Evaluation of Wind Turbine Operation Status Based on ACO + FAHP

Aiming at the shortcomings of the fuzzy analytic hierarchy process (FAHP) in the comprehensive evaluation of wind power projects, such as the diffi culty of satisfying and modifying the consistency of the judgment matrix and the high computational complexity, a fuzzy analytic hierarchy process based on ant colony optimization (ACO+FAHP) is proposed. Firstly, the proposed fuzzy analytic hierarchy process based on ant colony optimization algorithm overcomes the disadvantages that the weight and consistency cannot be improved once the judgment matrix is given. The comparison chart of the consistency ratio calculated according to this method shows that the consistency ratio B, C1-C5 all have diff erent degrees of reduction. Then, in view of the fact that various qualitative indicators cannot be accurately calculated, the wind turbine operating status evaluation model is established by using the fuzzy comprehensive evaluation method. In this paper, the evaluation score of a certain wind farm is 0.731, which means that the operators need to carry out high-level maintenance at this time

BiHRNet: A Binary high-resolution network for Human Pose Estimation

Human Pose Estimation (HPE) plays a crucial role in computer vision applications. However, it is difficult to deploy state-of-the-art models on resouce-limited devices due to the high computational costs of the networks. In this work, a binary human pose estimator named BiHRNet(Binary HRNet) is proposed, whose weights and activations are expressed as $\pm$1. BiHRNet retains the keypoint extraction ability of HRNet, while using fewer computing resources by adapting binary neural network (BNN). In order to reduce the accuracy drop caused by network binarization, two categories of techniques are proposed in this work. For optimizing the training process for binary pose estimator, we propose a new loss function combining KL divergence loss with AWing loss, which makes the binary network obtain more comprehensive output distribution from its real-valued counterpart to reduce information loss caused by binarization. For designing more binarization-friendly structures, we propose a new information reconstruction bottleneck called IR Bottleneck to retain more information in the initial stage of the network. In addition, we also propose a multi-scale basic block called MS-Block for information retention. Our work has less computation cost with few precision drop. Experimental results demonstrate that BiHRNet achieves a PCKh of 87.9 on the MPII dataset, which outperforms all binary pose estimation networks. On the challenging of COCO dataset, the proposed method enables the binary neural network to achieve 70.8 mAP, which is better than most tested lightweight full-precision networks.Comment: 12 pages, 6 figure

Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder

Generative Adversarial Network (GAN) based vocoders are superior in inference speed and synthesis quality when reconstructing an audible waveform from an acoustic representation. This study focuses on improving the discriminator to promote GAN-based vocoders. Most existing time-frequency-representation-based discriminators are rooted in Short-Time Fourier Transform (STFT), whose time-frequency resolution in a spectrogram is fixed, making it incompatible with signals like singing voices that require flexible attention for different frequency bands. Motivated by that, our study utilizes the Constant-Q Transform (CQT), which owns dynamic resolution among frequencies, contributing to a better modeling ability in pitch accuracy and harmonic tracking. Specifically, we propose a Multi-Scale Sub-Band CQT (MS-SB-CQT) Discriminator, which operates on the CQT spectrogram at multiple scales and performs sub-band processing according to different octaves. Experiments conducted on both speech and singing voices confirm the effectiveness of our proposed method. Moreover, we also verified that the CQT-based and the STFT-based discriminators could be complementary under joint training. Specifically, enhanced by the proposed MS-SB-CQT and the existing MS-STFT Discriminators, the MOS of HiFi-GAN can be boosted from 3.27 to 3.87 for seen singers and from 3.40 to 3.78 for unseen singers

Zoom Out and Observe: News Environment Perception for Fake News Detection

Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we "zoom out" to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors.Comment: ACL 2022 Main Conference (Long Paper

Response characteristics of root to moisture change at seedling stage of Kengyilia hirsuta

Kengyilia hirsuta is an important pioneer plant distributed on the desertified grassland of the Qinghai-Tibet Plateau. It has strong adaptability to alpine desert habitats, so it can be used as a sand-fixing plant on sandy alpine land. To study the response mechanisms of root morphological and physiological characteristics of K. hirsuta to sandy soil moisture, 10%, 25% and 40% moisture levels were set up through potted weighing water control method. The biomass, root-shoot ratio, root architecture parameters, and biochemical parameters malondialdehyde, free proline, soluble protein, indole-3-acetic acid, abscisic acid, cytokinin, gibberellin, relative conductivity and antioxidant enzyme activities were measured in the trefoil stage, and the response mechanisms of roots at different moisture levels were analyzed. The results showed that with the increase of soil moisture, root morphological indexes such as root biomass, total root length, total root volume and total root surface increased, while the root topological index decreased continuously. The malondialdehyde content, relative conductivity, superoxide dismutase activity, peroxidase activity, catalase activity, free proline content, soluble protein content, abscisic acid content and cytokinin content at the 25% and 40% moisture levels were significantly decreased compared with the 10% level (P< 0.05). Thus, the root growth of K. hirsuta was restricted by the 10% moisture level, but supported by the 25% and 40% moisture levels. An artificial neural network revealed that total root length, total root surface area, root link average length, relative conductivity, soluble protein, free proline and moisture level were the key factors affecting root development. These research results could contribute to future agricultural sustainability

The clinical implications of fasting serum insulin levels in patients with insulin-treated type 2 diabetes: a cross-sectional survey

ObjectiveThis study aimed to investigate the clinical implications of fasting serum insulin (FINS) levels in subjects with type 2 diabetes who were receiving insulin therapy.MethodsA total of 1,553 subjects with type 2 diabetes [774 subjects who had never received insulin treatment (N-INS) and 779 subjects who were receiving insulin therapy (constant insulin treatment, C-INS)] admitted to the Department of Endocrinology and Metabolism of Peking University People’s Hospital were enrolled in this study. Their FINS levels were measured and those with hyperinsulinemia were identified. The underlying mechanisms of hyperinsulinemia were revealed by measuring insulin antibodies (IAs) and analyzing changes in FINS levels before and after polyethylene glycol (PEG) precipitation. In addition, the clinical characteristics of patients with different types of hyperinsulinemia were compared.ResultsHigher FINS levels and a higher incidence (43.8%, 341/779) of hyperinsulinemia (FINS > 15μIU/mL) were observed in subjects with C-INS than in subjects with N-INS. Among subjects with C-INS and hyperinsulinemia, 66.9% (228/341) were IAs positive, and the incidence of IAs was found to be positively associated with FINS level. By performing PEG precipitation, we found that all subjects without IAs (i.e., those with real hyperinsulinemia) and 31.1% of subjects (71/228) with IAs (i.e., those with both real and IAs-related hyperinsulinemia) still had hyperinsulinemia after PEG precipitation, whereas FINS levels in the other 68.9% of subjects (157/228) with IAs were normal (IAs-related hyperinsulinemia) after PEG precipitation. Comparisons between the groups showed that subjects with real hyperinsulinemia showed more obvious insulin resistance characteristics, including higher lipid levels, BMIs, and homoeostasis model assessment2-estimated insulin resistance (HOMA2-IR) index, and were more likely to have hypertension, obesity, and metabolic syndromes (p < 0.05). However, the risk of hypoglycemia and glucose variability increased significantly in subjects with IAs compared with those without IAs. A cutoff of FINS to serum C-peptide ratio (≥ 9.3μIU/ng) could be used to screen IAs in clinical practice with 83.3% sensitivity and 70% specificity.ConclusionsIt is necessary to measure FINS in subjects with C-INS to distinguish between types of hyperinsulinemia, which should help to tailor treatment regimens