4,694 research outputs found
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
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
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
DCTNet : A Simple Learning-free Approach for Face Recognition
PCANet was proposed as a lightweight deep learning network that mainly
leverages Principal Component Analysis (PCA) to learn multistage filter banks
followed by binarization and block-wise histograming. PCANet was shown worked
surprisingly well in various image classification tasks. However, PCANet is
data-dependence hence inflexible. In this paper, we proposed a
data-independence network, dubbed DCTNet for face recognition in which we adopt
Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is
motivated by the fact that 2D DCT basis is indeed a good approximation for high
ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated
sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is
free from learning as 2D DCT bases can be computed in advance. Besides that, we
also proposed an effective method to regulate the block-wise histogram feature
vector of DCTNet for robustness. It is shown to provide surprising performance
boost when the probe image is considerably different in appearance from the
gallery image. We evaluate the performance of DCTNet extensively on a number of
benchmark face databases and being able to achieve on par with or often better
accuracy performance than PCANet.Comment: APSIPA ASC 201
Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power
In the probabilistic energy forecasting literature, emphasis is mainly placed
on deriving marginal predictive densities for which each random variable is
dealt with individually. Such marginals description is sufficient for power
systems related operational problems if and only if optimal decisions are to be
made for each lead-time and each location independently of each other. However,
many of these operational processes are temporally and spatially coupled, while
uncertainty in photovoltaic (PV) generation is strongly dependent in time and
in space. This issue is addressed here by analysing and capturing
spatio-temporal dependencies in PV generation. Multivariate predictive
distributions are modelled and space-time trajectories describing the potential
evolution of forecast errors through successive lead-times and locations are
generated. Discrimination ability of the relevant scoring rules on performance
assessment of space-time trajectories of PV generation is also studied.
Finally, the advantage of taking into account space-time correlations over
probabilistic and point forecasts is investigated. The empirical investigation
is based on the solar PV dataset of the Global Energy Forecasting Competition
(GEFCom) 2014.Comment: 33 pages, 11 Figure
- …