Bo, Lin Hetong

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This paper focuses on the orbit avoidance strategies of optical remote sensing satellite. The optical remote sensing satellite, moving along the Sun-synchronous orbit, is equipped with laser warning equipment to alert CCD camera from laser attacks. There are three ways to protect the CCD camera: closing the camera cover, satellite attitude maneuver and satellite orbit avoidance. In order to enhance the safety of optical remote sensing satellite in orbit, this paper explores the strategy of satellite avoidance. The avoidance strategy is expressed as the evasion of pre-determined target points in the orbital coordinates of virtual satellite. The so-called virtual satellite is a passive vehicle which superposes the satellite at the initial stage of avoidance. The target points share the consistent cycle time and the same semi-major axis with the virtual satellite, which ensures the properties of the satellite’s Sun-synchronous orbit remain unchanged. Moreover, to further strengthen the avoidance capability of satellite, it can perform multi-target-points avoid maneuvers. On occasions of fulfilling the satellite orbit tasks, the orbit can be restored back to virtual satellite through orbit maneuvers. Thereinto, the avoid maneuvers adopts pulse guidance. And the fuel consumption is also optimized. The avoidance strategy discussed in this article is applicable to optical remote sensing satellite when it is encountered with hostile attack of space-based laser anti-satellite.

Keywords: optical remote sensing satellite, satellite avoidance, virtual satellite, avoid target-point, avoid maneuver

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Authors: Keden Montgomery, Kathie Ardzejewska, Alison Casey, Rosemarie Hogan

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Research was undertaken to explore the question “what is known about the experiences of regional, rural and remote Australian women undertaking a Bachelor of Nursing program delivered online?”. The findings will support future research aimed at improving the retention and completion rates of women studying nursing in regional, rural and remote areas.  There is a critical shortage of nurses working in regional, rural and remote (RRR) Australia. It is well supported that this shortage of nurses is most likely to be addressed by nursing students who are completing their studies in RRR areas. Despite this, students from RRR Australia remain an equity group and experience poorer outcomes than their metropolitan counterparts. Completion rates for RRR students who enrol in tertiary education courses are much less than students from metropolitan areas. In addition to this, RRR students are less likely than students from metropolitan areas to gain a tertiary level qualification at all, and even less likely to gain a Bachelor level degree which is required for Registered Nurses. Supporting students to remain in regional, rural and remote areas while they study reduces the need for students to relocate to metropolitan areas and to continue living and working in RRR areas after graduation. This research holds implications for workforce shortages internationally.

Keywords: nurse education, online education, regional, rural, remote, workforce

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Authors: Clif P. Lewis, Ise-Lu Möller

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The COVID-19 pandemic has had a profound impact on employment. The ‘future of work’ is now the ‘present of work’. Changes in the social context within which organisations are embedded necessitated drastic changes in how we work. Through the leveraging of technology and changes in mindset, we have seen exciting innovations in the world of work. This global shift in the context of employment offers a unique opportunity to examine a key unresolved issue in the study of Burnout, namely contextual antecedents. This study answers the call for deeper empirical insight into the contexts within which Burnout occur. We explore the emergence of Burnout within a remote work context by using survey data that incorporates the latest global work trends into the Areas of Worklife framework.

Keywords: burnout, remote work, pandemic, wellness

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Authors: Juan Heredia, Naci Dilekli

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The Ecuadorian Rainforest has been polluted for almost 60 years with little to no regard to oversight, law, or regulations. The consequences have been vast environmental damage such as pollution and deforestation, as well as sickness and the death of many people and animals. The aim of this paper is to quantify and localize the polluted zones, which something that has not been conducted and is the first step for remediation. To approach this problem, multi-spectral Remote Sensing imagery was utilized using a novel algorithm developed for this study, based on four normalized indices available in the literature. The algorithm classifies the pixels in polluted or healthy ones. The results of this study include a new algorithm for pixel classification and quantification of the polluted area in the selected image. Those results were finally validated by ground control points found in the literature. The main conclusion of this work is that using hyperspectral images, it is possible to identify polluted vegetation. The future work is environmental remediation, in-situ tests, and more extensive results that would inform new policymaking.

