The Study on Bayesian Network-Based IETM Information Retrieval Technology

2013 ◽  
Vol 846-847 ◽  
pp. 1180-1184
Author(s):  
Jia Ju Wu ◽  
Ze You Pan ◽  
Yong Kang Xu ◽  
Rong Yu
Keyword(s):  
Information Retrieval ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Network Model ◽  
Probability Learning ◽  
Model Structure ◽  
Retrieval Process ◽  
Inference Process ◽  
Bayesian Network Model ◽  
Key Technologies

IETM has been an important role in equipment integrated support. IETM works mainly through the guidance of the equipment maintenance support information retrieval. So, IETM information retrieval technology is one of the key technologies in the application of integrated equipment support. This paper proposes a new method of IETM information query based on Bayesian network model. The model supports for the IETM information of structured query. IETM information query is based features, the use the IETM dataset words, the elements and structure of unit stats information on the topology of the model structure and the conditional probability learning. Combination of the probability functions of the method and using the model of the probabilistic inference process, the relevance structured IETM information query conditions were estimated. Finally, the information retrieval process is analyzed.

scholarly journals Penerapan Metode Bayesian Network Model Untuk Menghitung Probabilitas Penyakit Sesak Nafas Bayi

2018 ◽  
Vol 2 (1) ◽  
pp. 62
Author(s):  
Hasniati Hasniati ◽  
Arianti Arianti ◽  
William Philip
Keyword(s):  
Probability Distribution ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Network Model ◽  
Posterior Probability ◽  
Prior Probability ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Bayesian Network Model

Bayesian Network dapat digunakan untuk menghitung probabilitas dari kehadiran berbagai gejala penyakit. Dalam tulisan ini, penulis menerapkan bayesian network model untuk menghitung probabilitas penyakit sesak nafas pada bayi. Bayesian network diterapkan berdasar pada data yang diperoleh melalui wawancara kepada dokter spesialis anak yaitu data nama penyakit, penyebab, dan gejala penyakit sesak nafas pada bayi. Struktur Bayesian Network penyakit sesak nafas bayi dibuat berdasarkan ada tidaknya keterkaitan antara gejala terhadap penyakit sesak nafas. Untuk setiap gejala yang direpresentasikan pada struktur bayesian network mempunyai estimasi parameter yang didapat dari data yang telah ada atau pengetahuan dari dokter spesialis. Data estimasi ini disebut nilai prior probaility atau nilai kepercayaan dari gejala penyakit sesak nafas bayi. Setelah diketahui prior probability, langkah berikutnya adalah menentukan Conditional probability (peluang bersyarat) antara jenis penyakit sesak nafas dengan masing-masing gejalanya. Pada langkah akhir, nilai posterior probability dihitung dengan mengambil nilai hasil joint probability distribution (JPD) yang telah diperoleh, kemudian nilai inilah yang digunakan untuk menghitung probabilitas kemunculan suatu gejala. Dengan mengambil satu contoh kasus bahwa bayi memiliki gejala sesak, lemah, gelisah dan demam, disimpulkan bahwa bayi menderita penyakit sesak nafas Pneumoni Neonatal sebesar 0,1688812743.

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.

Corrosion threat assessment of crude oil flow lines using Bayesian network model

2015 ◽  
Vol 50 (3) ◽  
pp. 236-247 ◽  
Author(s):  
G. Koch ◽  
F. Ayello ◽  
V. Khare ◽  
N. Sridhar ◽  
A. Moosavi
Keyword(s):  
Bayesian Network ◽  
Crude Oil ◽  
Network Model ◽  
Threat Assessment ◽  
Bayesian Network Model ◽  
Flow Lines ◽  
Oil Flow
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