Imaging Ferroelectric Domains via Charge Gradient Microscopy Enhanced by Principal Component Analysis

Local domain structures of ferroelectrics have been studied extensively using various modes of scanning probes at the nanoscale, including piezoresponse force microscopy (PFM) and Kelvin probe force microscopy (KPFM), though none of these techniques measure the polarization directly, and the fast formation kinetics of domains and screening charges cannot be captured by these quasi-static measurements. In this study, we used charge gradient microscopy (CGM) to image ferroelectric domains of lithium niobate based on current measured during fast scanning, and applied principal component analysis (PCA) to enhance the signal-to-noise ratio of noisy raw data. We found that the CGM signal increases linearly with the scan speed while decreases with the temperature under power-law, consistent with proposed imaging mechanisms of scraping and refilling of surface charges within domains, and polarization change across domain wall. We then, based on CGM mappings, estimated the spontaneous polarization and the density of surface charges with order of magnitude agreement with literature data. The study demonstrates that PCA is a powerful method in imaging analysis of scanning probe microscopy (SPM), with which quantitative analysis of noisy raw data becomes possible

RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos

Obtaining the ground truth labels from a video is challenging since the manual annotation of pixel-wise flow labels is prohibitively expensive and laborious. Besides, existing approaches try to adapt the trained model on synthetic datasets to authentic videos, which inevitably suffers from domain discrepancy and hinders the performance for real-world applications. To solve these problems, we propose RealFlow, an Expectation-Maximization based framework that can create large-scale optical flow datasets directly from any unlabeled realistic videos. Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow. Thus the new image pairs and their corresponding flows can be regarded as a new training set. Besides, we design a Realistic Image Pair Rendering (RIPR) module that adopts softmax splatting and bi-directional hole filling techniques to alleviate the artifacts of the image synthesis. In the E-step, RIPR renders new images to create a large quantity of training data. In the M-step, we utilize the generated training data to train an optical flow network, which can be used to estimate optical flows in the next E-step. During the iterative learning steps, the capability of the flow network is gradually improved, so is the accuracy of the flow, as well as the quality of the synthesized dataset. Experimental results show that RealFlow outperforms previous dataset generation methods by a considerably large margin. Moreover, based on the generated dataset, our approach achieves state-of-the-art performance on two standard benchmarks compared with both supervised and unsupervised optical flow methods. Our code and dataset are available at https://github.com/megvii-research/RealFlowComment: ECCV 2022 Ora

Fault diagnosis of refrigerant charge based on PCA and decision tree for variable refrigerant flow systems

Variable refrigerant flow (VRF) systems are easily subjected to performance degradation due to refrigerant leakage, mechanical failure or improper maintenance after years of operation. Ideal VRF systems should equip with fault detection and diagnosis (FDD) program to sustain its normal operation. This paper presents the fault diagnosis method for refrigerant charge faults of variable refrigerant flow (VRF) systems. It is developed based on the principal component analysis (PCA) feature extraction method and the decision tree (DT) classification algorithm. Nine refrigerant charge schemes are implemented on the VRF system in the laboratory, which contain the normal and faulty refrigerant charge conditions. In addition, data of the online operating VRF systems are collected in this work. Firstly, data from both experimental VRF system and online operating systems are pre-processed by outlier cleaning, feature extraction and data normalization, because the original data of the VRF system usually has poor quality and complex structure. Secondly, the fault diagnosis model based on the PCA-DT method is built using the data of the experimental VRF system. In this step, the PCA method is used to obtain a new data sample which includes four comprehensive features, then the new data sample are randomly split into training and testing sets as the input of DT classifier for fault diagnosis. Thirdly, the advantages of the PCA-DT method is validated using the experimental data of different fault severity levels. Results show that the combined use of PCA and DT methods can achieve better fault diagnosis efficiency than the single decision tree method. Further, the robustness of the PCA-DT method in online fault diagnosis is verified using the data from online VRF systems. The online VRF systems have the same or different number of indoor units as the trained (experimental) VRF system. The PCA-DT method also shows desirable goodness on the online fault diagnosis process. In this sense, this work provides a promising fault diagnosis strategy for refrigerant charge faults of VRF system application

Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching

Correlation based stereo matching has achieved outstanding performance, which pursues cost volume between two feature maps. Unfortunately, current methods with a fixed model do not work uniformly well across various datasets, greatly limiting their real-world applicability. To tackle this issue, this paper proposes a new perspective to dynamically calculate correlation for robust stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios. Specifically, a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation. Additionally, we improve the traditional non-parametric warping with learnable parameters, such that the position-specific weights can be learned. We show that by empowering the recurrent network with the UGAC module, stereo matching can be exploited more robustly and effectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury datasets when employing the same fixed model over these datasets without any retraining procedure. To target real-time applications, we further design a lightweight model based on UGAC, which also outperforms other methods over KITTI benchmarks with only 0.6 M parameters.Comment: Accepted by ICCV202

Atomic-Scale Tracking Phase Transition Dynamics of Berezinskii-Kosterlitz-Thouless Polar Vortex-Antivortex

Particle-like topologies, such as vortex-antivortex (V-AV) pairs, have garnered significant attention in the field of condensed matter. However, the detailed phase transition dynamics of V-AV pairs, as exemplified by self-annihilation, motion, and dissociation, have yet to be verified in real space due to the lack of suitable experimental techniques. Here, we employ polar V-AV pairs as a model system and track their transition pathways at atomic resolution with the aid of in situ (scanning) transmission electron microscopy and phase field simulations. We demonstrate the absence of a Berezinskii-Kosterlitz-Thouless phase transition between the room-temperature quasi-long-range ordered ground phase and the high-temperature disordered phase. Instead, we observe polarization suppression in bound V-AV pairs as the temperature increases. Furthermore, electric fields can promote the vortex and antivortex to approach each other and annihilate near the interface. The elucidated intermediate dynamic behaviors of polar V-AV pairs under thermal- and electrical-fields lay the foundation for their potential applications in electronic devices. Moreover, the dynamic behaviors revealed at atomic scale provide us new insights into understanding topological phase of matter and their topological phase transitions.Comment: 19 pages and 4 figure

Enhancement of Local Piezoresponse in Polymer Ferroelectrics via Nanoscale Control of Microstructure

Polymer ferroelectrics are flexible and lightweight electromechanical materials that are widely studied due to their potential application as sensors, actuators, and energy harvesters. However, one of the biggest challenges is their low piezoelectric coefficient. Here, we report a mechanical annealing effect based on local pressure induced by a nanoscale tip that enhances the local piezoresponse. This process can control the nanoscale material properties over a microscale area at room temperature. We attribute this improvement to the formation and growth of β-phase extended chain crystals via sliding diffusion and crystal alignment along the scan axis under high mechanical stress. We believe that this technique can be useful for local enhancement of piezoresponse in ferroelectric polymer thin films