scholarly journals DEVELOPMENT OF AUTOMATIC MODE DETECTION SYSTEM BY IMPLEMENTING THE STATISTICAL ANALYSIS OF SHIP DATA TO MONITOR THE PERFORMANCE

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
I Zaman ◽  
K Pazouki ◽  
R Norman ◽  
S Younessi ◽  
S Coleman
Keyword(s):  
Statistical Analysis ◽  
Technology Implementation ◽  
Human Error ◽  
Performance Monitoring ◽  
Detection System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Shipping Industry ◽  
Autonomous Operation ◽  
Marine Industry

The shipping industry depends on a global regulatory framework to operate efficiently. The industry is currently facing various technical and regulatory challenges. Performance monitoring, vessel optimisation, reduction of emissions and maintenance have become high priorities for ship operators. The marine industry is also moving towards autonomous operation to reduce human error. The rate of sensor technology implementation has increased and also raised new technological challenges. The analysis of sensor data creates new challenges to achieve operational excellence. This paper presents the implementation of statistical analysis on ship data and develops a system to automatically detect the vessel operational modes based on sensor data.

scholarly journals Development of Automatic Mode Detection System by Implementing the Statistical Analysis of Ship Data to Monitor the Performance

2017 ◽  
Vol Vol 159 (A3) ◽  
Author(s):  
I Zaman ◽  
K Pazouki ◽  
R Norman ◽  
S Younessi ◽  
S Coleman
Keyword(s):  
Statistical Analysis ◽  
Technology Implementation ◽  
Human Error ◽  
Performance Monitoring ◽  
Detection System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Shipping Industry ◽  
Autonomous Operation ◽  
Marine Industry

The shipping industry depends on a global regulatory framework to operate efficiently. The industry is currently facing various technical and regulatory challenges. Performance monitoring, vessel optimisation, reduction of emissions and maintenance have become high priorities for ship operators. The marine industry is also moving towards autonomous operation to reduce human error. The rate of sensor technology implementation has increased and also raised new technological challenges. The analysis of sensor data creates new challenges to achieve operational excellence. This paper presents the implementation of statistical analysis on ship data and develops a system to automatically detect the vessel operational modes based on sensor data.

scholarly journals Autonomous Detection System for Non-Hard-Hat Use at Construction Sites Using Sensor Technology

2021 ◽  
Vol 13 (3) ◽  
pp. 1102
Author(s):  
Jung Hoon Kim ◽  
Byung Wan Jo ◽  
Jun Ho Jo ◽  
Yun Sung Lee ◽  
Do Keun Kim
Keyword(s):  
Heart Rate ◽  
Detection System ◽  
Low Cost ◽  
Sensor Data ◽  
Sensor Technology ◽  
Construction Sites ◽  
Novel Method ◽  
Validation Tests ◽  
Microcontroller Unit ◽  
Hard Hat

In this study, we present a novel method of detecting hard hat use on construction sites using a modified version of an off-the-shelf wearable device. The data-transmitting node of the device contained two sensors, a photoplethysmogram (PPG) and accelerometers (Acc), along with two modules, a global positioning system (GPS) and a low-power wide-area (LoRa) network module. All the components were embedded into a microcontroller unit (MCU) in addition to the power supply. The receiving node included a server that displayed the results via both the Internet of Things (IoT) and smartphones. The LoRa network connected two nodes so that it could function in larger areas such as construction sites at a relatively low cost. The proposed method analyzes the data from a PPG sensor located on the hard hat chin strap and automatically notifies a manager when a worker is not wearing the required hard hat at the site. In addition, by utilizing the PPG sensor data, a heart rate abnormality-detecting feature was added based on an age-adjusted maximum heart rate formula. In validation tests, various PPG sensor locations and shapes were studied, and the results demonstrated the smallest error in the circular shaped sensor located at the upper neck (0.56%). Finally, an IoT monitoring page was created to monitor heart rate abnormalities while identifying hard hat use violations via both PCs and smart phones.

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

Validation of Wearable Sensor Technology to Measure Social Proximity of Young Children With Autism Spectrum Disorders

2021 ◽  
pp. 108835762110282
Author(s):  
Sarah N. Douglas ◽  
Yan Shi ◽  
Saptarshi Das ◽  
Subir Biswas
Keyword(s):  
Autism Spectrum Disorders ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Sensor System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Future Research ◽  
Social Proximity ◽  
Children With Asd ◽  
Spectrum Disorders

Children with autism spectrum disorders (ASD) struggle to develop appropriate social skills, which can lead to later social rejection, isolation, and mental health concerns. Educators play an important role in supporting and monitoring social skill development for children with ASD, but the tools used by educators are often tedious, lack suitable sensitivity, provide limited information to plan interventions, and are time-consuming. Therefore, we conducted a study to evaluate the use of a sensor system to measure social proximity between three children with ASD and their peers in an inclusive preschool setting. We compared video-coded data with sensor data using point-by-point agreement to measure the accuracy of the sensor system. Results suggest that the sensor system can adequately measure social proximity between children with ASD and their peers. The next steps for sensor system validation are discussed along with clinical and educational implications, limitations, and future research directions.

