Feature Extraction and Pattern Identification for Anemometer Condition Diagnosis

Cup anemometers are commonly used for wind speed measurement in the wind industry. Anemometer malfunctions lead to excessive errors in measurement and directly influence the wind energy development for a proposed wind farm site. This paper is focused on feature extraction and pattern identification to solve the anemometer condition diagnosis problem of the PHM 2011 Data Challenge Competition. Since the accuracy of anemometers can be severely affected by the environmental factors such as icing and the tubular tower itself, in order to distinguish the cause due to anemometer failures from these factors, our methodologies start with eliminating irregular data (outliers) under the influence of environmental factors. For paired data, the relation between the relative wind speed difference and the wind direction is extracted as an important feature to reflect normal or abnormal behaviors of paired anemometers. Decisions regarding the condition of paired anemometers are made by comparing the features extracted from training and test data. For shear data, a power law model is fitted using the preprocessed and normalized data, and the sum of the squared residuals (SSR) is used to measure the health of an array of anemometers. Decisions are made by comparing the SSRs of training and test data. The performance of our proposed methods is evaluated through the competition website. As a final result, our team ranked the second place overall in both student and professional categories in this competition

The Research of Static Var Compensator's Time Characteristics and System-level Model of Controlled Current Source

AbstractIn the status of lacking research on response time of static var compensator (SVC), this paper established the controlled current source model which can achieve the same effect in response time and reactive compensation with the physical model of SVC by analyzing of characteristics in reactive power compensation and the response of the static var compensator (SVC) physical model. Through the time module in control signal of controlled current source, it can accurately calculate the response time of SVC. It tested the consistency of two models through the simulation of a rolling mill start experiment in PSCAD

Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image

Reconstructing interacting hands from a single RGB image is a very challenging task. On the one hand, severe mutual occlusion and similar local appearance between two hands confuse the extraction of visual features, resulting in the misalignment of estimated hand meshes and the image. On the other hand, there are complex spatial relationship between interacting hands, which significantly increases the solution space of hand poses and increases the difficulty of network learning. In this paper, we propose a decoupled iterative refinement framework to achieve pixel-alignment hand reconstruction while efficiently modeling the spatial relationship between hands. Specifically, we define two feature spaces with different characteristics, namely 2D visual feature space and 3D joint feature space. First, we obtain joint-wise features from the visual feature map and utilize a graph convolution network and a transformer to perform intra- and inter-hand information interaction in the 3D joint feature space, respectively. Then, we project the joint features with global information back into the 2D visual feature space in an obfuscation-free manner and utilize the 2D convolution for pixel-wise enhancement. By performing multiple alternate enhancements in the two feature spaces, our method can achieve an accurate and robust reconstruction of interacting hands. Our method outperforms all existing two-hand reconstruction methods by a large margin on the InterHand2.6M dataset.Comment: Accepted to ICCV 2023 (Oral

Expression of the microRNA-143/145 cluster is decreased in hepatitis B virus-associated hepatocellular carcinoma and may serve as a biomarker for tumorigenesis in patients with chronic hepatitis B

The aims of the present study were to identify the expression profile of microRNA (miR)‑143/145 in hepatitis B virus (HBV)‑associated hepatocellular carcinoma (HCC), explore its association with prognosis and investigate whether the serum miR‑143/145 expression levels may serve as a diagnostic indicator of HBV‑associated HCC. The microRNA (miRNA) chromatin immunoprecipitation dataset was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus databases, and analyzed using the Wilcoxon signed‑rank test. It was observed that the expression of miR‑143 and miR‑145 was decreased 1.5‑fold in HBV‑associated HCC samples compared with non‑tumor tissue in the TCGA and the GSE22058 datasets (P\u3c0.01). Using the reverse transcription‑quantitative polymerase chain reaction, it was further confirmed that miR‑143/145 and their host gene MIR143HG were downregulated in HBV‑associated HCC tissues compared with corresponding distal non‑tumor tissues. The lower level of miR‑143 and miR‑145 expression was associated with tumor differentiation, and may thus be responsible for a poor prognosis of patients with HBV‑associated HCC. The receiver‑operating characteristic (ROC) curves were used to explore the potential value of miR‑143 and miR‑145 as biomarkers for predicting HBV‑associated HCC tumorigenesis. In serum, miR‑143/145 were identified to be significantly decreased in patients with HBV‑associated HCC compared with negative control patients, and their associated areas under the ROC curves were calculated at 0.813 and 0.852 (P\u3c0.05), with each having a sensitivity and a specificity close to 0.80. These results indicated that the decreased expression of the miR‑143/145 cluster and their host gene MIR143HG in HBV‑associated HCC tissue was associated with prognosis, and each of these miRNAs may serve as a valuable diagnostic biomarker for predicting HBV‑associated HCC tumorigenesis

Classifying globular clusters and applying them to estimate the mass of the Milky Way

We combine the kinematics of 159 globular clusters (GCs) provided by the Gaia Early Data Release 3 (EDR3) with other observational data to classify the GCs, and to estimate the mass of the Milky Way (MW). We use the age-metallicity relation, integrals of motion, action space and the GC orbits to identify the GCs as either formed in-situ (Bulge and Disk) or ex situ (via accretion). We find that $45.3\%$ have formed in situ, $38.4\%$ may be related to known merger events: Gaia-Sausage-Enceladus, the Sagittarius dwarf galaxy, the Helmi streams, the Sequoia galaxy, and the Kraken galaxy. We also further identify three new sub-structures associated with the Gaia-Sausage-Enceladus. The remaining $16.3\%$ of GCs are unrelated to the known mergers and thought to be from small accretion events. We select 46 GCs which have radii $8.0<r<37.3$ kpc and obtain the anisotropy parameter $\beta=0.315_{-0.049}^{+0.055}$, which is lower than the recent result using the sample of GCs in Gaia Data Release 2, but still in agreement with it by considering the error bar. By using the same sample, we obtain the MW mass inside the outermost GC as $M(<37.3 kpc)=0.423_{-0.02}^{+0.02}\times10^{12}M_{\odot}$, and the corresponding $M_{200}=1.11_{-0.18}^{+0.25}\times10^{12}M_{\odot}$. The estimated mass is consistent with the results in many recent studies. We also find that the estimated $\beta$ and mass depend on the selected sample of GCs. However, it is difficult to determine whether a GC fully traces the potential of the MW.Comment: 32 pages, 11 figures, Accepted to RA