Keywords: remote sensing, oil pollution quatification, amazon forest, hyperspectral remote sensing

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Authors: G. Yoga Sairam Teja, K. Harsha Vardhan, A. Vinay Kumar, K. Nithish Kumar, Ch. Shanthi Priyag

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The emergence of the Internet of Things (IoT) has facilitated better device control and monitoring in the modern world. The constant monitoring of a patient would be drastically altered by the usage of IoT in healthcare. As we've seen in the case of the COVID-19 pandemic, it's important to keep oneself untouched while continuously checking on the patient's heart rate and temperature. Additionally, patients with paralysis should be closely watched, especially if they are elderly and in need of special care. Our "IoT BASED PATIENT HEALTH MONITORING SYSTEM" project uses IoT to track patient health conditions in an effort to address these issues. In this project, the main board is an 8051 microcontroller that connects a number of sensors, including a heart rate sensor, a temperature sensor (LM-35), and a saline water measuring circuit. These sensors are connected via an ESP832 (WiFi) module, which enables the sending of recorded data directly to the cloud so that the patient's health status can be regularly monitored. An LCD is used to monitor the data in offline mode, and a buzzer will sound if any variation from the regular readings occurs. The data in the cloud may be viewed as a graph, making it simple for a user to spot any unusual conditions.

Keywords: IoT, ESP8266, 8051 microcontrollers, sensors

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Authors: Amir AlAmeeri, Mohamed Ibrahim

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Various data points are constantly being measured in the production system, and due to the nature of the wells, these data points, such as pressure, temperature, water cut, etc.., fluctuations are constant, which requires high frequency monitoring and collection. It is a very difficult task to analyze these parameters manually using spreadsheets and email. An automated system greatly enhances efficiency, reduce errors, the need for constant emails which take up disk space, and frees up time for the operator to perform other critical tasks. Various production data is being recorded in an oil field, and this huge volume of data can be seen as irrelevant to some, especially when viewed on its own with no context. In order to fully utilize all this information, it needs to be properly collected, verified and stored in one common place and analyzed for surveillance and monitoring purposes. This paper describes how data is recorded by different parties and departments in the field, and verified numerous times as it is being loaded into a repository. Once it is loaded, a final check is done before being entered into a production monitoring system. Once all this is collected, various calculations are performed to report allocated production. Calculated production data is used to report field production automatically. It is also used to monitor well and surface facility performance. Engineers can use this for their studies and analyses to ensure field is performing as it should be, predict and forecast production, and monitor any changes in wells that could affect field performance.

Keywords: automation, oil production, Cheleken, exploration and production (E&P), Caspian Sea, allocation, forecast

Procedia PDF Downloads 156

Authors: Gabriela-Andreea Despescu, Mădălina Mavrodin, Gheorghe Lăzăroiu, S. Nacu, R. Băstinaş

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The Monitoring of Surface Water Bodies (Rivers) from Tisa Catchment Area - Maramureş County in 2014. This study is focused on the monitoring and evaluation of river’s water bodies from Maramureş County, using the methodology associated with the EU Water Framework Directive 60/2000. Thus, in the first part are defined the theoretical terms of monitoring activities related to the water bodies’ quality and the specific features of those we can find in the studied area. There are presented the water bodies’ features, quality indicators and the monitoring frequencies for the rivers situated in the Tisa catchment area. The results have shown the actual ecological and chemical state of those water bodies, in relation with the standard values mentioned through the Water Framework Directive.

Keywords: monitoring, surveillance, water bodies, quality

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Authors: John P. O’Sullivan, Thomas M. P. Ratouis, Michael J. O’Sullivan

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As the use of geothermal energy grows internationally more effort is required to monitor and protect areas with rare and important geothermal surface features. A number of approaches are presented for developing and calibrating numerical geothermal reservoir models that are capable of accurately representing geothermal surface features. The approaches are discussed in the context of cases studies of the Rotorua geothermal system and the Orakei-korako geothermal system, both of which contain important surface features. The results show that models are able to match the available field data accurately and hence can be used as valuable tools for predicting the future response of the systems to changes in use.

