scholarly journals Databased prediction and planning of order-specific transition times

Procedia CIRP ◽  
2020 ◽  
Vol 93 ◽  
pp. 885-890
Author(s):  
Günther Schuh ◽  
Andreas Gützlaff ◽  
Frederick Sauermann ◽  
Oliver Kaul ◽  
Nicolas Klein
Keyword(s):  
Transition Times

Transition Times in Memory

2018 ◽  
Author(s):  
Donatella della Porta ◽  
Massimiliano Andretta ◽  
Tiago Fernandes ◽  
Eduardo Romanos ◽  
Markos Vogiatzoglou
Keyword(s):  
Theoretical Model ◽  
Social Movement ◽  
Transition Period ◽  
Two Dimensions ◽  
Democratic Regime ◽  
Organizational Models ◽  
Different Types ◽  
Transition Times ◽  
Movement Participation

The second chapter covers the main characteristics of transition time in the four countries: Italy, Greece, Spain, and Portugal. After developing the theoretical model on paths of transition, with a focus on social movement participation, the chapter looks at social movements and protest events as turning points during transition, covering in particular the specific movement actors, their organizational models, and their repertoires of action and frames. The chapter focuses on two dimensions: the role of mobilization in the transition period, which implies the analysis of how elites and masses interact, ally, or fight with each other in the process, and the outcome of transitions as continuity versus rupture of the democratic regime vis-à-vis the old one. It concludes by elaborating some hypotheses on how different modes of transition may produce different types and uses of (transition) memories.

scholarly journals Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1303
Author(s):  
Karol Lisowski ◽  
Andrzej Czyżewski
Keyword(s):  
Statistical Data ◽  
Input Parameter ◽  
Gaussian Mixture ◽  
Adaptive Method ◽  
Transition Time ◽  
Surveillance Systems ◽  
Modified Particle Swarm Optimization ◽  
Novel Approach ◽  
Transition Times ◽  
Temporal Dependencies

A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously utilized Expectation-Maximization (EM) approach, requiring setting the number of mixtures arbitrarily as an input parameter, was extended with the algorithm that automatically adapts the model to statistical data. The probabilistic model was obtained by matching to the histogram of transition times between a particular pair of cameras. The proposed matching procedure uses a modified particle swarm optimization (mPSO). A way of using models of transition time in object re-identification is also presented. Experiments with the proposed method of modeling the transition time were carried out, and a comparison between previous and novel approach results are also presented, revealing that added swarms approximate normalized histograms very effectively. Moreover, the proposed swarm-based algorithm allows for modelling the same statistical data with a lower number of summands in GMM.

scholarly journals Event Matching Classification Method for Non-Intrusive Load Monitoring

2021 ◽  
Vol 13 (2) ◽  
pp. 693
Author(s):  
Elnaz Azizi ◽  
Mohammad T. H. Beheshti ◽  
Sadegh Bolouki
Keyword(s):  
Energy Management ◽  
Low Frequency ◽  
Training Dataset ◽  
Mode Transition ◽  
Smart Meters ◽  
Available Energy ◽  
Consumption Values ◽  
Load Monitoring ◽  
Transition Times ◽  
Event Based

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

Self-Management of Schedule following in Three Teenagers with Autism

1995 ◽  
Vol 20 (3) ◽  
pp. 190-196 ◽  
Author(s):  
Bobby Newman ◽  
Dawn M. Buffington ◽  
Mairead A. O'grady ◽  
Mary E. Mcdonald ◽  
Claire L. Poulson ◽  
...  
Keyword(s):  
Treatment Phase ◽  
Noncontingent Reinforcement ◽  
Transition Time ◽  
Multiple Baseline ◽  
Self Management ◽  
Accurate Identification ◽  
Baseline Conditions ◽  
Transition Times

A multiple baseline across students design was used to investigate the effects of a self-management package on schedule following by three teenagers with autism. During baseline conditions, noncontingent reinforcement was provided. In the treatment phase, students contingently self-reinforced the verbal identification of transition times. Systematic increases in accurate identification of transitions were observed across all students. Accurate identification of transition time and self-reinforcement were maintained in a one-month follow-up.

scholarly journals Prediction of photosynthesis in Scots pine ecosystems across Europe by a needle-level theory

2018 ◽  
Vol 18 (18) ◽  
pp. 13321-13328
Author(s):  
Pertti Hari ◽  
Steffen Noe ◽  
Sigrid Dengel ◽  
Jan Elbers ◽  
Bert Gielen ◽  
...  
Keyword(s):  
Scots Pine ◽  
Boreal Forests ◽  
Co2 Flux ◽  
Field Studies ◽  
Co2 Assimilation ◽  
Co2 Fluxes ◽  
New Era ◽  
Evergreen Forests ◽  
Transition Times

Abstract. Photosynthesis provides carbon for the synthesis of macromolecules to construct cells during growth. This is the basis for the key role of photosynthesis in the carbon dynamics of ecosystems and in the biogenic CO2 assimilation. The development of eddy-covariance (EC) measurements for ecosystem CO2 fluxes started a new era in the field studies of photosynthesis. However, the interpretation of the very variable CO2 fluxes in evergreen forests has been problematic especially in transition times such as the spring and autumn. We apply two theoretical needle-level equations that connect the variation in the light intensity, stomatal action and the annual metabolic cycle of photosynthesis. We then use these equations to predict the photosynthetic CO2 flux in five Scots pine stands located from the northern timberline to Central Europe. Our result has strong implications for our conceptual understanding of the effects of the global change on the processes in boreal forests, especially of the changes in the metabolic annual cycle of photosynthesis.

scholarly journals Transcription factor residence time dominates over concentration in transcription activation

2020 ◽  
Author(s):  
Achim P. Popp ◽  
Johannes Hettich ◽  
J. Christof M. Gebhardt
Keyword(s):  
Transcription Factor ◽  
Residence Time ◽  
Single Molecule ◽  
Search Time ◽  
Mrna Synthesis ◽  
Burst Frequency ◽  
Quantitative Measurements ◽  
Rna Fish ◽  
Transition Times ◽  
Basic Course

Transcription is a vital process activated by transcription factor (TF) binding. The active gene releases a burst of transcripts before turning inactive again. While the basic course of transcription is well understood, it is unclear how binding of a TF affects the frequency, duration and size of a transcriptional burst. We systematically varied the residence time and concentration of a synthetic TF and characterized the transcription of a reporter gene by combining single molecule imaging, single molecule RNA-FISH, live transcript visualisation and analysis with a novel algorithm, Burst Inference from mRNA Distributions (BIRD). For this well-defined system, we found that TF binding solely affected burst frequency and variations in TF residence time had a stronger influence than variations in concentration. This enabled us to device a model of gene transcription, in which TF binding triggers multiple successive steps before the gene transits to the active state and actual mRNA synthesis is decoupled from TF presence. We quantified all transition times of the TF and the gene, including the TF search time and the delay between TF binding and the onset of transcription. Our quantitative measurements and analysis revealed detailed kinetic insight, which may serve as basis for a bottom-up understanding of gene regulation.

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