Open architecture dynamic data system at Langley's Transonic Dynamics Tunnel

1998 ◽  
Author(s):  
Charles Bryant
Keyword(s):  
Data System ◽  
Open Architecture ◽  
Dynamic Data

Cutting Dynamics Identification by Dynamic Data System (DDS) Modeling Approach

1985 ◽  
Vol 107 (2) ◽  
pp. 91-94 ◽  
Author(s):  
T. Y. Ahn ◽  
K. F. Eman ◽  
S. M. Wu
Keyword(s):  
Transfer Functions ◽  
Moving Average ◽  
Analytical Form ◽  
Data System ◽  
Cutting Process ◽  
Autoregressive Moving Average ◽  
Multivariate System ◽  
Dynamic Data ◽  
Dynamic Cutting Force ◽  
Cutting Processes

The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.

Communication: Dynamic Data System Modeling—Revisited

1978 ◽  
Vol 24 (11) ◽  
pp. 1200-1202 ◽  
Author(s):  
H. J. Steudel ◽  
S. M. Wu
Keyword(s):  
System Modeling ◽  
Data System ◽  
Dynamic Data

scholarly journals Blood Pressure Variability and Complexity in Nursing Home Residents Before Death

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 138-138
Author(s):  
Laura A Graham ◽  
Sei Lee ◽  
Michael Steinman ◽  
Carmen Peralta ◽  
Anna Rubinsky ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Nursing Home ◽  
Healthy Aging ◽  
Clinical Decision Making ◽  
Linear Models ◽  
Nursing Home Residents ◽  
Data System ◽  
Veterans Health ◽  
Dynamic Data ◽  
Factors Associated

Abstract Blood pressure (BP) is a complex dynamic system in the human body and an important determinant of healthy aging. Exploring BP as a dynamic data system may provide important insights into how BP patterns can provide complementary information to the static, one-time BP measurements that are more commonly used for clinical decision making. Thus, we sought to describe BP as a dynamic data system in older adults nearing death. Using a prospective cohort study design, we assessed BP measures 6 months before death in Veterans Health Administrative nursing home residents between 10/1/2006 and 9/30/2017. Variability was characterized using standard deviation and mean square error after adjusting for diurnal variations. Complexity (i.e., amount of novel information vs. redundancy) was examined using Shannon’s entropy (bits). Generalized linear models were used to examine factors associated with overall BP variability. We identified 17,953 patients (98.0% male, 82.5% White, mean age 80.2 years, and mean BP 125.7/68.6 mmHg). In the last 6 months of life, systolic BP decreased slightly (⃞-7.2mmHg). Variability was stable until the last month of life, at which point variability increased by as much as 30%. In contrast, complexity did not change in the 6 months before death (⃞0.02 bits). Factors associated with BP variability before death include hospitalizations, hospice care, and medication changes. Systolic BP decreases in the last 6 months before death, and BP variability increases in the last month of life. Further, the increase in BP variability may be driven by increasingly complex care patterns as one approaches death.

Signature Analysis for Mechanical Systems via Dynamic Data System (DDS) Monitoring Technique

1980 ◽  
Vol 102 (2) ◽  
pp. 217-221 ◽  
Author(s):  
S. M. Wu ◽  
T. H. Tobin ◽  
M. C. Chow
Keyword(s):  
Quality Control ◽  
Electric Motor ◽  
Control Variable ◽  
Data System ◽  
Statistical Quality Control ◽  
Normal Operation ◽  
Signature Analysis ◽  
Dynamic Data ◽  
Monitoring Technique ◽  
On Line

A new modeling technique called Dynamic Data System (DDS) is introduced for signature analysis. A one-horsepower electric motor experiment is used to demonstrate the methodology. The ‘normal’ operation is simulated by idling runs and eccentric loads representing the ‘defective’ operations. A DDS Monitoring Technique, by employing statistical quality control theory, is developed to monitor the operations of imposed electric motor defects. It takes thirteen seconds for data processing and calculation of a control variable value from an On-line programmed microprocessor to provide the control justification. The DDS Monitoring Technique proves to be effective and sensitive in identifying the operational status of a mechanical system.

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