Positioning Accuracy Improvement via Distributed Location Estimate in Cooperative Vehicular Networks

The development of cooperative vehicle safety (CVS) applications, such as collision warnings, turning assistants, and speed advisories, etc., has received great attention in the past few years. Accurate vehicular localization is essential to enable these applications. In this study, motivated by the proliferation of the Global Positioning System (GPS) devices, and the increasing sophistication of wireless communication technologies in vehicular networks, we propose a distributed location estimate algorithm to improve the positioning accuracy via cooperative inter-vehicle distance measurement. In particular, we compute the inter-vehicle distance based on raw GPS pseudorange measurements, instead of depending on traditional radio-based ranging techniques, which usually either suffer from high hardware cost or have inadequate positioning accuracy. In addition, we improve the estimation of the vehicles' locations only based on the inaccurate GPS fixes, without using any anchors with known exact locations. The algorithm is decentralized, which enhances its practicability in highly dynamic vehicular networks. We have developed a simulation model to evaluate the performance of the proposed algorithm, and the results demonstrate that the algorithm can significantly improve the positioning accuracy.Comment: To appear in Proc. of the 15th International IEEE Conference on Intelligent Transportation Systems (IEEE ITSC'12

Anxious states : culture and politics in Singapore and Hong Kong

Since Singapore and Hong Kong are the two most economically successful, ethnic Chinese dominant city-states in Asia, comparisons have always been made between these locations. Fundamental to the Singaporean collective social life is a realization that ‘the world does not need Singapore but Singapore needs the world’. The demand for immigrants to supplement the small local workforce is constant, adding complexity to the domestic multi-ethnic population and geopolitical situation, and confounding the processes of individual and national identity formation. The constant demand of physical space threatens to erase heritage, social memories and individual biographies, yet simultaneously encourages a progressive future-mindedness. The prevalent social anxieties undergird a wide political consensus that emphasizes stability, cohesion and political order. This has engendered a ‘politics of the middle ground’, favoured by the long governing single-party dominant parliament, that marginalizes liberal individual rights and individuals who falls out of the ‘middle’. Are such anxieties broadly shared by Hong Kong and its people? And, if they are, how might some of these anxieties be culturally and politically expressed, and in what institutional structural configurations

Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features

Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.Comment: 5 page

Polyhedral Predictive Regions For Power System Applications

Despite substantial improvement in the development of forecasting approaches, conditional and dynamic uncertainty estimates ought to be accommodated in decision-making in power system operation and market, in order to yield either cost-optimal decisions in expectation, or decision with probabilistic guarantees. The representation of uncertainty serves as an interface between forecasting and decision-making problems, with different approaches handling various objects and their parameterization as input. Following substantial developments based on scenario-based stochastic methods, robust and chance-constrained optimization approaches have gained increasing attention. These often rely on polyhedra as a representation of the convex envelope of uncertainty. In the work, we aim to bridge the gap between the probabilistic forecasting literature and such optimization approaches by generating forecasts in the form of polyhedra with probabilistic guarantees. For that, we see polyhedra as parameterized objects under alternative definitions (under $L_1$ and $L_\infty$ norms), the parameters of which may be modelled and predicted. We additionally discuss assessing the predictive skill of such multivariate probabilistic forecasts. An application and related empirical investigation results allow us to verify probabilistic calibration and predictive skills of our polyhedra.Comment: 8 page

An update of a simulation study of passively heated residemtial buildings

A simulation study of passively heated residential buildings” published in Procedia Engineering 2015 showed how circulating 15-17ºC water from a 50-m deep U-tube to a floor radiator and solar-heated water from a 30 evacuated tube solar collector and a 2-m3 indoor tank to a wall radiator could keep a 30-m2 Melbourne, Australia house thermally comfortable. This paper presents a summary of the ongoing review of publications together with three updates: - (1) Report on that water heated by a 100-metre deep U-tube is 22-24ºC, i.e., 2-4 ºC warmer than thermal comfort temperature. (2) May 2016 experimental validations of the simulated results which show that when the outdoors is below 10ºC, the temperature of the floor radiator is 2-4ºC less than the 15-17ºC water heated by a 50-m deep U-tube and 25 W fish tank pumps could circulate the waters. (3) Simulations with the addition of phase change materials (PCM) to inside faces show that though a PCM halves the diurnal indoor temperature variations, it confirms that such PCM does not significantly increase the 20ºC temperature in a 2-m3 storage tank at the end of winter. Therefore, the size of intersessional thermal storage would be a problem for family-sized houses. German Guidelines indicate that 1-2 boreholes could provide enough heat for family-sized houses. The heat extracted in winter can be replenished in summer. Thus the geothermal heat from about 100-m deep boreholes with 22-24ºC bottom temperature could sustainably keep residential buildings in cool climates similar to Melbourne's cool temperate thermally comfortable