Quantum Information Approach to Rotating Bose-Einstein Condensate

We investigate the 2D weakly interacting Bose-Einstein condensate in a rotating trap by the tools of quantum information theory. The critical exponents of the ground state fidelity susceptibility and the correlation length of the system are obtained for the quantum phase transition when the frst vortex is formed. We also find the single-particle entanglement can be an indicator of the angular momentums for some real ground states. The single-particle entanglement of fractional quantum Hall states such as Laughlin state and Pfaffian state is also studied.Comment: 4 pages, 6 figures, minimal changes are mad

Ultrasound-targeted microbubble destruction enhances AAV mediated gene transfection: human RPE cells in vitro and the rat retina in vivo

The present study was performed to investigate the efficacy and safety of Ultrasound-targeted microbubble destruction (UTMD) mediated rAAV2-EGFP to cultured human retinal pigment epithelium (RPE) cells _in vitro_ and the rat retina _in vivo_. _In vitro_ study, cultured human RPE cells were exposed to US under different conditions with or without microbubbles. Furthermore, the effect of UTMD to rAAV2-EGFP itself and the cells were evaluated. _In vivo_ study, gene transfer was examined by injecting rAAV2-EGFP into the subretinal space of the rats with or without microbubbles and then exposed to US. We investigated EGFP expression _in vivo_ via stereomicroscopy and performed quantitative analysis by Axiovision 3.1 software. HE staining and frozen sections were used to observe tissue damage and location of EGFP gene expression. _In vitro_ study, the transfection efficiency of rAAV2-EGFP increased 74.85% under the optimal UTMD conditions. Furthermore, there was almost no cytotoxicity to the cells and rAAV2-EGFP itself. _In vivo_ study, UTMD could be used safely to enhance and accelerate transgene expression of the retina. Fluorescence expression was mainly located in the layer of retina. UTMD is a promising method for gene delivery to the retina

Fault diagnosis using an improved fusion feature based on manifold learning for wind turbine transmission system

In this paper, a novel fault diagnosis method based on vibration signal analysis is proposed for fault diagnosis of bearings and gears. Firstly, the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal into several subsequences, and a multi-entropy (ME) is proposed to make up the fusion features of the vibration signal. Secondly, an improved manifold learning algorithm, local and global preserving embedding (LGPE), is applied to compress the high-dimensional fusion feature set into a two-dimension feature set. Finally, according to the clustering accuracy of different feature set, the fault classification and diagnosis can be performed in the reduced two-dimension space. The performance of the proposed technique is tested on the fault of wind turbine transmission system. The application results indicate that the proposed method can achieve high accuracy of fault diagnosis

Quantum Information Approach to Bose-Einstein Condensate in a Tilted Double-Well System

We study the ground state properties of bosons in a tilted double-well system. We use fidelity susceptibility to identify the possible ground state transitions under different tilt values. For a very small tilt (for example $10^{-10}$), two transitions are found. For a moderate tilt (for example $10^{-3}$), only one transition is found. For a large tilt (for example $10^{-1}$), no transition is found. We explain this by analyzing the spectrum of the ground state. The quantum discord and total correlation of the ground state under different tilts are also calculated to indicate those transitions. In the transition region, both quantities have peaks decaying exponentially with particle number $N$. This means for a finite-size system the transition region cannot be explained by the mean-field theory, but in the large-$N$ limit it can be.Comment: 5 pages, 5 figures, slightly different from the published versio

Semi-active vibration control of the motorized spindle using a self-powered SSDV technique: simulation and experimental study

SSD (synchronized switch damping) is used for vibration control of the motorized spindle based on piezoelectric stack. Moreover, inspired by self-powered SSDI, a self-powered SSDV circuit was designed to overcome the disadvantages of requiring readjusting control parameters and sensor re-positioning of SSDI (synchronized switch damping on inductor) and SSDV (synchronized switch damping on voltage source). A simulation and an experimental were built, and the results show vibration control performance of self-powered SSDV is better than self-powered SSDI and is more flexible and effective than self-powered SSDI by adjusting the DC voltage to adapt to different speeds of the motorized spindle

CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The PLM does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Comprehensive experiments on two benchmark datasets, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art in terms of all metrics in the task of OWOD, incremental object detection (IOD) and open-set detection.Comment: CVPR 2023 camera-ready versio

Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

In order to enhance the capability of feature extraction and fault classification of bearings, this study proposes a feature extraction approach based on dual-tree complex wavelet transform (DTCWT) and permutation entropy (PE), using the fuzzy c means clustering (FCM) to identify fault types. The vibration signal of bearings can be decomposed into several wavelet components with DTCWT which can describe the local characteristics of vibration signals accurately. And the PE of each wavelet component, which can describe the complexity of a time series, is calculated to be regarded as the fault features. Then forming the standard clustering centers by the FCM, we defined a standard using the Hamming approach degree to evaluate the classification results in the FCM. In order to verify the effectiveness of the proposed approach, compared with two other typical signal analysis methods: ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD), through extracting fault features, it required to identify the fault types and severities under variable operating conditions. The experimental results demonstrate that the proposed approach has a better accuracy and performance to diagnose a bearing fault under different fault severities and variable operating conditions. The proposed approach is suitable for a fault diagnosis due to its good ability to the feature extraction and fault classification