112 research outputs found
Essays on capacity underutilization and demand driven business cycles
In Chapter 1, I build a macroeconomic model that features chronic excess capacity. In my model, if one firm expands its capacity while other firms do not, it \steals" profitable demand from others. This capacity competition externality can cause an over-investment in capacity. I show that with the existence of chronic excess capacity, capital resources can be slack and consumption demand shocks can generate realistic business cycles. If consumption demand increases, more capacity will be utilized, heating up the capacity competition: firms invest with haste until the capacity utilization rate falls back to normal. If consumption demand decreases, more capacity will be left idle, cooling down the capacity competition: firms dis-invest with haste until the capacity utilization rate goes back to normal.
In Chapter 2, I show that the above results cannot be obtained in models with efficient utilization of capital or capacity. In these models, there is no capacity competition externality. None of these models could feature chronic excess capacity nor capital resource slackness. Thus, the response of output to demand shocks is limited and it is difficult to obtain demand driven business cycles in these models.
In Chapter 3, I study what kind of goods market structure features the capacity competition externality that can cause chronic excess capacity. The following assumptions are identified. First, if a firm expands its capacity while other firms do not, it can \steal" demand from others. Second, firms can charge a sufficiently high price to make a positive net profit. These two assumptions imply a negative capacity competition externality and are sufficient to cause long-term capacity underutilization at the firm-level. Third, if the invested capital has no positive externality that can potentially offset the negative externality, the capacity competition externality will be dominant and the economy will exhibit chronic excess capacity. I present several different ways to micro-found this kind of goods market structure, demonstrating the generality of the results obtained in the previous chapters
Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter
The likelihood calculation of a vast number of particles is the computational
bottleneck for the particle filter in applications where the observation
information is rich. For fast computing the likelihood of particles, a
numerical fitting approach is proposed to construct the Likelihood Probability
Density Function (Li-PDF) by using a comparably small number of so-called
fulcrums. The likelihood of particles is thereby analytically inferred,
explicitly or implicitly, based on the Li-PDF instead of directly computed by
utilizing the observation, which can significantly reduce the computation and
enables real time filtering. The proposed approach guarantees the estimation
quality when an appropriate fitting function and properly distributed fulcrums
are used. The details for construction of the fitting function and fulcrums are
addressed respectively in detail. In particular, to deal with multivariate
fitting, the nonparametric kernel density estimator is presented which is
flexible and convenient for implicit Li-PDF implementation. Simulation
comparison with a variety of existing approaches on a benchmark 1-dimensional
model and multi-dimensional robot localization and visual tracking demonstrate
the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a
draft/preprint of one paper submitted to the IEEE Transaction
Attribute-preserving gamut mapping of measured BRDFs
Reproducing the appearance of real-world materials using current printing technology is problematic. The reduced number of inks available define the printer's limited gamut, creating distortions in the printed appearance that are hard to control. Gamut mapping refers to the process of bringing an out-of-gamut material appearance into the printer's gamut, while minimizing such distortions as much as possible. We present a novel two-step gamut mapping algorithm that allows users to specify which perceptual attribute of the original material they want to preserve (such as brightness, or roughness). In the first step, we work in the low-dimensional intuitive appearance space recently proposed by Serrano et al. [SGM*16], and adjust achromatic reflectance via an objective function that strives to preserve certain attributes. From such intermediate representation, we then perform an image-based optimization including color information, to bring the BRDF into gamut. We show, both objectively and through a user study, how our method yields superior results compared to the state of the art, with the additional advantage that the user can specify which visual attributes need to be preserved. Moreover, we show how this approach can also be used for attribute-preserving material editing
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved
in the last decade in the context of target tracking, with regard to either a single target or multiple
targets in the presence of false or missing data. The first part of our review is on remarkable
achievements that have been made for the single-target PF from several aspects including importance
proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal
systems. The second part of our review is on analyzing the intractable challenges raised within
the general multitarget (multi-sensor) tracking due to random target birth and termination, false
alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream
multitarget PF approaches consist of two main classes, one based on M2T association approaches and
the other not such as the finite set statistics-based PF. In either case, significant challenges remain due
to unknown tracking scenarios and integrated tracking management
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