scholarly journals Analisis kemampuan VO2max cabang olahraga beladiri

2021 ◽  
Vol 10 (1) ◽  
pp. 78-88
Author(s):  
Y. Touvan Juni Samodra ◽  
Mashud Mashud
Keyword(s):  
Test Data ◽  
Multi Stage ◽  
Non Parametric

Pondasi untuk melakukan aktivitas dalam waktu yang lama, salah satunya adalah kemampuan VO2max. Kemampuan VO2max tidak secara mudah dan singkat dimiliki oleh setiap atlet, perlu latihan yang lama dengan dosis yang tepat. Cabang olahraga beladiri  dalam pertandingan memerlukan intensitas yang tinggi dalam waktu yang relative singkat. Berdasarkan hal ini maka kemampuan VO2max atlet seharusnya tinggi. Tujuan penelitian ini  adalah untuk melihat kemampuan VO2max empat cabang olahraga (cabor) beladiri (judo, karate, taekwondo dan kempo) yang mengikuti tes seleksi untuk kepentingan pemusatan latihan. Sampel adalah atlet keempat cabor tersebut yang berjumlah 45 atlet yang terdiri dari, judo (11), karate (16), taekwondo (10) dan Kempo (8). Instrumen tes yang dipergunakan adalah beep multi stage test. Data dianalisis dengan statistik deskriptif dan uji non parametric. Hasil penelitian menggambarkan bahwa rerata VO2max atlet beladiri adalah 34.75. Nilai VO2max ini bukanlah nilai yang tinggi untuk atlet. Dibuktikan lebih lanjut di antara keempat cabor beladiri ini dengan uji beda non parametric ternyata bedasaran uji kruskal wallis test hasilnya ditemukan signifikansi 0.119. Hasil ini mengindikasikan bahwa tidak terdapat perbedaan VO2max di antara keempat cabor tersebut. Kesimpulan dalam penelitian ini kemampuan VO2max cabang olahraga beladiri masih dalam kategori rendah.

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

scholarly journals TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach

2018 ◽  
Vol 10 (11) ◽  
pp. 1822 ◽  
Author(s):  
Gustavo Martín del Campo ◽  
Yuriy Shkvarko ◽  
Andreas Reigber ◽  
Matteo Nannini
Keyword(s):  
Structural Model ◽  
Imaging Techniques ◽  
Resolution Enhancement ◽  
Adaptive Beamforming ◽  
Irregular Sampling ◽  
Multi Stage ◽  
Forested Areas ◽  
German Aerospace ◽  
Design Regularization ◽  
Non Parametric

Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods.

scholarly journals An Investigation of the Flow Distribution Inside a Radial Sidestream Inlet of a Centrifugal Compressor

2000 ◽  
Author(s):  
Zheji Liu ◽  
D. Lee Hill ◽  
Gary Colby
Keyword(s):  
Test Data ◽  
Centrifugal Compressor ◽  
Flow Distribution ◽  
Production Test ◽  
Cfd Model ◽  
Cfd Models ◽  
Multi Stage ◽  
Final Design ◽  
Computational Fluid Dynamics Cfd ◽  
Return Channel

A radial sidestream inlet is commonly utilized in multi-stage centrifugal compressors to introduce additional gas into the mid-stage of the compressor. The flow distribution after the junction of the sidestream and the main return channel of the upstream stage can significantly affect the performance of the next stage. In this study, the mixing between the fluid from the sidestream component and the fluid from the main return channel was investigated numerically using Computational Fluid Dynamics (CFD). A variety of CFD models of different geometry, different boundary conditions, and different grid density were developed to analyze the uniformity of the flow entering the impeller of the next stage. The flow distribution difference between the sidestream CFD model and the CFD model with the sidestream coupled to the main return channel suggests that both the return channel and the sidestream have to be modeled together to get meaningful results. The results of this effort were used in conjunction with production test data to help resolve a performance shortfall of a multi-stage centrifugal compressor with sidestream injection. The test data from the final design is also provided to show the resulting improvement in head rise.