Keywords: geothermal reservoir models, surface features, monitoring, TOUGH2

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Authors: Dianxun Zheng, Wuxing Jing, Lin Hetong

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Optical remote sensing satellite, always running on the Sun-synchronous orbit, equipped laser warning equipment to alert CCD camera from laser attack. There have three ways to protect the CCD camera, closing the camera cover satellite attitude maneuver and satellite orbit avoidance. In order to enhance the safety of optical remote sensing satellite in orbit, this paper explores the strategy of satellite avoidance. The avoidance strategy is expressed as the evasion of pre-determined target points in the orbital coordinates of virtual satellite. The so-called virtual satellite is a passive vehicle which superposes a satellite at the initial stage of avoidance. The target points share the consistent cycle time and the same semi-major axis with the virtual satellite, which ensures the properties of the Sun-synchronous orbit remain unchanged. Moreover, to further strengthen the avoidance capability of satellite, it can perform multi-object avoid maneuvers. On occasions of fulfilling the orbit tasks of the satellite, the orbit can be restored back to virtual satellite through orbit maneuvers. There into, the avoid maneuvers adopts pulse guidance. and the fuel consumption is also optimized. The avoidance strategy discussed in this article is applicable to avoidance for optical remote sensing satellite when encounter the laser hostile attacks.

Keywords: optical remote sensing satellite, always running on the sun-synchronous

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Authors: Vedant Janapaty

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Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

Procedia PDF Downloads 104

Authors: D. Courault, L. Hossard, V. Demarez, E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, F. Ruget

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In the global change context, efficient management of the available resources has become one of the most important topics, particularly for sustainable crop development. Timely assessment with high precision is crucial for water resource and pest management. Rice cultivated in Southern France in the Camargue region must face a challenge, reduction of the soil salinity by flooding and at the same time reduce the number of herbicides impacting negatively the environment. This context has lead farmers to diversify crop rotation and their agricultural practices. The objective of this study was to evaluate this crop diversity both in crop systems and in agricultural practices applied to rice paddy in order to quantify the impact on the environment and on the crop production. The proposed method is based on the combined use of crop models and multispectral data acquired from the recent Sentinel 2 satellite sensors launched by the European Space Agency (ESA) within the homework of the Copernicus program. More than 40 images at fine spatial resolution (10m in the optical range) were processed for 2016 and 2017 (with a revisit time of 5 days) to map crop types using random forest method and to estimate biophysical variables (LAI) retrieved by inversion of the PROSAIL canopy radiative transfer model. Thanks to the high revisit time of Sentinel 2 data, it was possible to monitor the soil labor before flooding and the second sowing made by some farmers to better control weeds. The temporal trajectories of remote sensing data were analyzed for various rice cultivars for defining the main parameters describing the phenological stages useful to calibrate two crop models (STICS and SAFY). Results were compared to surveys conducted with 10 farms. A large variability of LAI has been observed at farm scale (up to 2-3m²/m²) which induced a significant variability in the yields simulated (up to 2 ton/ha). Observations on more than 300 fields have also been collected on land use. Various maps were elaborated, land use, LAI, flooding and sowing, and harvest dates. All these maps allow proposing a new typology to classify these paddy crop systems. Key phenological dates can be estimated from inverse procedures and were validated against ground surveys. The proposed approach allowed to compare the years and to detect anomalies. The methods proposed here can be applied at different crops in various contexts and confirm the potential of remote sensing acquired at fine resolution such as the Sentinel2 system for agriculture applications and environment monitoring. This study was supported by the French national center of spatial studies (CNES, funded by the TOSCA).

Keywords: agricultural practices, remote sensing, rice, yield

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Authors: Guenther Schuh, Christina Koenig, Nico Schoen, Markus Wellensiek

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Identification and selection of appropriate product and manufacturing technologies are key factors for competitiveness and market success of technology-based companies. Therefore many companies perform technology intelligence (TI) activities to ensure the identification of evolving technologies at the right time. Technology monitoring is one of the three base activities of TI, besides scanning and scouting. As the technological progress is accelerating, more and more technologies are being developed. Against the background of limited resources it is therefore necessary to focus TI activities. In this paper, we propose a concept for defining appropriate search fields for technology monitoring. This limitation of search space leads to more concentrated monitoring activities. The concept will be introduced and demonstrated through an anonymized case study conducted within an industry project at the Fraunhofer Institute for Production Technology. The described concept provides a customized monitoring approach, which is suitable for use in technology-oriented companies especially those that have not yet defined an explicit technology strategy. It is shown in this paper that the definition of search fields and search tasks are suitable methods to define topics of interest and thus to direct monitoring activities. Current as well as planned product, production and material technologies as well as existing skills, capabilities and resources form the basis of the described derivation of relevant search areas. To further improve the concept of technology monitoring the proposed concept should be extended during future research e.g. by the definition of relevant monitoring parameters.

