scholarly journals Robust Learning for Adaptive Programs by Leveraging Program Structure

2010 ◽  
Author(s):  
Jervis Pinto ◽  
Alan Fern ◽  
Tim Bauer ◽  
Martin Erwig
Keyword(s):  
Program Structure ◽  
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.

MAIN RESULTS AND SCIENTIFIC OUTCOMES OF THE STUDENT SCIENTIFIC EXPEDITION TO THE MUNICIPAL AREA

2020 ◽  
Author(s):  
Марина Викторовна Курникова
Keyword(s):  
Digital Technologies ◽  
Digital Economy ◽  
Program Structure

В статье приводится программа, структура и результаты студенческой научной экспедиции в муниципальный район Безенчукский Самарской области, проведенной летом 2019 г. Целью экспедиции явилось исследование процессов формирования экосистемы цифровой экономики в сельском муниципальном районе и уровня использования имеющихся информационных и цифровых технологий его организациями и населением. The paper presents the program, structure and outcomes of the student scientific expedition to the Bezenchuksky municipal area in 2019. The expedition was aimed at the research into the development of digital economy ecosystem in the rural municipal area and the level of ICT and digital technologies used by its businesses and citizens.

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
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