Statistical Evaluation on Cumulative Damage Property of Aluminum Alloy Welded Joint under Multi-Stage Loading

2010 ◽  
Vol 118-120 ◽  
pp. 522-526
Author(s):  
Wei Li ◽  
Qiang Li ◽  
Ping Wang
Keyword(s):  
Aluminum Alloy ◽  
Test Data ◽  
Goodness Of Fit ◽  
Damage Model ◽  
Butt Joint ◽  
Load Level ◽  
Cumulative Damage ◽  
Goodness Of Fit Test ◽  
Multi Stage ◽  
Cumulative Damage Model

Two-stage loading tests corresponding to high-low loading and low-high loading were performed to clarify the cumulative damage properties of two kinds of aluminum alloy welded joints such as the corner joint and butt joint. As a result, the logarithmic value of cumulative damage can better be characterized by the normal distribution in accordance with the Komogorov-Smirnov goodness of fit test. The proposed linear cumulative damage model can better reflect the scatter characteristic of test data, as well as the newly developed nonlinear cumulative damage model can better characterize the effects of load sequence and load level on fatigue damage value, which was verified with the obtained test data.

Understanding Stage Matching of a High-Efficiency Multi-Stage Centrifugal Gas Compressor

2000 ◽  
Author(s):  
Michael J. Cave ◽  
Tim David
Keyword(s):  
Test Data ◽  
Closed Loop ◽  
High Efficiency ◽  
Analysis Software ◽  
Multi Stage ◽  
Software Program ◽  
Stage Matching ◽  
D Model

Abstract A series of closed loop development tests were conducted on a new high-efficiency, multi-stage gas compressor. Using the test data and a 1-D compressor analysis software program, stage characteristics were modeled for each of the six stages. Overall stage results from the 1-D model were compared to development test data to validate the model. Once the model was validated, it was used to understand stage-to-stage matching at design and off-design conditions.

scholarly journals Non-Parametric Mixture Modelling and its Application to Disease Progression Modelling

2018 ◽  
Author(s):  
Nicholas C. Firth ◽  
Neil P. Oxtoby ◽  
Silvia Primativo ◽  
Emilie Brotherhood ◽  
Alexandra L. Young ◽  
...  
Keyword(s):  
Disease Progression ◽  
Test Data ◽  
Cognitive Test ◽  
Cognitive Changes ◽  
Cross Sectional ◽  
Mixture Modelling ◽  
Changes Over Time ◽  
Over Time ◽  
Cognitive Data ◽  
Non Parametric

AbstractDementia is characterised by its progressive degeneration of cognitive abilities. In research cohorts, detailed neuropsychological test batteries are often administered to better understand how cognition changes over time. Understanding cognitive changes in dementia is of great importance, particularly in determining how structural changes in the brain may affect cognition and in facilitating earlier detection of symptomatic changes. Disease progression models are often applied to these data to understand how a disease changes over time from cross-sectional data or to disease trajectories from large numbers of individuals. Previous disease progression models used to build longitudinal models from cross-sectional data have focused on brain imaging data; however, these models are not directly applicable to cognitive data. Here we use the novel, non-parametric, Kernel Density Estimation Mixture Modelling (KDEMM) approach and demonstrate accurate modelling of the progression of cognitive test data. We found that using KDEMM resulted in more accurate models of disease progression in simulated data compared to Gaussian Mixture Models (GMMs) for the majority of parameters used to simulate the data. When comparing KDEMM and GMM to cognitive data collected in different Alzheimers Disease subtypes, we found the KDEMM resulted in a model much more in line with clinical phenotype. We anticipate that the KDEMM will be used to integrate cognitive test data, and other non-normally distributed datasets into complex disease progression models.

Statistical Failure Analysis for Non-Parametric Characteristic in DRAM

2019 ◽  
Author(s):  
Myunghoon Oak ◽  
Jongbum Lee ◽  
Jungwon Bae ◽  
Kyungrak-Cho ◽  
Minju-Shin ◽  
...  
Keyword(s):  
Test Data ◽  
Test Method ◽  
Test Statistic ◽  
Final Test ◽  
Test Items ◽  
Root Cause ◽  
Yield And Quality ◽  
Highly Correlated ◽  
Test Failure ◽  
Non Parametric

Abstract Rapid and accurate root cause analysis of the defect contributes to improvement in yield and quality in semiconductor manufacturing system. In particular, imperfection of final test can cause major problems for customers, so analysis on root cause of final test failure is important activity for high quality. It can be started with finding first test data which is highly correlated with final test failure. However, it is difficult to analyze the correlation of first test data and final test failures because the first test is made up of hundreds of test items, and the data also show non-parametric characteristics with extreme outlier. In this study, Kolmogorov-Smirnov test (K-S test), which is a non-parametric test method, is statistically applied to the first test data. The K-S test is intuitive and descriptive, which makes it easy to analyze the root cause. And K-S test showed a performance improvement compared to t-test statistic, which requires a normal distribution assumption. Therefore, our data mining approach can help analysis to improve yield and quality of mass production with highly scaled devices.

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