Fault diagnosis of gearbox based on ant colony algorithm optimized support vector machine

The kernel function parameter g and penalty factor c in Support Vector Machine (SVM) will have an important impact on the fault classification and performance of the support vector machine. Based on this, a fault analysis and diagnosis model using ant colony algorithm to optimize support vector machine is proposed to improve the accuracy of gearbox fault diagnosis. First, the collected original vibration signal is decomposed by EEMD to obtain the modal function component IMF, and then the energy entropy of the IMF component is calculated as the feature vector of the original vibration signal. Finally, the feature vector is input to the support vector optimized by the ant colony algorithm identify and classify in the machine, and finally get the diagnosis result. Comparing ACO-SVM with SVM, the experimental results prove that the ACO-SVM model has a higher fault diagnosis rate, stronger optimization ability, and faster convergence speed.

Association Between Cadmium Exposure and Liver Function in Adults in the United States: A Cross-sectional Study

Objectives: Cadmium is widely used, leading to extensive environmental and occupational exposure. Unlike other organs, for which the harmful and carcinogenic effects of cadmium have been established, the hepatotoxicity of cadmium remains unclear. Some studies detected correlations between cadmium exposure and hepatotoxicity, but others concluded that they were not associated. Thus, we investigated the relationship between cadmium and liver damage in the general population.Methods: In total, 11 838 adult participants from National Health and Nutrition Examination Survey 1999-2015 were included. Urinary cadmium levels and the following liver function parameters were measured: alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), total bilirubin (TB), and alkaline phosphatase (ALP). Linear and logistic regression analyses were performed to assess the associations between urinary cadmium concentrations and each liver function parameter after adjusting for age, sex, race/ethnicity, annual family income, smoking status, alcohol consumption status, physical activity, and body mass index.Results: The covariate-adjusted results of the linear regression analyses showed significant positive relationships between log-transformed urinary cadmium levels and each log-transformed liver function parameter, where beta±standard error of ALT, AST, GGT, TB, and ALP were 0.049±0.008 (p<0.001), 0.030±0.006 (p<0.001), 0.093±0.011 (p<0.001), 0.034±0.009 (p<0.001), and 0.040±0.005 (p<0.001), respectively. Logistic regression also revealed statistically significant results. The odds ratios (95% confidence intervals) of elevated ALT, AST, GGT, TB, and ALP per unit increase in log-transformed urinary cadmium concentration were 1.360 (1.210 to 1.528), 1.307 (1.149 to 1.486), 1.520 (1.357 to 1.704), 1.201 (1.003 to 1.438), and 1.568 (1.277 to 1.926), respectively.Conclusions: Chronic exposure to cadmium showed positive associations with liver damage.

Methodology on Establishing Multivariate Failure Thresholds for Improved Remaining Useful Life Prediction in PHM

Prognostics and Health Management (PHM) methodologies and techniques have been much widely studied in the academia and practiced by the industry in recent years. Prognostic approaches commonly try to establish the relationship between Remaining Useful Life (RUL) and a single variable or health indicator (HI) which can be obtained from multi-sensor fusion or data-driven models. However, simply relying on a single variable could reduce RUL prediction robustness when it is less representative of the system health conditions. Taking multiple variables into consideration for RUL prediction, quantifying operating risks and determining multivariate failure threshold is essential yet rarely studied. Generally, there are three major challenges that limit the practicality of this topic. 1) How to determine the multivariate failure threshold? 2) How to quantify operation risks based on multiple variables? 3) How to make reliable extrapolations of future conditions? To address these questions, this paper proposes 1) a novel copula model to determine multivariate failure threshold, and 2) a Maximum Likelihood Estimation enhanced similarity-based Particle Filter (MLE-SMPF) to predict future system conditions. In the proposed methodology, the health assessment is firstly performed to obtain HI trajectory. The copula risk quantification model is then trained by two variables HI and life. The proposed copula model can easily include multiple variables compared with our previously published approach using bivariate Weibull Distribution[1]. Afterward, MLE-SMPF is used to extrapolate future HI for testing data. The prediction capability is further improved compared with [2] by introducing MLE for Particle Filter transition function parameter initialization. Finally, the system RUL is determined from the failure threshold which is obtained according to the quantified operation risk. The proposed methodology is validated on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The result outperforms most of the benchmarks from recent publications. The proposed methodology is easy to transfer to other potential machine prognostic applications.

Longitudinal Assessment of Health and Quality of Life of COVID-19 Patients Requiring Intensive Care—An Observational Study

Long-term health consequences in survivors of severe COVID-19 remain unclear. Eighteen COVID-19 patients admitted to the intensive care unit at the University Hospital Rechts der Isar, Munich, Germany, between 14 March and 23 June 2020, were prospectively followed-up at a median of 36, 75.5, 122 and 222 days after discharge. The health-related quality of life (HrQoL) (36-item Short Form Health Survey and St. George’s Respiratory Questionnaire, SGRQ), cardiopulmonary function, laboratory parameters and chest imaging were assessed longitudinally. The HrQoL assessment revealed a reduced physical functioning, as well as increased SGRQ impact and symptoms scores that all improved over time but remained markedly impaired compared to the reference groups. The median radiological severity scores significantly declined; persistent abnormalities were found in 33.3% of the patients on follow-up. A reduced diffusion capacity was the most common abnormal pulmonary function parameter. The length of hospitalization correlated with role limitations due to physical problems, the SGRQ symptom and the impact score. In conclusion, in survivors of severe COVID-19, the pulmonary function and symptoms improve over time, but impairments in their physical function and diffusion capacity can persist over months. Longer follow-up studies with larger cohorts will be necessary to comprehensively characterize long-term sequelae upon severe COVID-19 and to identify patients at risk.

