scholarly journals IoT Based WSN Ground Water Monitoring System with Cloud-Based Monitoring as a Service (Maas) and Prediction using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 816-821
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Monitoring System ◽  
Indian Subcontinent ◽  
Current System ◽  
Water Monitoring ◽  
Machine Learning Algorithms ◽  
Sensor Nodes ◽  
Raspberry Pi ◽  
Time Data

The main source of water in the Indian Subcontinent is Groundwater. It is also the most rapidly depleting resource due to various reasons such as rampant unchecked irrigation and exploitation of groundwater by industries and other organizations. The current system is limited by short communication range, high power consumption and the system monitors only the water level and the report is available only to the consumer i.e. it is a single-user system. Due to the unavailability of a centralized system to monitor and prevent overuse of water resources, sudden water crises have become a major issue in India. This project aims at implementing an IoT (Internet of things) based water monitoring system that monitors the water level and the quality of groundwater and updates real-time data to the database. This system is designed to monitor the groundwater level of an entire village or a town. It updates the people and the concerned government authorities in case of any decrease in water level and water quality below the threshold value, and also monitors the water consumption during a period and predicts exhaustion time. This system predicts the availability of water in the future based on current demand and usage and the recharge rate using machine learning algorithms. The data collected and the analysis of the data is made available in a Public Cloud. The modules are based on Raspberry Pi Zero, sensor nodes and LoRa (Long Range) Module or Wi-Fi module according to the network requirement for connectivity

scholarly journals A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6349
Author(s):  
Jawad Ahmad ◽  
Johan Sidén ◽  
Henrik Andersson
Keyword(s):  
Machine Learning ◽  
Pressure Distribution ◽  
Real Time ◽  
Monitoring System ◽  
Pressure Ulcers ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Support Vector ◽  
Processing Unit ◽  
Posture Recognition

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

scholarly journals IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
M.RAMANA REDDY
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Monitoring System ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Raspberry Pi

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

Development of Failure Prediction Models for Subsea Blowout Preventers Using Data Analytics and AI

2021 ◽  
Author(s):  
Rodrigo Chamusca Machado ◽  
Fabbio Leite ◽  
Cristiano Xavier ◽  
Alberto Albuquerque ◽  
Samuel Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Analytics ◽  
Historical Data ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Real Time Data ◽  
Operational Data

Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.

Architecting IoT based Healthcare Systems Using Machine Learning Algorithms

2020 ◽  
pp. 40-66
Author(s):  
G. S. Karthick ◽  
P. B. Pankajavalli
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Machine Learning Algorithms ◽  
Daily Activities ◽  
Sensor Data ◽  
Time Data ◽  
Sensory Data ◽  
Hybrid Classification ◽  
Smart Data

The rapid innovations in technologies endorsed the emergence of sensory equipment's connection to the Internet for acquiring data from the environment. The increased number of devices generates the enormous amount of sensor data from diversified applications of Internet of things (IoT). The generation of data may be a fast or real-time data stream which depends on the nature of applications. Applying analytics and intelligent processing over the data streams discovers the useful information and predicts the insights. Decision-making is a prominent process which makes the IoT paradigm qualified. This chapter provides an overview of architecting IoT-based healthcare systems with different machine learning algorithms. This chapter elaborates the smart data characteristics and design considerations for efficient adoption of machine learning algorithms into IoT applications. In addition, various existing and hybrid classification algorithms are applied to sensory data for identifying falls from other daily activities.

scholarly journals Classification of Children’s Sitting Postures Using Machine Learning Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1280 ◽  
Author(s):  
Yong Kim ◽  
Youngdoo Son ◽  
Wonjoon Kim ◽  
Byungki Jin ◽  
Myung Yun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monitoring System ◽  
Multinomial Logistic Regression ◽  
Learning Algorithms ◽  
Feedback System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Future Research ◽  
Support Vector

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

scholarly journals Vision based Office Automation and Security System using Machine Learning and Internet of Things

