18,775 research outputs found
Building information modeling (BIM) and green building index (GBI) assessment framework for non-residential new construction building (NRNC)
The global construction industry endorsed Building Information Modeling (BIM)
and its many advantages. However, despite this endorsement, BIM still failed to
attract Malaysian companies to use BIM in green building assessment, especially for
the assessment of Green Building Index (GBI), and maintain GBI certification during
building occupancy using BIM features. The main issue of utilizing BIM as a GBI
assessment tool is the applicability of BIM Tools to digitalize GBI credit by design
team, which results in the digitization of GBI criteria into BIM Model. This study
aims to identify common components related to the capability of BIM to digitalize
and assess GBI criteria. These components include BIM uses and tools and GBI
criteria and processes. This study applied quantitative and qualitative approaches to
collect data. The quantitative approach used questionnaires, which were distributed
to 900 GBI members, i.e. GBI certifiers and facilitators. The survey generated a
response rate of 32% during eight months of data collection. The results were
analyzed using SPSS and SmartPLS. Four model categories were identified, namely,
BIM uses, BIM tools, GBI criteria and GBI certification process. These categories
were used to assess the BIM–GBI framework. The results obtained from the
questionnaire showed that only 16 BIM uses must be included in the BIM execution
plan of the GBI project for assessment purposes. The results also showed that the
BIM tools present different levels of effect on the GBI criteria. The capability of
BIM to assess GBI could be stronger in the design assessment (DA) than in the
operation assessment, which supports the suggested BIM–GBI assessment
framework. The second data collection was conducted through a focus group
interview with BIM and GBI experts. Two interview sessions were conducted.
Results show that the assessment method has a significant correlation in the BIM–
GBI framework. The following categories were identified for the BIM assessment
framework: BIM uses, BIM tools, and control, which were based on the GBI criteria
for scoring and certification. Findings from the BIM and GBI assessment method
framework show that GBI credits can be digitalized using different BIM uses directly
and indirectly assessed by BIM tools for each GBI credit in both GBI assessment
process. Based on the qualitative result of this research showed that BIM can help the
design team to achieve 55% point in design assessment (DA) only and this helps the
building to achieve GBI certification in level 4 of certified rating. On the other hand,
45% points of GBI credits can be digitals in completion and verification assessment
(CVA). The framework provides a guide for the design team and facility
management in digitalizing and assessing GBI criteria using BIM application during
design assessment (DA) and completion and verification assessment (CVA) for new
nonresidential constructions. The framework also offers and provides insights that
will enable designers to understand the relationship between BIM and GBI criteria,
which will contribute to BIM integration in Stage 3 and automate GBI assessment for
the Malaysian construction industry
Quadrature Phase Shift Keying (QPSK) Modulator Design using Multi-Port Network in Multilayer Microstrip-Slot Technology for Wireless Communication Applications
The design of the quadrature phase shift keying (QPSK) modulator by using a multi-port network is proposed in this article for the use in wireless communication applications. The multi-port network is in the form of multilayer microstrip-slot technology. This multi-port network is composed of three 3-dB rectangular-shaped directional couplers with virtual stubs and an equal power division divider with in-phase characteristic. The design is performed by applying a full-wave electromagnetic simulation software, CST Microwave Studio (CST MWS). Keysight’s Advanced Design System (ADS) is applied in analyzing and evaluating the QPSK constellation of the proposed modulator. This comparatively small size of proposed design has been fabricated, and its wideband performance of 2 to 6 GHz is verified
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949
Operation of stand-alone self-excited induction generator supported by energy storage systems for small scale wind energy generation
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