scholarly journals Evaluating Flight Crew Performance by a Bayesian Network Model

Entropy ◽  
2018 ◽  
Vol 20 (3) ◽  
pp. 178 ◽  
Author(s):  
Wei Chen ◽  
Shuping Huang
Keyword(s):  
Bayesian Network ◽  
Delphi Technique ◽  
Human Error ◽  
Performance Model ◽  
Error Management ◽  
Flight Crew ◽  
Bayesian Network Model ◽  
Subjective Data ◽  
Aviation Accidents ◽  
Probability Prediction

Flight crew performance is of great significance in keeping flights safe and sound. When evaluating the crew performance, quantitative detailed behavior information may not be available. The present paper introduces the Bayesian Network to perform flight crew performance evaluation, which permits the utilization of multidisciplinary sources of objective and subjective information, despite sparse behavioral data. In this paper, the causal factors are selected based on the analysis of 484 aviation accidents caused by human factors. Then, a network termed Flight Crew Performance Model is constructed. The Delphi technique helps to gather subjective data as a supplement to objective data from accident reports. The conditional probabilities are elicited by the leaky noisy MAX model. Two ways of inference for the BN—probability prediction and probabilistic diagnosis are used and some interesting conclusions are drawn, which could provide data support to make interventions for human error management in aviation safety.

scholarly journals Correction: Chen, W.; Huang, S.P. Evaluating Flight Crew Performance by a Bayesian Network Model. Entropy 2018, 20, 178

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 972
Author(s):  
Wei Chen ◽  
Shuping Huang
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Research Paper ◽  
Flight Crew ◽  
Bayesian Network Model

After publication of the research paper [...]

scholarly journals Desain Konseptual Speech Recognition di Komunikasi Pesawat untuk Mengurangi Kesalahan Komunikasi Penerbangan

WARTA ARDHIA ◽  
2017 ◽  
Vol 42 (3) ◽  
pp. 109
Author(s):  
Nur Oktaviani Widiastuti
Keyword(s):  
Decision Making ◽  
Speech Recognition ◽  
Literature Review ◽  
System Design ◽  
Conceptual Design ◽  
Human Error ◽  
Flight Crew ◽  
Aviation Accidents ◽  
The World ◽  
Checking Procedure

Penyebab utama kecelakaan penerbangan adalah human error (55%) dengan salah satu penyebab adalah miskomunikasi. Miskomunikasi menjadi penyebab kasus kecelakaan terbesar di dunia seperti kecelakaan antara Pan Am dan KLM, Garuda dengan nomor penerbangan 152, dan kecelakaan yang baru-baru ini terjadi antara Batik Air dan Transnusa. Selain itu, KNKT pernah mengalami kebingungan saat menginvestigasi cockpit recorder Air Asia QZ8501. Saat miskomunikasi terjadi, instruksi sering kali sulit dipahami dan diperlukan pengulangan komunikasi, yang mempersempit waktu pengambilan tindakan. Penelitian ini ingin mengembangkan konsep speech recognition dengan sistem voice sign and text untuk prosedur pengecekan komunikasi kru penerbangan. Metode dalam penelitian ini adalah kajian pustaka dan perancangan sistem. Hasil dari penelitian ini adalah penambahan prosedur komunikasi dengan sign dan teks yang diperoleh dengan bantuan speech recognition. Pesan teks berasal dari ucapan yang diterjemahkan oleh speech recognition menjadi teks, kemudian teks akan diubah menjadi sign. Lalu sign dan teks akan ditampilkan dan dilihat langsung oleh pilot. Dengan penambahan sistem sign dan teks disamping komunikasi melalui suara diharapkan kesalahan dapat diminimalisir, pengambilan keputusan dapat dilakukan dengan cepat, dan kecelakaan dapat dihindari. [Speech Recognition Conceptual Design in Aircraft Communications to Reduce Flight Communication Mistake] The main cause of aviation accidents is the human error (55%) in which one of its factor is the miscommunication. Miscommunication involved in the most accidents in the world including Pan Am and KLM accident, Garuda flight 152 (the biggest accident in Indonesia), and that was recently occurred between Batik Air and Transnusa Airline. Moreover, NTSC was confused when investigating the Air Asia QZ8501 cockpit recorder. When miscommunication occurs, the repetition of communication is prominently required due to the difficulty in understanding the instruction, which later narrowed the time to take any action. This study is intended to develop the concept of speech recognition with voice sign and text system for flight crew communication checking procedure. The methodology that is used in this research is the combining of literature review and system design. The results of this study is the addition of communication procedure by means of sign and text that obtained from speech recognition process. The text is produced from the translation of voice by speech recognition and converted into sign afterwards. Both of sign and text will be displayed, thus can be seen by the pilot. In addition to communication by voice, the implementation of sign and text is expected in minimalizing the error, supporting faster decision making, and avoiding the accident.