2018 ◽  
Vol 7 (2.24) ◽  
pp. 42
Author(s):  
Amber Goel ◽  
Apaar Khurana ◽  
Pranav Sehgal ◽  
K Suganthi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Internet Of Things ◽  
Office Automation ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Human Detection ◽  
Raspberry Pi ◽  
Efficient Tool

The paper focuses on two areas, automation and security. Raspberry Pi is the heart of the project and it is fuelled by Machine Learning Algorithms using Open CV and Internet of Things. Face recognition uses Linear Binary Pattern and if an unknown person uses their workstation, a message will be sent to the respective person with the photo of the person who uses the workstation. Face recognition is also being used for uploading attendance and switching ON and OFF appliances automatically. During un-official hours, A Human Detection algorithm is being used to detect the human presence. If an unknown person enters the office, a photo of the person will be taken and sent to the authorities. This technology is a combination of Computer Vision, Machine learning and Internet of things, that serves to be an efficient tool for both automation and security.  

scholarly journals Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 989 ◽  
Author(s):  
Martin Čertický ◽  
Michal Čertický ◽  
Peter Sinčák ◽  
Gergely Magyar ◽  
Ján Vaščák ◽  
...  
Keyword(s):  
Machine Learning ◽  
User Experience ◽  
Electrodermal Activity ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Consumer Experience ◽  
Time Data ◽  
Psychophysiological Measures ◽  
Digital Entertainment ◽  
Group Interviews

Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.

Satellite Fields Digitalization & ALS Optimization with EDGE & Advance Analytics Application

2021 ◽  
Author(s):  
Nitin Johri ◽  
Nimish Pandey ◽  
Sanket Kadam ◽  
Sanjeev Vermani ◽  
Shubham Agarwal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Domain Knowledge ◽  
Machine Learning Algorithms ◽  
Fine Tuning ◽  
Abnormal Behavior ◽  
Final Decision ◽  
Production Loss ◽  
Time Data ◽  
E Mail

Abstract Data monitoring in remote satellite field without any DOF platform is a challenging task but critical for ALS monitoring and optimization. In SRP wells the VFD data collection is important for analysis of downhole pump behavior and system health. SRP maintenance crew collects data from VFDs daily, but it is time consuming and can target only few wells in a day. The steps from requirement of dyna to final decision taken for ALS optimization are mobilizing team, permits approvals, download data, e-mail dynacards, dyna visualization, final decision. The problems with above process were: - Insufficient and discrete data for any post-failure analysis or ALS-optimization Minimal data to investigate the pre failure events The lack of real time monitoring was resulting in well downtime and associated production loss. The combination of IOT, Cloud Computing and Machine learning was implemented to shift from the reactive to proactive approach which helped in ALS Optimization and reduced production loss. The data was transmitted to a Cloud server and further it was transmitted to web-based app. Since thousands of Dynacards are generated in a day, hence it requires automated classification using computer driven pattern recognition techniques. The real time data is used for analysis involving basic statistic and Machine learning algorithms. The critical pump signatures were identified using machine learning libraries and email is generated for immediate action. Several informative dashboards were developed which provide quick analysis of ALS performance. The types of dashboard are as below Well Operational Status Dynacards Interpretation module SRP parameters visualization Machine Learning model calibration module Pump Performance Statistics After collection of enough data and creation of analytical dashboards on the three wells using domain knowledge the gained insights were used for ALS optimization. To keep the model in an evergreen high-confidence prediction state, inputs from domain experts are often required. After regular fine-tuning the prediction accuracy of the ML model increased to 80-85 %. In addition, system was made flexible so that a new algorithm can be deployed when required. Smart Alarms were generated involving statistic and Machine Learning by the system which gives alerts by e-mail if an abnormal behavior or erratic dynacards were identified. This helped in reduction of well downtime in some events which were treated instinctively before. The integration of domain knowledge and digitalization enables an engineer to take informed and effective decisions. The techniques discussed above can be implemented in marginal fields where DOF implementation is logistically and economically challenged. EDGE along with advanced analytics will gain more technological advances and can be used in other potential domains as well in near future.

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