A Method for Marine Human Error Probability Estimate: APJE-SLIM

2011 ◽  
Vol 97-98 ◽  
pp. 825-830 ◽  
Author(s):  
Yong Tao Xi ◽  
Chong Guo
Keyword(s):  
Collision Avoidance ◽  
Human Error ◽  
Shipping Industry ◽  
Human Reliability ◽  
Combined Method ◽  
Probability Estimate ◽  
Human Errors ◽  
Performance Shaping Factors ◽  
Industry Research ◽  
Human Error Probability

Safety is the eternal theme in shipping industry. Research shows that human error is the main reason of maritime accidents. Therefore, it is very necessary to research marine human errors, to discuss the contexts which caused human errors and how the contexts effect human behavior. Based on the detailed investigation of human errors in collision avoidance behavior which is the most key mission in navigation and the Performance Shaping Factors (PSFs), human reliability of mariners in collision avoidance was analyzed by using the integration of APJE and SLIM. Result shows that this combined method is effective and can be used for the research of maritime human reliability.

scholarly journals Predicting Parkinson’s Disease Medication Regimen Using Sensor Technology

2021 ◽  
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Classification Model ◽  
Sensor Data ◽  
Sensor Technology ◽  
Medication Regimen ◽  
Levodopa Dose ◽  
Subjective Data ◽  
Random Forest Classification

Abstract Parkinson’s disease (PD) medication treatment planning is generally based on subjective data through in-office, physicianpatient interactions. The Personal KinetiGraphTM (PKG) has shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to subtype patients based on levodopa regimens and response. We apply k-means clustering to a dataset of with-in-subject Parkinson’s medication changes—clinically assessed by the PKG and Hoehn & Yahr (H&Y) staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective PKG data and demographic information. Clinically relevant clusters were developed based on longitudinal dopaminergic regimens—partitioned by levodopa dose, administration frequency, and total levodopa equivalent daily dose—with the PKG increasing cluster granularity compared to the H&Y staging. A random forest classifier was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 87:9 ±1:3

scholarly journals Embedded Programmable Web-based ECG Monitoring & Detection System Using a Fast Algorithm

2016 ◽  
pp. 141-145
Author(s):  
K. Rajamohan ◽  
K.Hanumantha Rao ◽  
T. Malyadri
Keyword(s):  
Critical Care ◽  
Electrical Activity ◽  
Fast Algorithm ◽  
Human Error ◽  
Detection System ◽  
Ecg Monitoring ◽  
Web Based ◽  
Ecg Interpretation ◽  
Ecg Data

The physicians have to interpret this large amount of ECG data to search for only a few abnormal beats in the ECG. Physicians may overlook some abnormal cycles due to fatigue and human error in interpreting such a large amount of data. Therefore, there is an urgent need for an automatic ECG interpreting system to help to reduce the burden of ECG interpretation. This proposed system is expected to monitor the electrical activity of heart of the patient under critical care more conveniently and accurately for diagnosing.

scholarly journals Applying Machine Learning for Improving Performance Classification on Driving Behavior

2021 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo
Keyword(s):  
Machine Learning ◽  
Traffic Accident ◽  
Large Scale ◽  
Detection System ◽  
Difficult Problem ◽  
Sensor Data ◽  
Driving Safety ◽  
Support Vector ◽  
Classification Methods ◽  
Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

scholarly journals Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7919
Author(s):  
Sjoerd van Ratingen ◽  
Jan Vonk ◽  
Christa Blokhuis ◽  
Joost Wesseling ◽  
Erik Tielemans ◽  
...  
Keyword(s):  
Low Cost ◽  
Added Value ◽  
Sensor Data ◽  
Sensor Technology ◽  
Monitoring Networks ◽  
Technical Problems ◽  
Eu Directive ◽  
Short Period ◽  
No2 Sensor ◽  
One Year

Low-cost sensor technology has been available for several years and has the potential to complement official monitoring networks. The current generation of nitrogen dioxide (NO2) sensors suffers from various technical problems. This study explores the added value of calibration models based on (multiple) linear regression including cross terms on the performance of an electrochemical NO2 sensor, the B43F manufactured by Alphasense. Sensor data were collected in duplicate at four reference sites in the Netherlands over a period of one year. It is shown that a calibration, using O3 and temperature in addition to a reference NO2 measurement, improves the prediction in terms of R2 from less than 0.5 to 0.69–0.84. The uncertainty of the calibrated sensors meets the Data Quality Objective for indicative methods specified by the EU directive in some cases and it was verified that the sensor signal itself remains an important predictor in the multilinear regressions. In practice, these sensors are likely to be calibrated over a period (much) shorter than one year. This study shows the dependence of the quality of the calibrated signal on the choice of these short (monthly) calibration and validation periods. This information will be valuable for determining short-period calibration strategies.

scholarly journals How User-Centered Design Supports Situation Awareness for Complex Interfaces

2021 ◽  
pp. 21-35
Author(s):  
Jacob D. Oury ◽  
Frank E. Ritter
Keyword(s):  
Situation Awareness ◽  
Traffic Control ◽  
Human Error ◽  
Detection System ◽  
Error Rates ◽  
User Centered Design ◽  
Design Philosophy ◽  
System States ◽  
Machine Systems ◽  
User Centered

AbstractThis chapter moves the discussion of how to design an operation center down a level towards implementation. We present user-centered design (UCD) as a distinct design philosophy to replace user experience (UX) when designing systems like the Water Detection System (WDS). Just like any other component (e.g., electrical system, communications networks), the operator has safe operating conditions, expected error rates, and predictable performance, albeit with a more variable range for the associated metrics. However, analyzing the operator’s capabilities, like any other component in a large system, helps developers create reliable, effective systems that mitigate risks of system failure due to human error in integrated human–machine systems (e.g., air traffic control). With UCD as a design philosophy, we argue that situation awareness (SA) is an effective framework for developing successful UCD systems. SA is an established framework that describes operator performance via their ability to create and maintain a mental model of the information necessary to achieve their task. SA describes performance as a function of the operator’s ability to perceive useful information, comprehend its significance, and predict future system states. Alongside detailed explanations of UCD and SA, this chapter presents further guidance and examples demonstrating how to implement these concepts in real systems.

Sign in / Sign up

Export Citation Format

Share Document