Application of improved support vector machine regression analysis for medium- and long-term vibration trend prediction

Aircraft engine fault diagnosis plays a crucial role in cost-effective operations of aircraft engines. However, successful detection of signals due to vibrations in multiple transmission channels is not always easy to accomplish, and traditional tests for nonlinearity are not always capable of capturing the dynamics. Here we applied a new method of smooth support vector machine regression (SSVMR) to better fit complicated dynamic systems. Since quadratic loss functions are less sensitive, the constrained quadratic optimization could be transferred to the unconstrained optimization so that the number of constraint conditions could be reduced. Meanwhile, the problem of slow operation speed and large memory space requirement associated with quadratic programming could be solved. Based on observed input and output data, the equivalent dynamic model of aircraft engineers was established, and model verification was done using historical vibration data. The results showed that SSVMR had fast operation speed and high predictive precision, and thus could be applied to provide early warning if engine vibration exceeds the required standard

Topological States in Twisted Pillared Phononic Plates

In recent years, the advances in topological insulator in the fields of condensed matter have been extended to classical wave systems such as acoustic and elastic waves. However, the quantitative robustness study of topological states which is indispensable in practical realization is rarely reported. In this work, we proposed topologically protected edge states with zigzag, bridge and armchair interfaces in a new twisted phononic plate. The robustness of non-trivial band gap in bulk structure is clearly presented versus twisted angles, revealing a threshold of 5 degrees which is the key fundamental information for the robustness of topological edge states. We further defined a localized displacement ratio as an efficient parameter to characterize edge states. Due to the different orientation of the three interfaces, zigzag and bridge edge states show higher quantitative robustness in their localized displacement ratio. A map of robustness as a function of both frequency and twisted angle highlights the better performance of the topological zigzag edge state. Robustness is evaluated for twisted angle and for all possible types of interfaces for the first time, which benefits for the design and fabrication of solid functional devices with great potential applications

Inverse design of topological metaplates for flexural waves with machine learning

The mechanical analog to the topological insulators brings anomalous elastic wave properties which diversifies classic wave functions for potential broad applications. To obtain topological mechanical wave states with good quality at desired frequency ranges, it needs repetitive trials of different geometric parameters with traditional forward designs. In this work, we develop an inverse design of topological edge states for flexural wave using machine learning method which is promising for instantaneous design. Nonlinear mapping function from input targets to output desired parameters are adopted in artificial neural networks where the data sets for training are generated by the plane wave expansion method. Topological edge states are then realized and compared for different bandgap width conditions with such inverse designs, proving that wide bandgap can promote the confinement of the topological edge states. Finally, direction selective propagations with sharp turns are further demonstrated as anomalous wave behaviors. The machine learning inverse design of topological states for flexural wave provides an efficient way to design practical devices with targeted needs for potential applications such as signal processing, sensing and energy harvesting

A sample-driven classification and identification method with KPCA and multi-SVM

It is difficult to develop an accurate fault diagnosis model for aero-engines due to their complex structure. In present paper, a new method for classifying and identifying fault modes according to the effective information extracted from history fault samples was proposed. This method includes three steps: firstly, find out independent source vibration signals through diagnosing and separating vibration signals. Secondly, find out the features that have great contribution to fault analysis using kernel principal component analysis (KPCA), and extract the eigenvector that is sensitive to status. Finally, classify the eigenvectors characterizing fault nature using support vector machine (SVM), meanwhile, select and analyze the parameters affecting classification effect. Two typical fault samples of aero-engine were used for verifying the feasibility of this method. Results show that for fault classification, this method has high identification accuracy, fast diagnosis speed, and is applicable to solving the classification and identification of small and nonlinear faults

Incremental learning-based visual tracking with weighted discriminative dictionaries

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out

The global burden of breast cancer in women from 1990 to 2030: assessment and projection based on the global burden of disease study 2019

Background and aimThis study aims to analyze the worldwide prevalence, mortality rates, and disability-adjusted life years (DALYs) attributed to breast cancer in women between 1990 and 2019. Additionally, it seeks to forecast the future trends of these indicators related to the burden of breast cancer in women from 2020 to 2030.MethodsData from the Global Burden of Disease Study (GBD) 2019 was analyzed to determine the age-standardized incidence rate (ASIR) and age-standardized death rate (ASDR) of DALYs due to breast cancer in women across 204 countries and territories from 1990 to 2019. Socio-economic development levels of countries and regions were assessed using Socio-demographic Indexes, and trends in the burden of breast cancer in women worldwide from 2020 to 2030 were projected using generalized additive models (GAMs).ResultsThe estimated annual percentage change (EAPC) in the ASIR breast cancer in women globally was 0.36 from 1990 to 2019 and is expected to increase to 0.44 from 2020 to 2030. In 2019, the ASIR of breast cancer in women worldwide was 45.86 and is projected to reach 48.09 by 2030. The burden of breast cancer in women generally rises with age, with the highest burden expected in the 45–49 age group from 2020 to 2030. The fastest increase in burden is anticipated in Central sub-Saharan Africa (EAPC in the age-standardized death rate: 1.62, EAPC in the age-standardized DALY rate: 1.52), with the Solomon Islands (EAPC in the ASIR: 7.25) and China (EAPC in the ASIR: 2.83) projected to experience significant increases. Furthermore, a strong positive correlation was found between the ASIR breast cancer in women globally in 1990 and the projected rates for 2030 (r = 0.62).ConclusionThe anticipated increase in the ASIR of breast cancer in women globally by 2030 highlights the importance of focusing on women aged 45–49 in Central sub-Saharan Africa, Oceania, the Solomon Islands, and China. Initiatives such as breast cancer information registries, raising awareness of risk factors and incidence, and implementing universal screening programs and diagnostic tests are essential in reducing the burden of breast cancer and its associated morbidity and mortality

Autotransplantation of Inferior Parathyroid glands during central neck dissection for papillary thyroid carcinoma: A retrospective cohort study

AbstractIntroduction: The management of inferior parathyroid glands during central neck dissection (CND) for papillary thyroid carcinoma (PTC) remains controversial. Most surgeons preserve inferior parathyroid glands in situ. Autotransplantation is not routinely performed unless devascularization or inadvertent parathyroidectomy occurs. This retrospective study aimed to compare the incidence of postoperative hypoparathyroidism and central neck lymph node (CNLN) recurrence in patients with PTC who underwent inferior parathyroid glands autotransplantation vs preservation in situ. Methods: This is a retrospective study which was conducted in a tertiary referral hospital. A total of 477 patients with PTC (pN1) who underwent total thyroidectomy (TT) and bilateral CND with/without lateral neck dissection were included. Patients' demographical characteristics, tumor stage, incidence of hypoparathyroidism, CNLN recurrence and the number of resected CNLN were analyzed. Results: Three hundred and twenty-one patients underwent inferior parathyroid glands autotransplantation (autotransplantation group). Inferior parathyroid glands were preserved in situ among 156 patients (preservation group). Permanent hypoparathyroidism rate was 0.9% (3/321) versus 3.8% (6/156) respectively (p = 0.028). Mean numbers of resected CNLN were 15 ± 3 (6–23) (autotransplantation group) versus 11 ± 3 (7–21) (preservation group) (p < 0.001). CNLN recurrence rate was 0.3% (1/321) versus 3.8% (6/156) respectively (p = 0.003). Conclusion: Inferior parathyroid glands autotransplantation during CND of PTC (pN1) might reduce permanent hypoparathyroidism and CNLN recurrence. Further study enrolling more patients with long-term follow-up is needed to support this conclusion