Designing the Moving Pattern of Cleaning Robot based on Grammatical Evolution with Conditional Probability Table

2016 ◽  
Vol 22 (4) ◽  
pp. 184-188
Author(s):  
Soon-Joe Gwon ◽  
Hyun-Tae Kim ◽  
Chang Wook Ahn
Keyword(s):  
Conditional Probability ◽  
Grammatical Evolution ◽  
Cleaning Robot ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Moving Pattern

Research on Power System Fault Diagnosis Based on Bayesian Networks

2013 ◽  
Vol 347-350 ◽  
pp. 1930-1934 ◽  
Author(s):  
Guo Feng Yang ◽  
Qing Ming Xiao ◽  
Hong Ouyang ◽  
Jia Kui Zhao ◽  
Ting Shun Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Structure Learning ◽  
Parameter Learning ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Diagnosis Model ◽  
Diagnosis Approach

Aiming at the incompleteness and uncertainty of information existing in power system fault diagnosis, a new fault diagnosis approach based on Bayesian network is proposed in this paper. Through the Bayesian network of structure learning and parameter learning, a power system fault diagnosis model based on Bayesian network has been proposed. Conditional probability table describes the connection degree between various factors in quantity. Diagnostic results of instance proved the effectiveness and superiority of the proposed method.

scholarly journals Information Value-Based Fault Diagnosis of Train Door System under Multiple Operating Conditions

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3952
Author(s):  
Seokgoo Kim ◽  
Nam Ho Kim ◽  
Joo-Ho Choi
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Fault Isolation ◽  
Information Value ◽  
Operating Conditions ◽  
Data Driven ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Operating Regimes

While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional probability table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.

scholarly journals Probabilistic Model-Based Malaria Disease Recognition System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rahila Parveen ◽  
Wei Song ◽  
Baozhi Qiu ◽  
Mairaj Nabi Bhatti ◽  
Tallal Hassan ◽  
...  
Keyword(s):  
Probabilistic Models ◽  
Early Stage ◽  
Recognition System ◽  
Environmental Data ◽  
Influence Diagram ◽  
Conditional Probability Table ◽  
Probability Table ◽  
The Given ◽  
Very High ◽  
Huge Variation

In this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms and from the environmental data. In this paper, we proposed a Bayesian network (BN) model to predict the occurrences of malaria disease. The proposed BN model is built on different attributes of the patient’s symptoms and environmental data which are divided into training and testing parts. Our proposed BN model when evaluated on the collected dataset found promising results with an accuracy of 81%. One the other hand, F1 score is also a good evaluation of these probabilistic models because there is a huge variation in class data. The complexity of these models is very high due to the increase of parent nodes in the given influence diagram, and the conditional probability table (CPT) also becomes more complex.

scholarly journals Multiclass obstacles detection and classification using stereovision and Bayesian network for intelligent vehicles

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094727
Author(s):  
Lina Yang ◽  
Yingping Huang ◽  
Xing Hu ◽  
Hongjian Wei ◽  
Qixiang Wang
Keyword(s):  
Bayesian Network ◽  
Conditional Probability ◽  
Domain Knowledge ◽  
Three Dimensional ◽  
Intelligent Vehicles ◽  
Middle Level ◽  
Conditional Probability Distribution ◽  
Data Set ◽  
Probability Table ◽  
Path Condition

Intelligent vehicles should be able to detect various obstacles and also identify their types so that the vehicles can take an appropriate level of protection and intervention. This article presents a method of detecting and classifying multiclass obstacles for intelligent vehicles. A stereovision-based method is used to segment obstacles from traffic background and measure three-dimensional geometrical features. A Bayesian network (BN) model has been established to further classify them into five classes, including pedestrian, cyclist, car, van, and truck. The BN model is trained using substantial data samples. The optimized structure of the model is determined from the necessary path condition method with a presupposition constraint (NPC+PC). The conditional probability table of the discrete nodes and the conditional probability distribution of the continuous nodes are determined from expectation maximization (EM) training algorithm with consideration of prior domain knowledge. Experiments were conducted using the object detection data set on the public KITTI benchmark, and the results show that the proposed BN model exhibits an excellent performance for obstacle classification while the full pipeline of the method including detection and classification is in the upper middle level compared with other existing methods.

Facial Likability and Smiling Enhance Cooperation, but Have No Direct Effect on Moralistic Punishment

2016 ◽  
Vol 63 (5) ◽  
pp. 263-277 ◽  
Author(s):  
Laura Mieth ◽  
Raoul Bell ◽  
Axel Buchner
Keyword(s):  
Conditional Probability ◽  
Direct Effect ◽  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Prisoner’S Dilemma Game ◽  
Costly Punishment ◽  
Prisoner's Dilemma Game ◽  
Moralistic Punishment

Abstract. The present study serves to test how positive and negative appearance-based expectations affect cooperation and punishment. Participants played a prisoner’s dilemma game with partners who either cooperated or defected. Then they were given a costly punishment option: They could spend money to decrease the payoffs of their partners. Aggregated over trials, participants spent more money for punishing the defection of likable-looking and smiling partners compared to punishing the defection of unlikable-looking and nonsmiling partners, but only because participants were more likely to cooperate with likable-looking and smiling partners, which provided the participants with more opportunities for moralistic punishment. When expressed as a conditional probability, moralistic punishment did not differ as a function of the partners’ facial likability. Smiling had no effect on the probability of moralistic punishment, but punishment was milder for smiling in comparison to nonsmiling partners.

Interlocking patterns of conditional probability: The reinforcement matrix.

2002 ◽  
Vol 3 (1) ◽  
pp. 30-40
Author(s):  
Joseph D. Cautilli ◽  
Donald A. Hantula
Keyword(s):  
Conditional Probability
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