A parametric building energy cost optimization tool based on a genetic algorithm

This record of study summarizes the work accomplished during the internship at the Energy Systems Laboratory of the Texas Engineering Experiment Station. The internship project was to develop a tool to optimize the building parameters so that the overall building energy cost is minimized. A metaheuristic: genetic algorithm was identified as the solution algorithm and was implemented in the problem under study. Through two case studies, the impacts of the three genetic algorithm parameters, namely population size, crossover and mutation rates, on the algorithm's overall performance are also studied through statistical tests. Through these statistical tests, the optimum combination of above the mentioned parameters is also identified and applied. Finally, a performance analysis based on the case studies show that the tool achieved satisfactory results

Optimal Transport and Equilibrium Problems in Mathematical Finance

The thesis consists of three independent topics, each of which is discussed in an individual chapter. The first chapter considers a multiperiod optimal transport problem where distributions μ0, . . . , μn are prescribed and a transport corresponds to a scalar martingale X with marginals Xt ∼ μt. We introduce particular couplings called left-monotone transports; they are characterized equivalently by a no-crossing property of their support, as simultaneous optimizers for a class of bivariate transport cost functions with a Spence–Mirrlees property, and by an order-theoretic minimality property. Left-monotone transports are unique if μ0 is atomless, but not in general. In the one-period case n = 1, these transports reduce to the Left-Curtain coupling of Beiglbo ̈ck and Juillet. In the multiperiod case, the bivariate marginals for dates (0,t) are of Left-Curtain type, if and only if μ0, . . . , μn have a specific order property. The general analysis of the transport problem also gives rise to a strong duality result and a description of its polar sets. Finally, we study a variant where the intermediate marginals μ1,...,μn−1 are not prescribed. The second chapter studies the convergence of Nash equilibria in a game of optimal stopping. If the associated mean field game has a unique equilibrium, any sequence of n-player equilibria converges to it as n → ∞. However, both the finite and infinite player versions of the game often admit multiple equilibria. We show that mean field equilibria satisfying a transversality condition are limit points of n-player equilibria, but we also exhibit a remarkable class of mean field equilibria that are not limits, thus questioning their interpretation as “large n” equilibria. The third chapter studies the equilibrium price of an asset that is traded in continuous time between N agents who have heterogeneous beliefs about the state process underlying the asset’s payoff. We propose a tractable model where agents maximize expected returns under quadratic costs on inventories and trading rates. The unique equilibrium price is characterized by a weakly coupled system of linear parabolic equations which shows that holding and liquidity costs play dual roles. We derive the leading-order asymptotics for small transaction and holding costs which give further insight into the equilibrium and the consequences of illiquidity

Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed

We present a novel method for efficiently producing semi-dense matches across images. Previous detector-free matcher LoFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios but suffers from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. One key observation is that performing the transformer over the entire feature map is redundant due to shared local information, therefore we propose an aggregated attention mechanism with adaptive token selection for efficiency. Furthermore, we find spatial variance exists in LoFTR's fine correlation module, which is adverse to matching accuracy. A novel two-stage correlation layer is proposed to achieve accurate subpixel correspondences for accuracy improvement. Our efficiency optimized model is $\sim 2.5\times$ faster than LoFTR which can even surpass state-of-the-art efficient sparse matching pipeline SuperPoint + LightGlue. Moreover, extensive experiments show that our method can achieve higher accuracy compared with competitive semi-dense matchers, with considerable efficiency benefits. This opens up exciting prospects for large-scale or latency-sensitive applications such as image retrieval and 3D reconstruction. Project page: https://zju3dv.github.io/efficientloftr.Comment: CVPR 2024; Project page: https://zju3dv.github.io/efficientloft

Robust unsupervised small area change detection from SAR imagery using deep learning

Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection

Research on Water Absorption and Frost Resistance of Concrete Coated with Different Impregnating Agents for Ballastless Track Structure

In consideration of performance requirement of ballastless track concrete in cold regions of China, 3 types of commercially available impregnating agents were employed to research their effect on water absorption and frozen resistance of concrete, containing silanes, potassium silicate and osmotic curing agent. The results presented that coating silanes was the most effective on the reduction of water absorption among all employed impregnating agents, because of the most significant character change of concrete surface from hydrophilicity to hydrophobicity which could be proved by the contact angle test of concrete. The promotion on frozen resistance of concrete was not as significant as that for water absorption by coating 3 commercially available types of impregnant agents, because of the spalling damage on concrete surface during the freezing-thawing cycles

A novel molecular pathway of lipid accumulation in human hepatocytes caused by PFOA and PFOS

Exposed to ubiquitously perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with non-alcoholic fatty liver disease (NAFLD), yet the underlying molecular mechanism remains elusive. The extrapolation of empirical studies correlating per- and polyfluoroalkyl substance (PFAS) exposure with NAFLD occurrence to real-life exposure was hindered by the limited availability of mechanistic data at environmentally relevant concentrations. Herein, a novel pathway mediating hepatocyte lipid accumulation by PFOA and PFOS at human-relevant dose (&lt;10 μM) was identified by integrating CRISPR-Cas9 genome screening, concentration-dependent transcriptional assay in HepG2 cell and epidemiological data mining. 1) At genetic level, nudt7 showed the highest enriched potency among 569 NAFLD-related genes, and the transcription of nudt7 was significantly downregulated by PFOA and PFOS exposure (&lt;7 μM). 2) At molecular pathway, upon exposure to ≤10-4 μM PFOA and PFOS, the downregulation of nudt7 transcriptional expression triggered the reduction of Ace-CoA hydrolase activity. 3) At cellular level, increased lipids were measured in HepG2 cells with PFOA and PFOS (&lt;2 μM). Overall, we identified a novel mechanism mediated by transcriptional downregulation of nudt7 gene in hepatocellular lipid increase treated with PFOA and PFOS, which could potentially explain the NAFLD occurrence associated with exposure to PFASs in humans.</p