2,463 research outputs found
IoT Load Classification and Anomaly Warning in ELV DC Pico-grids using Hierarchical Extended k-Nearest Neighbors
The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc pico-grids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended k-nearest neighbors (HE-kNN) technique that exploits the use of distance in kNN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an ad hoc standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This paper presents the successful implementation of HE-kNN technique in three different circuits: lightings, air-conditioning and multiple load dc pico-grids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data
Coulomb Drag as a Probe of the Nature of Compressible States in a Magnetic Field
Magneto-drag reveals the nature of compressible states and the underlying
interplay of disorder and interactions. At \nu=3/2 a clear T^{4/3} dependence
is observed, which signifies the metallic nature of the N=0 Landau level. In
contrast, drag in higher Landau levels reveals an additional contribution,
which anomalously grows with decreasing T before turning to zero following a
thermal activation law. The anomalous drag is discussed in terms of
electron-hole asymmetry arising from disorder and localization, and the
crossover to normal drag at high fields as due to screening of disorder.Comment: 5 pages, 4 figure
Free-boundary conformal parameterization of point clouds
With the advancement in 3D scanning technology, there has been a surge of
interest in the use of point clouds in science and engineering. To facilitate
the computations and analyses of point clouds, prior works have considered
parameterizing them onto some simple planar domains with a fixed boundary shape
such as a unit circle or a rectangle. However, the geometry of the fixed shape
may lead to some undesirable distortion in the parameterization. It is
therefore more natural to consider free-boundary conformal parameterizations of
point clouds, which minimize the local geometric distortion of the mapping
without constraining the overall shape. In this work, we develop a
free-boundary conformal parameterization method for disk-type point clouds,
which involves a novel approximation scheme of the point cloud Laplacian with
accumulated cotangent weights together with a special treatment at the boundary
points. With the aid of the free-boundary conformal parameterization,
high-quality point cloud meshing can be easily achieved. Furthermore, we show
that using the idea of conformal welding in complex analysis, the point cloud
conformal parameterization can be computed in a divide-and-conquer manner.
Experimental results are presented to demonstrate the effectiveness of the
proposed method
Bijective Density-Equalizing Quasiconformal Map for Multiply-Connected Open Surfaces
This paper proposes a novel method for computing bijective density-equalizing
quasiconformal (DEQ) flattening maps for multiply-connected open surfaces. In
conventional density-equalizing maps, shape deformations are solely driven by
prescribed constraints on the density distribution, defined as the population
per unit area, while the bijectivity and local geometric distortions of the
mappings are uncontrolled. Also, prior methods have primarily focused on
simply-connected open surfaces but not surfaces with more complicated
topologies. Our proposed method overcomes these issues by formulating the
density diffusion process as a quasiconformal flow, which allows us to
effectively control the local geometric distortion and guarantee the
bijectivity of the mapping by solving an energy minimization problem involving
the Beltrami coefficient of the mapping. To achieve an optimal parameterization
of multiply-connected surfaces, we develop an iterative scheme that optimizes
both the shape of the target planar circular domain and the density-equalizing
quasiconformal map onto it. In addition, landmark constraints can be
incorporated into our proposed method for consistent feature alignment. The
method can also be naturally applied to simply-connected open surfaces. By
changing the prescribed population, a large variety of surface flattening maps
with different desired properties can be achieved. The method is tested on both
synthetic and real examples, demonstrating its efficacy in various applications
in computer graphics and medical imaging
Promoting employee safety performance in the Chinese construction industry
In the construction industry, safety leadership has been widely recognised as an indispensable factor that affects organisational safety performance. However, in China specifically, research on safety leadership in the construction domain is not adequately developed. This paper examines the role of organisational leadership in promoting safety performance, as moderated by safety climate. The study adopts quantitative research method through questionnaire survey with 106 construction professionals leading or participating in safety management work in the Chinese construction sectors. The results show that exerting certain leadership strategies that encourage construction stakeholders to comply with safety practices will improve safety performance. At a moment when the whole industry is suffering from momentous safety challenges, transformation is required; these findings are intended to guide construction managers in their commitment to programme safety management. The study reinforces the interaction between upper layer and lower layer employees thereby improving the safety performance via improvements in the safety climate. In addition to being rooted in the full-range leadership model, this paper considered the impo rtant (and often ignored) characteristics of Chinese culture. The study recommends the early involvement of contractors in the design process and considers site hazards when making design decisions
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
Quantum machine learning (QML) has received increasing attention due to its
potential to outperform classical machine learning methods in problems
pertaining classification and identification tasks. A subclass of QML methods
is quantum generative adversarial networks (QGANs) which have been studied as a
quantum counterpart of classical GANs widely used in image manipulation and
generation tasks. The existing work on QGANs is still limited to small-scale
proof-of-concept examples based on images with significant downscaling. Here we
integrate classical and quantum techniques to propose a new hybrid
quantum-classical GAN framework. We demonstrate its superior learning
capabilities by generating pixels grey-scale images without
dimensionality reduction or classical pre/post-processing on multiple classes
of the standard MNIST and Fashion MNIST datasets, which achieves comparable
results to classical frameworks with three orders of magnitude less trainable
generator parameters. To gain further insight into the working of our hybrid
approach, we systematically explore the impact of its parameter space by
varying the number of qubits, the size of image patches, the number of layers
in the generator, the shape of the patches and the choice of prior
distribution. Our results show that increasing the quantum generator size
generally improves the learning capability of the network. The developed
framework provides a foundation for future design of QGANs with optimal
parameter set tailored for complex image generation tasks
Development of an integrated grating and slicing machine for starchy vegetables
Processed foods usually undergo one or several unit food processing operations before becoming the final products. Many food processing equipments were developed to perform more than one operation in food processing by providing practical purposes that further enhance their performance. However, conventional processes of grating and slicing that produce grated and sliced food products normally involved two units of independent operation machines. Therefore in this study, grating and slicing processes have been combined into a single operation through an integrated machine for simultaneous grating and slicing operations. The purpose of integrating both grating and slicing processes is to increase productivity through the reduction of cost, time and the number of unit operations, which are involved in the processing system of grating and slicing production. The machine’s design specifications were identified to ensure that simultaneous grating and slicing operations in an integrated machine are capable to process the raw materials (starchy vegetables) simultaneously for grated and sliced outputs. A final machine design was generated by following a product development process as the research method. The design process steps starts from planning, concept development, detail design and machine fabrication, testing and refinement. The final design of the machine (at present) shows that it is suitable for use in industrial processing level which the output rate is powered at 750 W with variable speed of 0 – 180 rpm, grated and sliced production range of 750 – 1200 kg/h and 250– 400 kg/h, respectively. This newly designed machine is easy to setup, handle, store, clean, service and maintain. The design of an integrated grating and slicing machine will express a better understanding on the machine capability to reduce cost and energy for simultaneous grating and slicing processes with increased productivity
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