1,628 research outputs found
Studies of polymers in fouling release coating science
In chapter one, we describe our studies of the solubility properties of two biocides in F127 aqueous solutions. One is zinc omadine and the other is C9211. The partition coefficients of these two biocides were also determined from the analytical results.
In chapter two, we conducted phase related studies of a triblock copolymer, PEO400- PBO55-PEO400 in aqueous solution. The phase diagram features of PEO400-PBO55-PEO400 in water are compared with those of F127. We believe the lack of a lower gel to liquid transition boundary in this system is because of the relatively longer PEO segments relative to the PBO segment.
In chapter three, we successfully prepared and characterized a polymerizable macromonomer, PEG-PS-DVB. A preliminary application of the polymer was investigated and nanoparticles were prepared by microemulsion polymerization. According to the characterizations, the nanoparticles have crosslinked cores and could be used as “inorganic free” nanofluid. No apparent thermoreversible behaviors were observed for these nanoparticles in water
Hybrid Radio-map for Noise Tolerant Wireless Indoor Localization
In wireless networks, radio-map based locating techniques are commonly used
to cope the complex fading feature of radio signal, in which a radio-map is
built by calibrating received signal strength (RSS) signatures at training
locations in the offline phase. However, in severe hostile environments, such
as in ship cabins where severe shadowing, blocking and multi-path fading
effects are posed by ubiquitous metallic architecture, even radio-map cannot
capture the dynamics of RSS. In this paper, we introduced multiple feature
radio-map location method for severely noisy environments. We proposed to add
low variance signature into radio map. Since the low variance signatures are
generally expensive to obtain, we focus on the scenario when the low variance
signatures are sparse. We studied efficient construction of multi-feature
radio-map in offline phase, and proposed feasible region narrowing down and
particle based algorithm for online tracking. Simulation results show the
remarkably performance improvement in terms of positioning accuracy and
robustness against RSS noises than the traditional radio-map method.Comment: 6 pages, 11th IEEE International Conference on Networking, Sensing
and Control, April 7-9, 2014, Miami, FL, US
Stochastic path-integral approach for predicting the superconducting temperatures of anharmonic solids
We develop a stochastic path-integral approach for predicting the
superconducting transition temperatures of anharmonic solids. By defining
generalized Bloch basis, we generalize the formalism of the stochastic
path-integral approach, which is originally developed for liquid systems. We
implement the formalism for ab initio calculations using the projector
augmented-wave method, and apply the implementation to estimate the
superconducting transition temperatures of metallic deuterium and hydrogen
sulfide. For metallic deuterium, which is approximately harmonic, our result
coincides well with that obtained from the standard approach based on the
harmonic approximation and the density functional perturbation theory. For
hydrogen sulfide, we find that anharmonicity strongly suppresses the predicted
superconducting transition temperature. Compared to the self-consistent
harmonic approximation approach, our approach yields a transition temperature
closer to the experimentally observed one.Comment: 12 pages, 12 figures, 3 table
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision
tasks, with the strong capability to extract discriminative information for
imagesets and videos. However, such learning algorithms particularly on
high-dimensional Grassmann manifold always involve with significantly high
computational cost, which seriously limits the applicability of learning on
Grassmann manifold in more wide areas. In this research, we propose an
unsupervised dimensionality reduction algorithm on Grassmann manifold based on
the Locality Preserving Projections (LPP) criterion. LPP is a commonly used
dimensionality reduction algorithm for vector-valued data, aiming to preserve
local structure of data in the dimension-reduced space. The strategy is to
construct a mapping from higher dimensional Grassmann manifold into the one in
a relative low-dimensional with more discriminative capability. The proposed
method can be optimized as a basic eigenvalue problem. The performance of our
proposed method is assessed on several classification and clustering tasks and
the experimental results show its clear advantages over other Grassmann based
algorithms.Comment: Accepted by IJCAI 201
A Non-Iterative Balancing Method for HVAC Duct System