Keywords: monitoring radar, search field, technology intelligence, technology monitoring

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Authors: Bo Wang

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The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

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Authors: Francesco Morgan Bono, Simone Cinquemani

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This research aims to enhance bridge monitoring by employing innovative techniques that incorporate exogenous factors into the modeling of sensor signals, thereby improving long-term predictability beyond traditional static methods. Using real datasets from two different bridges equipped with Linear Variable Displacement Transducer (LVDT) sensors, the study investigates the fundamental principles governing sensor behavior for more precise long-term forecasts. Additionally, the research evaluates performance on noisy and synthetically damaged data, proposing a residual-based alarm system to detect anomalies in the bridge. In summary, this novel approach combines advanced modeling, exogenous factors, and anomaly detection to extend prediction horizons and improve preemptive damage recognition, significantly advancing structural health monitoring practices.

Keywords: structural health monitoring, dynamic models, sindy, railway bridges

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Authors: Jungho Moon, Lae-Jeong Park

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This paper introduces a signal monitoring program developed with a view to helping electrical engineering students get familiar with sensors with digital output. Because the output of digital sensors cannot be simply monitored by a measuring instrument such as an oscilloscope, students tend to have a hard time dealing with digital sensors. The monitoring program runs on a PC and communicates with an MCU that reads the output of digital sensors via an asynchronous communication interface. Receiving the sensor data from the MCU, the monitoring program shows time and/or frequency domain plots of the data in real time. In addition, the monitoring program provides a serial terminal that enables the user to exchange text information with the MCU while the received data is plotted. The user can easily observe the output of digital sensors and configure the digital sensors in real time, which helps students who do not have enough experiences with digital sensors. Though the monitoring program was programmed in the Matlab programming language, it runs without the Matlab since it was compiled as a standalone executable.

Keywords: digital sensor, MATLAB, MCU, signal monitoring program

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Authors: Mingyang Sun, Guozheng Yan, Dasheng Liu, Lei Yang

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Driven by the demand of intelligent monitoring in rehabilitation centers or hospitals, a high accuracy real-time location system based on UWB (ultra-wideband) technology was proposed. The system measures precise location of a specific person, traces his movement and visualizes his trajectory on the screen for doctors or administrators. Therefore, doctors could view the position of the patient at any time and find them immediately and exactly when something emergent happens. In our design process, different algorithms were discussed, and their errors were analyzed. In addition, we discussed about a , simple but effective way of correcting the antenna delay error, which turned out to be effective. By choosing the best algorithm and correcting errors with corresponding methods, the system attained a good accuracy. Experiments indicated that the ranging error of the system is lower than 7 cm, the locating error is lower than 20 cm, and the refresh rate exceeds 5 times per second. In future works, by embedding the system in wearable IoT (Internet of Things) devices, it could provide not only physical parameters, but also the activity status of the patient, which would help doctors a lot in performing healthcare.

Keywords: intelligent monitoring, ultra-wideband technology, real-time location, IoT devices, smart healthcare

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Authors: P. Senthilraja, C. Rukumani Khandhan, M. Palaniappan, S. L. Rama, P. Sai Sushimitha, R. Madhan, J. Vinumathi, N. Vijayarangan

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Nowadays, the logistics achieve a vast improvement in efficient delivery of goods. The technology improvement also helps to improve its development, but still the owners of transport vehicles face problems, i.e., fuel theft in vehicles by the drivers or by an unknown person. There is no proper solution to overcome the problems. This scheme is to determine the amount of fuel that has been stolen and also to determine the position of the vehicle at a particular time using the technologies like GPS, GSM, ultrasonic fuel level sensor and numeric lock system. The ultrasonic sensor uses the ultrasonic waves to calculate the height of the tank up to which the fuel is available. Based on height it is possible to calculate the amount of fuel. The Global Positioning System (GPS) is a satellite-based navigation system. The scientific community uses GPS for its precision timing capability and position information. The GSM provides the periodic information about the fuel level. A numeric lock system has been provided for fuel tank opening lever. A password is provided to access the fuel tank lever and this is authenticated only by the driver and the owner. Once the fuel tank is opened an alert is sent to owner through a SMS including the timing details. Third party monitoring software is a user interface that updates the information automatically into the database which helps to retrieve the data as and when required. Third party monitoring software provides vehicle’s information to the owner and also shows the status of the vehicle. The techniques that are to be proposed will provide an efficient output. This project helps to overcome the theft and hence to put forth fuel economy.

