Relative depth reconstruction method using marine ostracods: A case-study from the upper Barremian–Aptian of the Crimean Mountains

2021 ◽  
Vol 120 ◽  
pp. 104719
Author(s):  
Maria Karpuk
Keyword(s):  
Reconstruction Method ◽  
Relative Depth ◽  
Crimean Mountains ◽  
Depth Reconstruction

Mixed Precision ℓ1 Solver for Compressive Depth Reconstruction: An ADMM Case Study

2021 ◽  
Author(s):  
Yun Wu ◽  
Andrew Michael Wallace ◽  
Andreas Asmann ◽  
Brian Stewart
Keyword(s):  
Mixed Precision ◽  
Depth Reconstruction

Sampling and Reconstructing User Experience

2013 ◽  
pp. 225-245
Author(s):  
Panos Markopoulos ◽  
Vassilis-Javed Khan
Keyword(s):  
Experience Sampling ◽  
Field Studies ◽  
Reconstruction Method ◽  
Working Parents ◽  
Diary Method ◽  
User Attitudes ◽  
Communication Needs ◽  
Sampling And Reconstruction

The Experience Sampling and Reconstruction Method (ESRM) is a research method suitable for user studies conducted in situ that is needed for the design and evaluation of ambient intelligence technologies. ESRM is a diary method supported by a distributed application, Reconexp, which runs on a mobile device and a website, enabling surveying user attitudes, experiences, and requirements in field studies. ESRM combines aspects of the Experience Sampling Method and the Day Reconstruction Method aiming to reduce data loss, improve data quality, and reduce burden put upon participants. The authors present a case study of using this method in the context of a study of communication needs of working parents with young children. Requirements for future developments of the tool and the method are discussed.

Identifying the Cyber-Incidents in Additive Manufacturing Systems via Multimedia Signals

2020 ◽  
Author(s):  
Wei Yang ◽  
Jialei Chen ◽  
Kamran Paynabar ◽  
Chuck Zhang
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing Technology ◽  
Reconstruction Method ◽  
3D Printer ◽  
Online Detection ◽  
Audio Signals ◽  
Detection Mechanism ◽  
Video Signals

Abstract Additive Manufacturing (AM) is an emerging manufacturing technology that plays a growing role in both industrial and consumer settings. However, security concerns of the AM have been raised among researchers. In this paper, we present an online detection mechanism for the malicious attempts on AM system, which taps into both audios and videos collected during the actual printing process. For audio signals, we propose to monitor the characteristics or patterns in the spectrogram via the Wasserstain metric. For video signals, we present a path reconstruction method which effectively monitors the motion of the printer extruder. We then show the effectiveness of our methods in a case study using Ender 3D printer, where the cyber-incidence of modifying the internal fill density can be easily identified in an online manner.

scholarly journals TRANSFORMASI RUANG AKIBAT KONFLIK

2019 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Agung Wahyudi ◽  
Imam Buchori ◽  
Joesron Alie Sjahbana
Keyword(s):  
Research Methods ◽  
Spatial Planning ◽  
Conflicts Of Interest ◽  
Reconstruction Method ◽  
Tourist Destination ◽  
Alternative Concept ◽  
The People

The transformation of space in architecture due to conflicts of interest, has not been studied much, so that architecture is used as a benchmark for understanding quality, marking the ownership or power of a group towards space. With a case study of conflicts of interest in the Kuta Bali tourist area, and by applying the land reconstruction method, it was found a process of changing space and its impact on the people of Kuta in particular and for Bali tourism in general, because Kuta is a major tourist destination. In this study will refine the theory of the description of architectural processes of space change due to conflicts of interest. Findings about inter-disciplinary research methods, bridging architectural science with its supporting sciences (sociology, psychology, anthropology). The role of architecture will support an alternative concept in the process of changing space due to conflicts of interest, as well as spatial planning that experiences dynamic and multi-dimensionalchanges.

Sampling and Reconstructing User Experience

2011 ◽  
Vol 2 (3) ◽  
pp. 53-72 ◽  
Author(s):  
Panos Markopoulos ◽  
Vassilis-Javed Khan
Keyword(s):  
Experience Sampling ◽  
Field Studies ◽  
Reconstruction Method ◽  
Working Parents ◽  
Diary Method ◽  
User Attitudes ◽  
Communication Needs ◽  
Sampling And Reconstruction

The Experience Sampling and Reconstruction (ESRM) method is a research method suitable for user studies conducted in situ that is needed for the design and evaluation of ambient intelligence technologies. ESRM is a diary method supported by a distributed application, Reconexp, which runs on a mobile device and a website, enabling surveying user attitudes, experiences, and requirements in field studies. ESRM combines aspects of the Experience Sampling Method and the Day Reconstruction Method aiming to reduce data loss, improve data quality, and reduce burden put upon participants. The authors present a case study of using this method in the context of a study of communication needs of working parents with young children. Requirements for future developments of the tool and the method are discussed.

scholarly journals A Simple Hybrid Model for Short-Term Load Forecasting

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Suseelatha Annamareddi ◽  
Sudheer Gopinathan ◽  
Bharathi Dora
Keyword(s):  
Hybrid Model ◽  
Wavelet Coefficient ◽  
Series Data ◽  
Reconstruction Method ◽  
Short Term ◽  
Adaptive Smoothing ◽  
Double Exponential ◽  
Variation Characteristics ◽  
Double Exponential Smoothing

The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.

scholarly journals A universal error source in past climate estimates derived from tree rings

2016 ◽  
Author(s):  
Juhani Rinne ◽  
Mikko Alestalo ◽  
Jörg Franke
Keyword(s):  
Tree Rings ◽  
Low Frequency ◽  
Reconstruction Method ◽  
Low Frequencies ◽  
Climatic Oscillations ◽  
Low Frequency Variability ◽  
Data Gaps ◽  
Study Case

Abstract. Recently it has been shown that climate estimates derived from tree rings often tend to show erroneous long-term oscillations, i.e. there are spectral biases at low frequencies. The result is independent of parameter studied (precipitation or temperature) or measured proxy (tree ring widths or maximum latewood densities). In order to find reasons for such universal errors, a new reconstruction method is introduced where no age dependence of the tree rings is determined. The aim, however, is not to generate better reconstructions but to study error variances of long-term oscillations. It is shown that paucities and data gaps due to missing trees increase the risk for erroneous low-frequency variability. A general approximate formula is introduced in order to estimate the presence of such a risk. A case study using Torneträsk data from Northern Sweden illustrates how longer periods with missing trees cause paucities and gaps leading to erroneous climatic oscillations. Systematic underestimation of the temperature around AD 1600 and after 1950 (“divergence”) is in the study case explained by such data gaps and paucities.

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