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

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

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 $28 \times 28$ 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