Geometric computation based on an adaptive data length computation processor

2002 ◽  
Author(s):  
M. Niizeki ◽  
M. Yoshida ◽  
T. Kitamura ◽  
F. Yamaguchi
Keyword(s):  
Data Length

Proper Data Length in Thermal Properties Identification and Effect of Prescribed Parameter Error

1998 ◽  
Vol 9 (2) ◽  
pp. 53-64 ◽  
Author(s):  
Katsushi Ushioda ◽  
Noriyuki Nishida ◽  
Yoshiteru Dobashi ◽  
Kunihito Matsui
Keyword(s):  
Thermal Properties ◽  
Data Length ◽  
Parameter Error

scholarly journals Impact of Data Length on the Uncertainty of Hydrological Copula Modeling

2015 ◽  
Vol 20 (4) ◽  
pp. 05014019 ◽  
Author(s):  
X. Tong ◽  
D. Wang ◽  
V. P. Singh ◽  
J. C. Wu ◽  
X. Chen ◽  
...  
Keyword(s):  
Copula Modeling ◽  
Data Length

scholarly journals Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Da Un Jeong ◽  
Getu Tadele Taye ◽  
Han-Jeong Hwang ◽  
Ki Moo Lim
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Ventricular Fibrillation ◽  
World Health ◽  
Artificial Neural Network Classifier ◽  
Life Threatening ◽  
Causes Of Mortality ◽  
Health Organization ◽  
Forecast Time ◽  
Data Length

Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.

scholarly journals The probabilistic drought prediction using the improved surface water supply index in the Korean peninsula

2018 ◽  
Vol 50 (1) ◽  
pp. 393-415 ◽  
Author(s):  
Suk Hwan Jang ◽  
Jae-Kyoung Lee ◽  
Ji Hwan Oh ◽  
Jun Won Jo ◽  
Younghyun Cho
Keyword(s):  
Surface Water ◽  
Water Supply ◽  
Probability Distributions ◽  
Large Uncertainty ◽  
Ensemble Technique ◽  
Flood Season ◽  
Drought Prediction ◽  
Variables Selection ◽  
Data Length ◽  
Surface Water Supply

Abstract This research proposes the Korean surface water supply index (KSWSI) which overcomes some limitations of the modified SWSI (MSWSI) applied in Korea and conducts probabilistic drought prediction using KSWSI. In this research, all hydrometeorological variables were investigated and four to six appropriate variables were selected for each sub-basin and probability distributions applicable for each variable were estimated. As a result of verifying KSWSI results, the accuracy of KSWSI showed better drought phenomenon in drought events than MSWSI. Moreover, the uncertainty quantification of KSWSI calculation procedure was also carried out using the maximum entropy (ME) theory. Estimating appropriate probability distributions for each drought component in the flood season is crucial because ME values and standard deviations of KSWSI are huge, implying that large uncertainty occurs in the flood season. It is confirmed that the accuracy of KSWSI may be affected by the hydrometerological variables selection, station data obtained, used data length, and probability distributions. Furthermore, monthly probabilistic drought predictions were calculated based on the ensemble technique using KSWSI. In 2006 and 2014 drought events, the accuracy of drought predictions using KSWSI was higher than those using MSWSI, demonstrating that KSWSI is able to enhance the accuracy of drought prediction.

scholarly journals Evaluating the audio-diary method in qualitative research

2015 ◽  
Vol 15 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Iain Williamson ◽  
Dawn Leeming ◽  
Steven Lyttle ◽  
Sally Johnson
Keyword(s):  
Qualitative Research ◽  
Qualitative Content Analysis ◽  
Structured Interviews ◽  
Content Type ◽  
Critical Realist ◽  
Diary Method ◽  
Diary Methods ◽  
First Time ◽  
Practical Implications ◽  
Data Length

Purpose – Audio-diary methods are under-utilised in contemporary qualitative research. The purpose of this paper is to discuss participants and researchers’ experiences of using audio-diaries alongside semi-structured interviews to explore breastfeeding experiences in a short-term longitudinal study with 22 first-time mothers. Design/methodology/approach – The authors provide a qualitative content analysis of the participants’ feedback about their experiences of the audio-diary method and supplement this with the perspectives of the research team based on fieldwork notes, memos and team discussions. The authors pay particular attention to the ways in which the data attained from diaries compared with those from the interviews. Findings – The diaries produced were highly heterogeneous in terms of data length and quality. Participants’ experiences with the method were varied. Some found the process therapeutic and useful for reflecting upon the development of breastfeeding skills whilst negative aspects related to lack of mobility, self-consciousness and concerns about confidentiality. Researchers were positive about the audio-diary method but raised certain ethical, epistemological and methodological concerns. These include debates around the use of prompts, appropriate support for participants and the potential of the method to influence the behaviour under scrutiny. Interview and diary accounts contrasted and complemented in ways which typically enriched data analysis. Practical implications – The authors conclude that audio-diaries are a flexible and useful tool for qualitative research especially within critical realist and phenomenological paradigms. Originality/value – This appears to be the first paper to evaluate both participants and researchers’ experiences of using audio-diaries in a detailed and systematic fashion.

Cosinor Analysis for Temperature Time Series Data of Long Duration

2007 ◽  
Vol 9 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Nikhil S. Padhye ◽  
Sandra K. Hanneman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Model Fit ◽  
Series Data ◽  
Model Parameters ◽  
Cosinor Analysis ◽  
Long Time ◽  
Improved Model ◽  
Data Length ◽  
Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

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