scholarly journals A Unified View of Causal and Non-causal Feature Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Feature Selection ◽  
Bayesian Network ◽  
Synthetic Data ◽  
Selection Methods ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Feature Sets ◽  
Unified View ◽  
Optimal Feature ◽  
Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

scholarly journals Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model

2021 ◽  
pp. 125075
Author(s):  
Javad Roostaei ◽  
Sarah Colley ◽  
Riley Mulhern ◽  
Andrew A. May ◽  
Jacqueline MacDonald Gibson
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Well Water ◽  
Bayesian Network Model

scholarly journals Impact Mechanism of the Ecological Vulnerability of Highly Developed Islands Based on the Bayesian Network Model—Applied to the Changshan Islands

2021 ◽  
Vol 18 (8) ◽  
pp. 4150
Author(s):  
Keyu Qin ◽  
Haijun Huang ◽  
Jingya Liu ◽  
Liwen Yan ◽  
Yanxia Liu ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Geographical Information ◽  
Global Changes ◽  
Bayesian Network Model ◽  
Island Ecosystems ◽  
Impact Mechanism ◽  
The North ◽  
Ecological Vulnerability ◽  
The Impact

Islands are one of the most sensitive interfaces between global changes and land and sea dynamic effects, with high sensitivity and low stability. Therefore, under the dynamic coupling effect of human activities and frequent natural disasters, the vulnerability of the ecological environment of islands shows the characteristics of complexity and diversity. For the protection of island ecosystems, a system for the assessment of island ecosystems and studies on the mechanism of island ecological vulnerability are highly crucial. In this study, the North and South Changshan Islands of China were selected as the study area. Considering various impact factors of island ecological vulnerability, the geographical information systems (GIS) spatial analysis, field surveys, data sampling were used to evaluate island ecological vulnerability. The Bayesian network model was used to explore the impact mechanism of ecological vulnerability. The results showed that the ecological vulnerability of the North Changshan Island is higher than that of the South Changshan Island. Among all the indicators, the proportion of net primary productivity (NPP) and the steep slope has the strongest correlation with ecological vulnerability. This study can be used as references in the relevant departments to formulate management policies and promote the sustainable development of islands and their surrounding waters

scholarly journals Misper-Bayes: A Bayesian Network Model for Missing Person Investigations

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Denis Reilly ◽  
Mark Taylor ◽  
Paul Fergus ◽  
Carl Chalmers ◽  
Steven Thompson
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Bayesian Network Model

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Research on Combat Effectiveness Evaluation of Radar EW System Based on Bayesian Network

2011 ◽  
Vol 204-210 ◽  
pp. 1697-1700 ◽  
Author(s):  
Yu Jie Zheng
Keyword(s):  
Bayesian Network ◽  
Evaluation Model ◽  
Effectiveness Evaluation ◽  
Analysis Model ◽  
Key Factors ◽  
Bayesian Network Model ◽  
Simulation Data ◽  
Factors Analysis ◽  
Combat Effectiveness ◽  
Hidden Knowledge

Radar EW system combat effectiveness evaluation is a essential link to Radar system Demonstration, mainly give service to selection, optimization and key factors analysis of Weapon equipment scheme. In this paper, we introduce the Bayesian network model into the area of Radar EW system combat effectiveness evaluation and put forward the concept of combat effectiveness evaluation model based on Bayesian network. The ability to express complex relationship, the ability to express the uncertainty of probability, and the reasoning functions. By learning from Expertise and Simulation data, excavating the hidden knowledge included in both of them, we can build the combat efficiency Analysis model, and then carry out efficient analysis.

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