Robust Learning-Based Annotation of Medical Radiographs

2010 ◽  
pp. 77-88
Author(s):  
Yimo Tao ◽  
Zhigang Peng ◽  
Bing Jian ◽  
Jianhua Xuan ◽  
Arun Krishnan ◽  
...  
Keyword(s):  
Robust Learning

The Relationship between a Business Simulator, Constructivist Practices, and Motivation toward Developing Business Intelligence Skills

10.28945/3602 ◽  
2016 ◽  
Vol 15 ◽  
pp. 593-609
Author(s):  
Hsun-Ming Lee ◽  
Ju Long ◽  
Lucian Visinescu
Keyword(s):  
Active Learning ◽  
Business Intelligence ◽  
Meaningful Learning ◽  
Pedagogical Practices ◽  
Business Simulation ◽  
Robust Learning ◽  
Perceived Difficulty ◽  
Simulation Based ◽  
Based Instruction ◽  
Constructivist Practices

Developing Business Intelligence (BI) has been a top priority for enterprise executives in recent years. To meet these demands, universities need to prepare students to work with BI in enterprise settings. In this study, we considered a business simulator that offers students opportunities to apply BI and make top-management decisions in a system used by real-world professionals. The simulation-based instruction can be effective only if students are not discouraged by the difficulty of using the BI computer system and comprehending the complex BI subjects. Constructivist practices embedded in the business simulation are investigated to understand their potentials for helping the students to overcome the perceived difficulty. Consequently, it would enable instructors to more efficiently use the simulator by providing insights on its pedagogical practices. Our findings showed that the constructivist practices such as collaboration and subject integration positively influence active learning and meaningful learning respectively. In turn, both active learning and meaningful learning positively influence business intelligence motivational behavior. These findings can be further used to develop a robust learning environment in BI classes.

Leak detection and localization for pipelines using multivariable fuzzy learning backstepping

2021 ◽  
pp. 1-12
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Transmission Lines ◽  
Leak Detection ◽  
Support Vector ◽  
Robust Learning ◽  
Regression Techniques ◽  
Space Formulation ◽  
Points Of View ◽  
Detection And Localization ◽  
Fuzzy Learning ◽  
Backstepping Algorithm

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups.

CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning

2020 ◽  
Author(s):  
Zeren Sun ◽  
Xian-Sheng Hua ◽  
Yazhou Yao ◽  
Xiu-Shen Wei ◽  
Guosheng Hu ◽  
...  
Keyword(s):  
Noisy Data ◽  
Robust Learning

scholarly journals CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS

2002 ◽  
Vol 16 (03) ◽  
pp. 361-374 ◽  
Author(s):  
JUAN ANDRADE-CETTO ◽  
ALBERTO SANFELIU
Keyword(s):  
Learning Rule ◽  
Dynamic Environments ◽  
Position Estimation ◽  
Indoor Environments ◽  
Map Building ◽  
Robust Learning ◽  
Map Construction ◽  
Map Updating ◽  
Dynamic Map ◽  
Robot Position

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.

Creating and Implementing “Robust” Learning Tasks

2001 ◽  
Vol 32 (4) ◽  
pp. 248-251
Author(s):  
John Rohwer ◽  
Robert Wandberg
Keyword(s):  
Robust Learning ◽  
Learning Tasks
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