Application of fuzzy logic and machine learning techniques to improve inherently safer design in process safety management: A brief study

2021 ◽  
Author(s):  
Baiju Karun ◽  
Renjith V. R. ◽  
Sudheep Elayidom
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Safety Management ◽  
Machine Learning Techniques ◽  
Process Safety ◽  
Inherently Safer Design ◽  
Learning Techniques

scholarly journals Comparing Classical and Modern Machine Learning Techniques for Monitoring Pedestrian Workers in Top-View Construction Site Video Sequences

2020 ◽  
Vol 10 (23) ◽  
pp. 8466
Author(s):  
Marcel Neuhausen ◽  
Dennis Pawlowski ◽  
Markus König
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Kalman Filter ◽  
Safety Management ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Learning Approaches ◽  
Construction Sites ◽  
Learning Techniques ◽  
Different Characteristics

Keeping an overview of all ongoing processes on construction sites is almost unfeasible, especially for the construction workers executing their tasks. It is difficult for workers to concentrate on their work while paying attention to other processes. If their workflows in hazardous areas do not run properly, this can lead to dangerous accidents. Tracking pedestrian workers could improve the productivity and safety management on construction sites. For this, vision-based tracking approaches are suitable, but the training and evaluation of such a system requires a large amount of data originating from construction sites. These are rarely available, which complicates deep learning approaches. Thus, we use a small generic dataset and juxtapose a deep learning detector with an approach based on classical machine learning techniques. We identify workers using a YOLOv3 detector and compare its performance with an approach based on a soft cascaded classifier. Afterwards, tracking is done by a Kalman filter. In our experiments, the classical approach outperforms YOLOv3 on the detection task given a small training dataset. However, the Kalman filter is sufficiently robust to compensate for the drawbacks of YOLOv3. We found that both approaches generally yield a satisfying tracking performances but feature different characteristics.

scholarly journals Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

2021 ◽  
Author(s):  
Silvia Maria Ansaldi ◽  
Patrizia Agnello ◽  
Annalisa Pirone ◽  
Maria Rosaria Vallerotonda
Keyword(s):  
Machine Learning ◽  
Continuous Improvement ◽  
Management System ◽  
Safety Management ◽  
Near Miss ◽  
Machine Learning Techniques ◽  
Near Misses ◽  
Robust Model ◽  
Learning Techniques ◽  
Relation Model

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

scholarly journals Near Miss Archive: A Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

2021 ◽  
Vol 13 (15) ◽  
pp. 8456
Author(s):  
Silvia Maria Ansaldi ◽  
Patrizia Agnello ◽  
Annalisa Pirone ◽  
Maria Rosaria Vallerotonda
Keyword(s):  
Machine Learning ◽  
Continuous Improvement ◽  
Management System ◽  
Safety Management ◽  
Near Miss ◽  
Machine Learning Techniques ◽  
Near Misses ◽  
Robust Model ◽  
Learning Techniques ◽  
Relation Model

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses, and possible anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in 5 years by some 100 inspectors throughout Italy. This paper discusses in detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, machine learning techniques have been used for preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

scholarly journals Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 74
Author(s):  
Bhavesh Pandya ◽  
Amir Pourabdollah ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Random Forest ◽  
Web Services ◽  
Real Time ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Time Fall

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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