scholarly journals Shipping Accidents Dataset: Data-Driven Directions for Assessing Accident’s Impact and Improving Safety Onboard

Data ◽  
2021 ◽  
Vol 6 (12) ◽  
pp. 129
Author(s):  
Panagiotis Panagiotidis ◽  
Kyriakos Giannakis ◽  
Nikolaos Angelopoulos ◽  
Angelos Liapis
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Historical Data ◽  
Critical Factor ◽  
Data Driven ◽  
Management Systems ◽  
Weather Data ◽  
Incident Management ◽  
Unstructured Information ◽  
Safety Procedures

Recent tragic marine incidents indicate that more efficient safety procedures and emergency management systems are needed. During the 2014–2019 period, 320 accidents cost 496 lives, and 5424 accidents caused 6210 injuries. Ideally, we need historical data from real accident cases of ships to develop data-driven solutions. According to the literature, the most critical factor to the post-incident management phase is human error. However, no structured datasets record the crew’s actions during an incident and the human factors that contributed to its occurrence. To overcome the limitations mentioned above, we decided to utilise the unstructured information from accident reports conducted by governmental organisations to create a new, well-structured dataset of maritime accidents and provide intuitions for its usage. Our dataset contains all the information that the majority of the marine datasets include, such as the place, the date, and the conditions during the post-incident phase, e.g., weather data. Additionally, the proposed dataset contains attributes related to each incident’s environmental/financial impact, as well as a concise description of the post-incident events, highlighting the crew’s actions and the human factors that contributed to the incident. We utilise this dataset to predict the incident’s impact and provide data-driven directions regarding the improvement of the post-incident safety procedures for specific types of ships.

Addressing Driver Disengagement and Proper System Use: Human Factors Recommendations for Level 2 Driving Automation Design

2021 ◽  
pp. 155534342098312
Author(s):  
Alexandra S. Mueller ◽  
Ian J. Reagan ◽  
Jessica B. Cicchino
Keyword(s):  
Human Factors ◽  
Data Driven ◽  
Management Systems ◽  
Design Domain ◽  
Proper System ◽  
Operational Design ◽  
System Use ◽  
Level 2

Level 2 driving automation has the potential to reduce crashes; however, there are known risks when using these systems, particularly as they relate to drivers becoming disengaged from driving. This paper provides data-driven recommendations for Level 2 driving automation design using the best currently available methods to encourage driver engagement and communicate where and how a system can safely be used. Our recommendations pertaining to driver engagement concern driver management systems that monitor the driver for signs of disengagement and return the driver to the loop using a multimodal escalation process with attention reminders, countermeasures for sustained noncompliance to the attention reminders, and proactive methods for keeping drivers engaged with respect to driver-system interactions and system functionality considerations. We also provide guidance on how the operational design domain (ODD), driver responsibilities, and system limitations should be communicated and how these systems must be self-limited within the ODD. In addition, we discuss the benefits and limitations of training to emphasize the importance of making these systems intuitive to all users, regardless of training, to ensure proper use. These recommendations should be applied as a whole, because selectively adhering to only some may inadvertently exacerbate the dangers of driver disengagement.

Human Factors to Minimize the Human Error and Improving Patient Safety

2011 ◽  
Vol 1 (11) ◽  
pp. 82-86
Author(s):  
Sanjay Saproo ◽  
◽  
Dr. Sanjeev Bansal ◽  
Dr. Amit Kumar Pandey
Keyword(s):  
Patient Safety ◽  
Human Factors ◽  
Human Error

scholarly journals THE FUTURE OF PASSENGER AIR TRANSPORT – VERY LARGE AIRCRAFT AND OUT KEY HUMAN FACTORS AFFECTING THE OPERATION AND SAFETY OF PASSENGER AIR TRANSPORT

2017 ◽  
Vol 12 ◽  
pp. 104
Author(s):  
Petra Skolilova
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Operating Cost ◽  
Aircraft Design ◽  
Air Transport ◽  
Final Decision ◽  
Factors Affecting ◽  
Fuel Burn ◽  
The Future ◽  
The Impact

The article outlines some human factors affecting the operation and safety of passenger air transport given the massive increase in the use of the VLA. Decrease of the impact of the CO2 world emissions is one of the key goals for the new aircraft design. The main wave is going to reduce the burned fuel. Therefore, the eco-efficiency engines combined with reasonable economic operation of the aircraft are very important from an aviation perspective. The prediction for the year 2030 says that about 90% of people, which will use long-haul flights to fly between big cities. So, the A380 was designed exactly for this time period, with a focus on the right capacity, right operating cost and right fuel burn per seat. There is no aircraft today with better fuel burn combined with eco-efficiency per seat, than the A380. The very large aircrafts (VLAs) are the future of the commercial passenger aviation. Operating cost versus safety or CO2 emissions versus increasing automation inside the new generation aircraft. Almost 80% of the world aircraft accidents are caused by human error based on wrong action, reaction or final decision of pilots, the catastrophic failures of aircraft systems, or air traffic control errors are not so frequent. So, we are at the beginning of a new age in passenger aviation and the role of the human factor is more important than ever.

scholarly journals Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2135
Author(s):  
Marcin Witczak ◽  
Marcin Mrugalski ◽  
Bogdan Lipiec
Keyword(s):  
Historical Data ◽  
Field Effect Transistors ◽  
Failure Process ◽  
Practical Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Data Repository ◽  
Uncertainty Interval ◽  
Useful Life ◽  
Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

scholarly journals Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 259
Author(s):  
Ioannis Drivas ◽  
Dimitrios Kouis ◽  
Daphne Kyriaki-Manessi ◽  
Georgios Giannakopoulos
Keyword(s):  
Search Engine ◽  
Common Knowledge ◽  
Organizational Structures ◽  
Content Management ◽  
Data Driven ◽  
Management Systems ◽  
Search Engine Optimization ◽  
Optimization Methodology ◽  
Content Management Systems ◽  
The Web

While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.

The Prevention of Laparoscopic Bile Duct Injuries: An Analysis of 300 Cases of from a Human Factors and Cognitive Psychology Perspective

2007 ◽  
Vol 51 (11) ◽  
pp. 617-620 ◽  
Author(s):  
Lygia Stewart ◽  
Lawrence W. Way
Keyword(s):  
Laparoscopic Cholecystectomy ◽  
Bile Duct ◽  
Human Factors ◽  
Bile Duct Injury ◽  
Human Error ◽  
Perceptual Illusion ◽  
Bile Duct Injuries ◽  
Psychology Perspective ◽  
Cause Of Error ◽  
Key Steps

Application of human factors concepts to high-risk activities has facilitated reduction in human error. With introduction of laparoscopic cholecystectomy, the incidence of bile duct injury increased. Seeking ideas for prevention, we analyzed 300 laparoscopic bile duct injuries within the framework of human error analysis. The primary cause of error (97%) was a visual perceptual illusion. The laparoscopic environment contributed to 75% of injuries, poor visibility 22%. Most injuries involved deliberate major bile duct transection due to misperception of the anatomy. This illusion was so compelling that the surgeon usually did not recognize it. Even when irregular cues were detected, improper rules were employed, eliminating feedback. Since the complication-causing error occurred at few key steps during laparoscopic cholecystectomy; we instituted focused training to heighten vigilance, and have formulated specific rules to decrease the incidence of bile duct injury. In addition, factors in the laparoscopic environment contributing to this illusion are discussed.

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