Development of surface texture evaluation system for highly sparse data-driven machining domains

2020 ◽  
Vol 33 (9) ◽  
pp. 859-868
Author(s):  
Umamaheswara Raju R. S. ◽  
R. Ramesh ◽  
K. Rohit Varma
Keyword(s):  
Surface Texture ◽  
Evaluation System ◽  
Sparse Data ◽  
Data Driven

scholarly journals The 2011–2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance

2021 ◽  
Vol 11 (5) ◽  
pp. 2249
Author(s):  
Hyunah Shin ◽  
Jaehun Cha ◽  
Chungchun Lee ◽  
Hyejin Song ◽  
Hyuntae Jeong ◽  
...  
Keyword(s):  
Medical Informatics ◽  
Drug Safety ◽  
Evaluation System ◽  
Scientific Discipline ◽  
Data Driven ◽  
Drug Reactions ◽  
The Past ◽  
Assessment And Monitoring ◽  
Medical Big Data ◽  
Data Analysis Methods

Pharmacovigilance, the scientific discipline pertaining to drug safety, has been studied extensively and is progressing continuously. In this field, medical informatics techniques and interpretation play important roles, and appropriate approaches are required. In this study, we investigated and analyzed the trends of pharmacovigilance systems, especially the data collection, detection, assessment, and monitoring processes. We used PubMed to collect papers on pharmacovigilance published over the past 10 years, and analyzed a total of 40 significant papers to determine the characteristics of the databases and data analysis methods used to identify drug safety indicators. Through systematic reviews, we identified the difficulty of standardizing data and terminology and establishing an adverse drug reactions (ADR) evaluation system in pharmacovigilance, and their corresponding implications. We found that appropriate methods and guidelines for active pharmacovigilance using medical big data are still required and should continue to be developed.

scholarly journals Human wetness perception of fabrics under dynamic skin contact

2017 ◽  
Vol 88 (19) ◽  
pp. 2155-2168 ◽  
Author(s):  
Margherita Raccuglia ◽  
Kolby Pistak ◽  
Christian Heyde ◽  
Jianguo Qu ◽  
Ningtao Mao ◽  
...  
Keyword(s):  
Surface Roughness ◽  
High Pressure ◽  
Skin Temperature ◽  
Surface Texture ◽  
Evaluation System ◽  
Temperature Drop ◽  
Local Skin ◽  
Dynamic Conditions ◽  
Texture Properties ◽  
Local Skin Temperature

This experiment studied textile (surface texture, thickness) and non-textile (local skin temperature changes, stickiness sensation and fabric-to-skin pressure) parameters affecting skin wetness perception under dynamic interactions. Changes in fabric texture sensation between WET and DRY states and their effect on pleasantness were also studied. The surface texture of eight fabric samples, selected for their different structures, was determined from surface roughness measurements using the Kawabata Evaluation System. Sixteen participants assessed fabric wetness perception, at high pressure and low pressure conditions, stickiness, texture and pleasantness sensation on the ventral forearm. Differences in wetness perception (p < 0.05) were not determined by texture properties and/or texture sensation. Stickiness sensation and local skin temperature drop were determined as predictors of wetness perception (r2 = 0.89), and although thickness did not correlate with wetness perception directly, when combined with stickiness sensation it provided a similar predictive power (r2 = 0.86). Greater (p < 0.05) wetness perception responses at high pressure were observed compared with low pressure. Texture sensation affected pleasantness in DRY (r2 = 0.89) and WET (r2 = 0.93). In WET, pleasantness was significantly reduced (p < 0.05) compared to DRY, likely due to the concomitant increase in texture sensation (p < 0.05). In summary, under dynamic conditions, changes in stickiness sensation and wetness perception could not be attributed to fabric texture properties (i.e. surface roughness) measured by the Kawabata Evaluation System. In dynamic conditions thickness or skin temperature drop can predict fabric wetness perception only when including stickiness sensation data.

scholarly journals DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

2020 ◽  
Author(s):  
Alexander Rodríguez ◽  
Anika Tabassum ◽  
Jiaming Cui ◽  
Jiajia Xie ◽  
Javen Ho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Noisy Data ◽  
Sparse Data ◽  
Heterogeneous Data ◽  
Exploratory Analysis ◽  
Data Driven ◽  
Short Term ◽  
Learning Framework ◽  
Disease Exposure

AbstractHow do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCovid, an operational deep learning framework designed for real-time COVID-19 forecasting. Deep-Covid works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

scholarly journals Sparse Data Driven Mesh Deformation

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Lin Gao ◽  
Yu-Kun Lai ◽  
Jie Yang ◽  
Zhang Ling-Xiao ◽  
Shihong Xia ◽  
...  
Keyword(s):  
Sparse Data ◽  
Data Driven ◽  
Mesh Deformation

scholarly journals On Data-Driven Sparse Sensing and Linear Estimation of Fluid Flows

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3752
Author(s):  
Balaji Jayaraman ◽  
S M Abdullah Al Mamun
Keyword(s):  
Fluid Flows ◽  
Sparse Data ◽  
Data Driven ◽  
Estimation Methods ◽  
Natural Environments ◽  
Fine Scale ◽  
Linear Estimation ◽  
Performance Improvements ◽  
Learning Machine ◽  
Low Dimensional

The reconstruction of fine-scale information from sparse data measured at irregular locations is often needed in many diverse applications, including numerous instances of practical fluid dynamics observed in natural environments. This need is driven by tasks such as data assimilation or the recovery of fine-scale knowledge including models from limited data. Sparse reconstruction is inherently badly represented when formulated as a linear estimation problem. Therefore, the most successful linear estimation approaches are better represented by recovering the full state on an encoded low-dimensional basis that effectively spans the data. Commonly used low-dimensional spaces include those characterized by orthogonal Fourier and data-driven proper orthogonal decomposition (POD) modes. This article deals with the use of linear estimation methods when one encounters a non-orthogonal basis. As a representative thought example, we focus on linear estimation using a basis from shallow extreme learning machine (ELM) autoencoder networks that are easy to learn but non-orthogonal and which certainly do not parsimoniously represent the data, thus requiring numerous sensors for effective reconstruction. In this paper, we present an efficient and robust framework for sparse data-driven sensor placement and the consequent recovery of the higher-resolution field of basis vectors. The performance improvements are illustrated through examples of fluid flows with varying complexity and benchmarked against well-known POD-based sparse recovery methods.

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