Supersolid and pair correlations of the extended Jaynes-Cummings-Hubbard model on triangular lattices

We study the extended Jaynes-Cummings-Hubbard model on triangular cavity lattices and zigzag ladders. By using density-matrix renormalization group methods, we observe various types of solids with different density patterns and find evidence for light supersolids, which exist in extended regions of the phase diagram of the zigzag ladder. Furthermore, we observe strong pair correlations in the supersolid phase due to the interplay between the atoms in the cavities and atom-photon interaction. By means of cluster mean-field simulations and a scaling of the cluster size extending our analysis to two-dimensional triangular lattices, we present evidence for the emergence of a light supersolid in this case also.Comment: 11 pages, 16 figure

Hyperparameter Selection with Good Region Recognition for SVM Based Fault Diagnosis

This paper proposes a novel method of good region recognition for hyperparameter selection of SVM. The method can provide a much smaller good region for optimization search-based methods, and thus it can greatly save computation time. Experimental results show that the proposed method improves effi ciency of fault diagnosis of rolling bearing with no accuracy loss

Photochemical transformation of perfluoroalkyl acid precursors in water using engineered nanomaterials

The production of perfluoroalkyl acids (PFAAs) has been phased out over recent decades; however, no significant decline in their environmental concentrations has been observed. This is partly due to the photochemical decomposition of PFAAs precursors (PrePFAAs) which remain in extensive use. The decomposition of PrePFAAs may be accelerated by the light-activated engineered nanomaterials (ENMs) in water. In light of this hypothesis, we investigated the photochemical transformation of three PrePFAAs, which are 8:2 fluorotelomer sulfonic acid (8:2 FTSA), 8:2 fluorotelomer alcohol (8:2 FTOH), and 2-(N-ethylperfluorooctane-1-sulfonamido ethyl] phosphate (SAmPAP), in the presence of six ENMs under simulated sunlight irradiation. The transformation rates of 8:2 FTSA and 8:2 FTOH were increased by 2–6 times when in the presence of six ENMs. However, most of ENMs appeared to inhibit the decomposition of SAmPAP. The transformation rates of PrePFAAs were found to depend on the yield of reactive oxygen species generated by ENMs, but the rates were also related to compound photo-stability, adsorption to surfaces, and photo-shielding effects. The PrePFAAs are transformed to perfluorooctanoic acid (PFOA) or/and perfluorooctane sulfonate (PFOS) with higher toxicity and longer half-life, PFOA or PFOS and a few PFAAs having shorter carbon chain lengths. Higher concentrations of the PFAAs photodegradation products were observed in the presence of most of the ENMs

Learning and Prediction Theory of Distributed Least Squares

With the fast development of the sensor and network technology, distributed estimation has attracted more and more attention, due to its capability in securing communication, in sustaining scalability, and in enhancing safety and privacy. In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback control. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed LS. Moreover, our theoretical analysis is also different from the existing ones for distributed LS, because it is an integration of several powerful techniques including stochastic Lyapunov functions, martingale convergence theorems, and some inequalities on convex combination of nonnegative definite matrices.Comment: 14 pages, submitted to IEEE Transactions on Automatic Contro

ANALYSIS AND OPTIMIZATION OF CRYSTALLINE SILICON WAFER BASED PHOTOVOLTAIC MODULES

Ph.DDOCTOR OF PHILOSOPH

Photochemical transformation of perfluoroalkyl acid precursors in water using engineered nanomaterials

The production of perfluoroalkyl acids (PFAAs) has been phased out over recent decades; however, no significant decline in their environmental concentrations has been observed. This is partly due to the photochemical decomposition of PFAAs precursors (PrePFAAs) which remain in extensive use. The decomposition of PrePFAAs may be accelerated by the light-activated engineered nanomaterials (ENMs) in water. In light of this hypothesis, we investigated the photochemical transformation of three PrePFAAs, which are 8:2 fluorotelomer sulfonic acid (8:2 FTSA), 8:2 fluorotelomer alcohol (8:2 FTOH), and 2-(N-ethylperfluorooctane-1-sulfonamido ethyl] phosphate (SAmPAP), in the presence of six ENMs under simulated sunlight irradiation. The transformation rates of 8:2 FTSA and 8:2 FTOH were increased by 2–6 times when in the presence of six ENMs. However, most of ENMs appeared to inhibit the decomposition of SAmPAP. The transformation rates of PrePFAAs were found to depend on the yield of reactive oxygen species generated by ENMs, but the rates were also related to compound photo-stability, adsorption to surfaces, and photo-shielding effects. The PrePFAAs are transformed to perfluorooctanoic acid (PFOA) or/and perfluorooctane sulfonate (PFOS) with higher toxicity and longer half-life, PFOA or PFOS and a few PFAAs having shorter carbon chain lengths. Higher concentrations of the PFAAs photodegradation products were observed in the presence of most of the ENMs

Design of Power System Harmonic Measurement System Based on LabVIEW

Abstrac

Unsupervised Domain Adaptation GAN Inversion for Image Editing

Existing GAN inversion methods work brilliantly for high-quality image reconstruction and editing while struggling with finding the corresponding high-quality images for low-quality inputs. Therefore, recent works are directed toward leveraging the supervision of paired high-quality and low-quality images for inversion. However, these methods are infeasible in real-world scenarios and further hinder performance improvement. In this paper, we resolve this problem by introducing Unsupervised Domain Adaptation (UDA) into the Inversion process, namely UDA-Inversion, for both high-quality and low-quality image inversion and editing. Particularly, UDA-Inversion first regards the high-quality and low-quality images as the source domain and unlabeled target domain, respectively. Then, a discrepancy function is presented to measure the difference between two domains, after which we minimize the source error and the discrepancy between the distributions of two domains in the latent space to obtain accurate latent codes for low-quality images. Without direct supervision, constructive representations of high-quality images can be spontaneously learned and transformed into low-quality images based on unsupervised domain adaptation. Experimental results indicate that UDA-inversion is the first that achieves a comparable level of performance with supervised methods in low-quality images across multiple domain datasets. We hope this work provides a unique inspiration for latent embedding distributions in image process tasks