Keywords: fuel theft, third party monitoring software, bioinformatics, biomedicine

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Authors: Girish M. Bhatia

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Most of the important structures in refinery complex are RCC Structures for which in-depth structural monitoring and inspection is required for incessant service. Reinforced concrete structures can be under threat from a combination of insidious challenges due to environmental conditions, including temperature and humidity that lead to accelerated deterioration mechanisms like carbonation, as well as marine exposure, above and below ground structures can experience ingress from aggressive ground waters carrying chlorides and sulphates leading to unexpected deterioration that threaten the integrity of a vital structural asset. By application of health monitoring techniques like corrosion monitoring with help of sensor probes, visual inspection of high rise structures with help of drones, it is possible to establish an early warning at the onset of these destructive processes.

Keywords: concrete structures, corrosion sensors, drones, health monitoring

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Authors: Hadjadj Abdechafik, Kious Mecheri, Ameur Aissa

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The objective of this study is to develop a process of treatment of the vibratory signals generated during a horizontal high speed milling process without applying any coolant in order to establish a monitoring system able to improve the machining performance. Thus, many tests were carried out on the horizontal high speed centre (PCI Météor 10), in given cutting conditions, by using a milling cutter with only one insert and measured its frontal wear from its new state that is considered as a reference state until a worn state that is considered as unsuitable for the tool to be used. The results obtained show that the first harmonic follow well the evolution of frontal wear, on another hand a wavelet transform is used for signal processing and is found to be useful for observing the evolution of the wavelet approximations through the cutting tool life. The power and the Root Mean Square (RMS) values of the wavelet transformed signal gave the best results and can be used for tool wear estimation. All this features can constitute the suitable indicators for an effective detection of tool wear and then used for the input parameters of an online monitoring system. Although we noted the remarkable influence of the machining cycle on the quality of measurements by the introduction of a bias on the signal, this phenomenon appears in particular in horizontal milling and in the majority of studies is ignored.

Keywords: flank wear, vibration, milling, signal processing, monitoring

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Authors: Kaarthik K., Harshini S., Karthika M., Kripanandhini T.

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Water is an essential part of living organisms. Major water pollution is caused due to contamination of industrial wastewater in the river. The most important step in bringing wastewater contaminants down to levels that are safe for nature is wastewater treatment. The contamination of river water harms both humans who consume it and the aquatic life that lives there. We introduce a new cloud-based industrial IoT paradigm in this work for real-time control and monitoring of wastewater. The proposed system prevents prohibited entry of industrial wastewater into the plant by monitoring temperature, hydrogen power (pH), CO₂ and turbidity factors from the wastewater input that the wastewater treatment facility will process. Real-time sensor values are collected and uploaded to the cloud by the system using an IoT Wi-Fi Module. By doing so, we can prevent the contamination of industrial wastewater entering the river earlier, and the necessary actions will be taken by the users. The proposed system's results are 90% efficient, preventing water pollution due to industry and protecting human lives.

Keywords: sensors, pH, CO₂, temperature, turbidity

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Authors: Victor A. Okenyi, Kehinde A. Alawode

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Corrosion of reinforcement in concrete structures remains a durability issue in structural engineering with the increasing cost of repair and maintenance. The mechanism and factors influencing reinforcement corrosion in concrete with various electrochemical monitoring techniques including non-destructive, destructive techniques and the roles of sensors have been reviewed with the aim of determining the monitoring technique that proved most effective in determining corrosion parameters and more practicable for the assessment of concrete durability. Electrochemical impedance spectroscopy (EIS) and linear polarization resistance (LPR) techniques showed great performance in evaluating corrosion kinetics and corrosion rate, respectively, while the gravimetric weight loss (GWL) technique provided accurate measurements. However, no single monitoring technique showed to be the ultimate technique, and this calls for more research work in the development of more dynamic monitoring tools capable of considering all possible corrosion factors in the corrosion monitoring process.

