scholarly journals Spectral Design of Active Mechanical and Electrical Metamaterials

Author(s):  
H. Ronellenfitsch ◽  
J. Dunkel
Keyword(s):  
2021 ◽  
Vol 331 ◽  
pp. 07009
Author(s):  
I Wayan Sengara ◽  
Fahmi Aldiamar

General assessment on earthquake resistance spectral design load criteria for buildings and infrastructures associated with the recent development of Indonesian seismic hazard maps is presented in this paper. The assessment is directed toward general identification of their associated risks for input to policy formulation of disaster risk reduction management plans or strategies. Indonesian seismic hazard maps haveevolved for the last three decades. This is originated from an early development map before 2002, where a seismic hazard map particularly for buildings (1983) was developed adopting the early process of probabilisticseismic hazard analysis (PSHA) for 200 years return period (RP). Further, a 2002 version seismic hazard maphas been developed in the form of peak ground acceleration (PGA) for 500 years RP. Spectral design criteriafor buildings and bridges have been later developed by updating PSHA involving new seismic source zones, ground-motion predictive equations, and various earthquake RP, accommodating seismic codes for buildings(2500 years RP), for bridges (1000 years RP) and dams involving various RP up to 10,000 years RP correspond to its design level. The spectral accelerations also have included PGA, short (0.2s) period, and 1-s period. The latest update hazard maps (2017) have been developed and adopted for seismic codes for buildings, bridges, dams, and other related infrastructures. The increase in spectral design load criteria is identified to assess the general risk of existing constructions, considering the results of several recent building damage surveys. Adoption of new seismic codes based on the most recent hazard maps along with its enforcement is expected to contribute to seismic disaster risk reduction in Indonesia.


2005 ◽  
Vol 53 (3) ◽  
pp. 1045-1056 ◽  
Author(s):  
S. Hoyos ◽  
J. Bacca ◽  
G.R. Arce

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Snjezana Soltic ◽  
Andrew Chalmers

The advent of the LED light source has promoted the concept of human-centric lighting (HCL). The LED has also been responsible for increases in the electrical efficiency of lighting systems, coupled with recent improvements in their colour properties. We have found that it is also possible to create a lit environment with enhanced clinical attributes by providing a source spectrum that meets the requirements of the Cyanosis Observation Index (COI). This paper describes the use of a differential evolution (DE) algorithm for the spectral design of a mixed LED light source capable of meeting COI recommendations as well as HCL performance criteria.


2020 ◽  
Vol 95 (5) ◽  
pp. 055210 ◽  
Author(s):  
David J Fernández C ◽  
Barnana Roy

1996 ◽  
Vol 68 (7) ◽  
pp. 1441-1442 ◽  
Author(s):  
S. Nakamura ◽  
A. Murakami ◽  
M. Adachi ◽  
M. Irie

2016 ◽  
Vol 55 (28) ◽  
pp. 7771 ◽  
Author(s):  
Fuzheng Zhang ◽  
Haisong Xu ◽  
Zhehong Wang

2016 ◽  
Vol 49 (6) ◽  
pp. 697-713 ◽  
Author(s):  
TQ Khanh ◽  
P Bodrogi ◽  
QT Vinh ◽  
D Stojanovic

In Part I of this work, observers scaled colour preference, naturalness and vividness visually on interval scales (0–100) labelled by semantic categories (e.g. ‘moderate’, ‘good’ and ‘very good’) in the context of office lighting. Five customary light sources without object saturation effect illuminated a table with coloured objects in a real room. The observers’ assessments were predicted by recent colour quality indices and selected pairs of indices combined linearly. Criterion values of the indices for ‘good’ colour preference and vividness were determined to provide a usable acceptance limit for the spectral design and evaluation of light sources. To predict colour preference, correlated colour temperature turned out to be useful. In Part 2 of this work, another experiment with the same method but using multi-LED spectra with more object saturation will be analysed and the two datasets will be merged.


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