Building Heating, Ventilation and Air Conditioning (HVAC) system maintain comfortable indoor environment by supplying processed air to each terminal precisely through duct system. Testing, Adjusting and Balancing (TAB) plays critical role in achieving desired air distribution. Traditional TAB method is inaccurate and inefficient due to its trail-and-error natural, which forces people to pay high but expect low. Recently, it has been proposed that non-iterative approach to TAB is promising to improve performance and reduce cost. In this paper, a novel non-iterative balancing method is developed and implemented for TAB engineers to adjust dampers systematically and efficiently. Different from other TAB methods, this method is based on modeling and optimization. The mathematical model for duct system is firstly developed from its components including fan, duct segments and dampers to predict flow rates and pressures in the duct system for any damper positions. To identify the parameters in the model, flow rate measurements are taken for each terminal on real system under different damper positions. With the obtained model, optimal damper positions that gives desired air distribution are calculated by minimizing a specific objective function. To facilitate the adjusting process in real duct system, a sequential tuning instructions are generated which can help engineers to adjust dampers to their proper position using flowmeter as indicators. In this sequential tuning process, each damper only adjusts once to reach balance. Because the pressure and airflow dynamics of the duct system has been modeled, the entire TAB procedure is deterministic and non-iterative. Simulations are performed to validate the effectiveness of this method in Matlab/Simulink environment. Comparison study with existing methods shows that the proposed TAB method significantly shorten the duration of process and reduces balancing error while using easily-accessible equipment like pressure sensor and flowmeter only. It can be expected that the TAB service contractor will apply this method for advanced duct system where accurate air distribution is strictly required
A note on exploratory item factor analysis by singular value decomposition
We revisit a singular value decomposition (SVD) algorithm given in Chen et al. (Psychometrika 84:124–146, 2019b) for exploratory item factor analysis (IFA). This algorithm estimates a multidimensional IFA model by SVD and was used to obtain a starting point for joint maximum likelihood estimation in Chen et al. (2019b). Thanks to the analytic and computational properties of SVD, this algorithm guarantees a unique solution and has computational advantage over other exploratory IFA methods. Its computational advantage becomes significant when the numbers of respondents, items, and factors are all large. This algorithm can be viewed as a generalization of principal component analysis to binary data. In this note, we provide the statistical underpinning of the algorithm. In particular, we show its statistical consistency under the same double asymptotic setting as in Chen et al. (2019b). We also demonstrate how this algorithm provides a scree plot for investigating the number of factors and provide its asymptotic theory. Further extensions of the algorithm are discussed. Finally, simulation studies suggest that the algorithm has good finite sample performance
Boosting Continuous Control with Consistency Policy
Due to its training stability and strong expression, the diffusion model has
attracted considerable attention in offline reinforcement learning. However,
several challenges have also come with it: 1) The demand for a large number of
diffusion steps makes the diffusion-model-based methods time inefficient and
limits their applications in real-time control; 2) How to achieve policy
improvement with accurate guidance for diffusion model-based policy is still an
open problem. Inspired by the consistency model, we propose a novel
time-efficiency method named Consistency Policy with Q-Learning (CPQL), which
derives action from noise by a single step. By establishing a mapping from the
reverse diffusion trajectories to the desired policy, we simultaneously address
the issues of time efficiency and inaccurate guidance when updating diffusion
model-based policy with the learned Q-function. We demonstrate that CPQL can
achieve policy improvement with accurate guidance for offline reinforcement
learning, and can be seamlessly extended for online RL tasks. Experimental
results indicate that CPQL achieves new state-of-the-art performance on 11
offline and 21 online tasks, significantly improving inference speed by nearly
45 times compared to Diffusion-QL. We will release our code later.Comment: 18 pages, 9 page
Interpretation on Multi-modal Visual Fusion
In this paper, we present an analytical framework and a novel metric to shed
light on the interpretation of the multimodal vision community. Our approach
involves measuring the proposed semantic variance and feature similarity across
modalities and levels, and conducting semantic and quantitative analyses
through comprehensive experiments. Specifically, we investigate the consistency
and speciality of representations across modalities, evolution rules within
each modality, and the collaboration logic used when optimizing a
multi-modality model. Our studies reveal several important findings, such as
the discrepancy in cross-modal features and the hybrid multi-modal cooperation
rule, which highlights consistency and speciality simultaneously for
complementary inference. Through our dissection and findings on multi-modal
fusion, we facilitate a rethinking of the reasonability and necessity of
popular multi-modal vision fusion strategies. Furthermore, our work lays the
foundation for designing a trustworthy and universal multi-modal fusion model
for a variety of tasks in the future.Comment: This version was under review since 2023/3/
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