Keywords: corrosion, concrete structures, durability, non-destructive technique, sensor

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Authors: Takuya Sumi, Syoko Nukaya, Takashi Kaburagi, Hiroshi Tanaka, Kajiro Watanabe, Yosuke Kurihara

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We propose an unconstrained measurement system for scratching motion based on mathematical model of unconstrained bed sensing method which could measure the bed vibrations due to the motion of the person on the bed. In this paper, we construct mathematical model of the unconstrained bed monitoring system, and we apply the unconstrained bed sensing method to the system for detecting scratching motion. The proposed sensors are placed under the three bed feet. When the person is lying on the bed, the output signals from the sensors are proportional to the magnitude of the vibration due to the scratching motion. Hence, we could detect the subject’s scratching motion from the output signals from ceramic sensors. We evaluated two scratching motions using the proposed system in the validity experiment as follows: First experiment is the subject’s scratching the right side cheek with his right hand, and; second experiment is the subject’s scratching the shin with another foot. As the results of the experiment, we recognized the scratching signals that enable the determination when the scratching occurred. Furthermore, the difference among the amplitudes of the output signals enabled us to estimate where the subject scratched.

Keywords: unconstrained bed sensing method, scratching, body movement, itchy, piezoceramics

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Authors: Haffaf Hafid

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Hypergraphs, after being used to model the structural organization of System of Sytems (SoS) at macroscopic level, has recent trends towards generalizing this powerful representation at different stages of complex system modelling. In this paper, we first describe different applications of hypergraph theory, and step by step, introduce multilevel modeling of SoS by means of integrating Constraint Programming Langages (CSP) dealing with engineering system reconfiguration strategy. As an application, we give an A.C.T Terminal controlled by a set of Intelligent Automated Vehicle.

Keywords: hypergraph model, structural analysis, bipartite graph, monitoring, system of systems, reconfiguration analysis, hypernetwork

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Authors: Carolina Gouveia, José Vieira, Pedro Pinho

Abstract:

The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully.

Keywords: bio-signals, DC component, Doppler effect, ellipse fitting, radar, SDR

Procedia PDF Downloads 141

Authors: Murtadha Kareem, Oliver Faust

Abstract:

Wearable biosensor systems can be designed and developed for health monitoring. There is much interest in both scientific and industrial communities established since 2007. Fundamentally, the cost of healthcare has increased dramatically and the world population is aging. That creates the need to harvest technological improvements with small bio-sensing devices, wireless-communication, microelectronics and smart textiles, that leads to non-stop developments of wearable sensor based systems. There has been a significant demand to monitor patient's health status while the patient leaves the hospital in his/her personal environment. To address this need, there are numerous system prototypes which has been launched in the medical market recently, the aim of that is to provide real time information feedback about patient's health status, either to the patient himself/herself or direct to the supervising medical centre station, while being capable to give a notification for the patient in case of possible imminent health threatening conditions. Furthermore, wearable health monitoring systems comprise new techniques to address the problem of managing and monitoring chronic heart diseases for elderly people. Wearable sensor systems for health monitoring include various types of miniature sensors, either wearable or implantable. To be specific, our proposed system able to measure essential physiological parameter, such as heart rate signal which could be transmitted through Bluetooth to the cloud server in order to store, process, analysis and visualise the data acquisition. The acquired measurements are connected through internet of things to a central node, for instance an android smart phone or tablet used for visualising the collected information on application or transmit it to a medical centre.

Keywords: Wearable sensor, Heart rate, Internet of things, Chronic heart disease

Procedia PDF Downloads 161

Authors: Wannakorn Sangthongngam, Melissa Huerta, Jaewoo Kim, Doyeon Kim

Abstract:

Continuous glucose monitoring systems are essential for diabetics to avoid health complications but come at a costly price, especially when insurance does not fully cover the diabetic testing kits needed. This paper proposes a noninvasive continuous glucose monitoring system to provide an accessible, low-cost, and painless alternative method of accurate glucose measurements to help improve quality of life. Using a light source with a wavelength of 850nm illuminates the fingertip for the photodetector to detect the transmitted light. Utilizing SeeDevice’s photon-assisted tunneling photodetector (PAT-PD)-based QMOS™ sensor, fluctuations of voltage based on photon absorption in blood cells are comparable to traditional glucose measurements. The performance of the proposed method was validated using 4 test participants’ transmitted voltage readings compared with measurements obtained from the Accu-Chek glucometer. The proposed method was able to successfully measure concentrations from linear regression calculations.

Keywords: continuous glucose monitoring, non-invasive continuous glucose monitoring, NIR, photon-assisted tunneling photodetector, QMOS™, wearable device

Procedia PDF Downloads 97

Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

Abstract:

With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

Procedia PDF Downloads 94