Flight Testing, Data Collection, and System Identification of a Multicopter UAV

2017 ◽  
Author(s):  
Subodh Bhandari ◽  
Paul Navarro ◽  
Alex Ruiz
Keyword(s):  
System Identification ◽  
Data Collection ◽  
Flight Testing ◽  
Testing Data

Deadbands Tell No Tails: X-56A Dynamic Actuation Requirements

2020 ◽  
Author(s):  
Alexander M. Pankonien ◽  
Peter M. Suh ◽  
Jacob R. Schaefer ◽  
Robert M. Mitchell
Keyword(s):  
State Of The Art ◽  
Ride Quality ◽  
Flight Testing ◽  
Nonlinear Simulation ◽  
The Past ◽  
Actuation System ◽  
Testing Data ◽  
Aircraft Performance ◽  
Number Of Cycles ◽  
Significant Effort

Abstract Following significant effort over the past several years by AFRL and NASA, the X-56A flight vehicle has proven to be a useful platform for exploring controllers and distributed actuation on a flexible, swept flying-wing. The program sought to advance the state of the art in airworthiness for vehicles encountering flutter, leading to relaxed design constraints that could drastically decrease structural weight and improve aircraft performance. Specifically, the vehicle was designed to encounter different forms of flutter: body-freedom flutter, and wing-bending torsion flutter, making it an ideal candidate for identifying dynamic actuation challenges. Flight testing led to fundamental observations by controller designers about the actuation needs for such a vehicle. Namely, the small inherent actuator deadband led to significant constant-amplitude limit cycle oscillations of the system during post-flutter controlled flight. This work captures these observations by exploring theoretical changes in the actuators via a nonlinear simulation tuned with flight testing data and shows that a 60% reduction in actuator deadband can improve ride quality by nearly 50%. The results are combined into a set of actuation challenges for the adaptive structures community at large, including precise actuation for a large number of cycles over multiple timescales, with a relevant baseline described by original actuation system.

An Empirical Test of the Howard-Sheth Model of Buyer Behavior

1970 ◽  
Vol 7 (4) ◽  
pp. 427-438 ◽  
Author(s):  
John U. Farley ◽  
L. Winston Ring
Keyword(s):  
Data Collection ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Empirical Test ◽  
Structural Parameters ◽  
Behavior Model ◽  
Extreme Pressure ◽  
Buyer Behavior ◽  
Empirical Potential ◽  
Testing Data

The Howard-Sheth buyer behavior model was cast in the form of a multiple-equation regression model for testing data on a grocery product in a specific market. Estimated structural parameters were generally consistent with the model's predictions, but some goodness-of-fit measures were weak. The model was useful for organizing this analysis of consumer behavior, but the test put extreme pressure on the data. Considerably improved data collection techniques and procedures will be needed before the full empirical potential of such models will be realized.

scholarly journals Pengaruh Penggunaan Smartphone Terhadap Motivasi Belajar Siswa Sekolah Dasar di Kecamatan Mapanget, Kota Manado

2019 ◽  
Vol 3 (1) ◽  
pp. 52
Author(s):  
Kosmas Sobon
Keyword(s):  
Elementary School ◽  
Elementary Schools ◽  
Data Collection ◽  
Elementary School Students ◽  
Quantitative Study ◽  
Learning Motivation ◽  
School Students ◽  
Validity Testing ◽  
Research Results ◽  
Testing Data

The aim of this research is to explain influence smartphone on learning motivation of elementary school students Mapanget Subdistrict Manado City. This research is in four elementary schools used sample are 84 students. This research is a quantitative study used the survey’s method with the following stages: observation of the location of the research, preparation of questionnaires, distribution of questionnaires, validity testing, data collection, processing of research results, analysis of the results of research, and discussion. The result showed that the effect of smartphone on the learning motivation of students in elementary school Mapanget Subdistrict was 0.057 or 5.7% with a significance  0.028 < 0,05. The results of testing t-table is greater than t count, namely t-hitung = 2.232 ≥ (t-tab) = 1.989. This means that smartphone have an effect on students’ learning motivation even though it is in the low category 5.7%. It is recomended that students do not use excessive smartphones both at school and at home. Parents and teachers need to supervise children in using smartphone everday  because the effect  of smartphone on students’ learning motivation is very low.

scholarly journals Peningkatan Dalam Menyusun Rencana Pelaksanaan Melalui Supervisi Klinis Pada Kepala SD Daerah Binaan II UPTD Pendidikan Kecamatan Kalikotes Kabupaten Klaten Semester Gasal Tahun Pelajaran 2014/2015

2015 ◽  
Vol 26 (2) ◽  
Author(s):  
Sri Sumarsih
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Primary School ◽  
Clinical Supervision ◽  
School Principals ◽  
Lesson Plan ◽  
Research Data ◽  
Testing Data ◽  
Indicators Of Success ◽  
Academic Year

The aim of this study was to describe the increase in the principal’s efforts in preparing the implementation plan through clinical supervision at State Primary School Pa tronage II Regional District of Klaten Kalikotes odd semester of 2014/2015 academic year. Subject and source of research data were five school principals. Methods of data collection were observation, documentation, and testing. Data analysis used a critical and compara- tive analysis. Indicators of success used the minimum 76 and a target of 100% complete- ness. Results of research and discussion suggest that the observation of the principal in the supervision of lesson preparation plan in detail can be described from previsus cycle  to cycle I, there was an increase of 60%, from cycle to cycle II, there is an increase of 80%, and from the first cycle to the second cycle previsus there is an increase of 20%, it can be concluded that the start of the second cycle to previsus cycle is significantly, and principals in the preparation of the lesson plan in detail can be described from previsus cycle to the first cycle, an increase of 60%, from previsus cycle to second cycle, an increase of 100%, and from the first cycle to the second cycle there is an increase of 40%, it can be concluded that the start of the second cycle to before cycle is significantly.

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