Predicting the growth and feed intake of Bonsmara cattle on forage based rearing systems

This study aimed at estimating models to predict the growth and feed intake of Bonsmara bulls and heifers in backgrounding or pasture-based production systems (Ethical clearance number A16-SCI-AGR-001). Growth and intake data were collected from the Kromme Rhee Bonsmara stud in the Stellenbosch region. The growth curves of steers and heifers were modelled using the Gompertz function. Parameter estimates of the function showed that the mature weight (A) parameter was greater (P <0.05) for bulls than heifers (ca. 878.4 vs 562.1 kg, respectively). The maturation rates (parameter B) did not differ between the sexes, while the days at maximum growth (parameter C) was higher for bulls (291.5 days) than for heifers (182.4 days). Linear functions were used to describe the average feed intakes, as well as cumulative intakes, with body weights from growing bulls and heifers from the ages 6-20 months reared on lucerne hay and protein supplement. On average, it was observed that on the lucerne hay-based diet, Bonsmara bulls and heifers consumed about 2.43% of body weight daily throughout the study period. These models can be used in precision beef rearing systems to predict the production and market weights of Bonsmara calves that are either reared on hay or pasture.

Heating load prediction based on particle swarm optimization support vector machine

Heating load is affected by many uncertain factors, which makes it show certain randomness. To further improve the heating load forecasting accuracy, reduce the prediction error, using cross validation (CV) ideology in the choice of a model of performance evaluation and the superiority, combined with the advantages of particle swarm optimization (PSO), which is easy to implement and has stronger global optimization ability, the important parameters (penalty factor C and RBF kernel function parameter γ) are optimized, and the best parameters are automatically found in the training set, so as to obtain the best training model. Compared with other algorithms, the model precision of this method is improved a lot, and the prediction result is more accurate.

Monte Carlo Simulation in Quantile Regression Parameter for Sparsity Estimate

Monte Carlo is a method used to generate data according to the distribution and resampling until the parameters of the method used became convergen. The purpose of this simulation is first to prove that quantile regression with the estimated sparsity function parameter can model the data according to the non-uniform distribution of the data. Secondly, it’s to prove that the quantile regression is a developed method from the linear regression. The pattern of data which is not uniform is generally referred to as heterogeneous data, while the pattern of uniform data distribution is called homogeneous data. Data in this study will be generated for small and large samples on homogeneous and heterogeneous data. Uniformity of variance will be carried out on both heterogeneous and homogeneous data types, namely 0.25,1 and 4. The parameter estimation process and data generation will be resampled 1000 times. Thus, in conclusion of the simulation studies was the parameter estimates in the classical regression will be the same as the parameter estimates in the quantile regression at quantile 0.5. In the simulation, it is decided that the quantile regression method can be used on heterogeneous and homogeneous data to the unconstrained number of samples and variances.

The Feasibility and Efficiency of Remote Spirometry System on the Pulmonary Function for Multiple Ribs Fracture Patients

Background: Multiple rib fractures is a common chest trauma with a significant and sustained impact on pulmonary function and quality of life. Continuous monitoring of the pulmonary function parameter was necessary to adjust the therapeutic goals in these patients. We developed an internet-based remote system for lung function monitoring with a remote spirometry and smart device application to follow up these patients consecutively. Method: From Jan 2021 to April 2021, we conducted a prospective study that applied an intelligent spirometry system for patients with multiple rib fractures. With informed consent, we collected clinical data from them and introduced the remote spirometry system. We followed up with these patients for 12 weeks after trauma and compared the recovery of pulmonary function parameters and clinical outcomes. Result: A total of 21 patients were enrolled in our study. We divided them into two groups by the compliance to this remote spirometry system. The improvement of forced vital capacitywas better in the good compliance group than the poor compliance group (110% versus 21%, p value 0.049). Moreover, the complication rate was also lower in the good compliance group than the poor compliance group (10% versus 66.7% p value 0.017). Conclusion: Remote spirometry system is a novel system that can help in lung rehabilitation in patients with multiple rib fractures. Patients that cooperate well with this system presented superior lung function improvement and inferior complication rate.

The Quasi-Optimal Radial Basis Function Collocation Method: A Technical Note

The traditional radial basis function parameter controls the flatness of these functions and influences the precision and stability of approximation solution. The coupled radial basis function, which is based on the infinitely smooth radial basis functions and the conical spline, achieves an accurate and stable numerical solution, while the shape parameter values are almost independent. In this paper, we give a quasi-optimal conical spline which can improve the numerical results. Besides, we consider the collocation points in the Chebyshev-type which improves solution accuracy of the method with no additional computational cost.

Robust Multi-Frame Super-Resolution Based on Adaptive Half-Quadratic Function and Local Structure Tensor Weighted BTV

It is difficult to improve image resolution in hardware due to the limitations of technology and too high costs, but most application fields need high resolution images, so super-resolution technology has been produced. This paper mainly uses information redundancy to realize multi-frame super-resolution. In recent years, many researchers have proposed a variety of multi-frame super-resolution methods, but it is very difficult to preserve the image edge and texture details and remove the influence of noise effectively in practical applications. In this paper, a minimum variance method is proposed to select the low resolution images with appropriate quality quickly for super-resolution. The half-quadratic function is used as the loss function to minimize the observation error between the estimated high resolution image and low-resolution images. The function parameter is determined adaptively according to observation errors of each low-resolution image. The combination of a local structure tensor and Bilateral Total Variation (BTV) as image prior knowledge preserves the details of the image and suppresses the noise simultaneously. The experimental results on synthetic data and real data show that our proposed method can better preserve the details of the image than the existing methods.