Adaptive Robust Actuator Fault Accommodation for a Class of Uncertain Nonlinear Systems with Unknown Control Gains

An adaptive robust fault tolerant control approach is proposed for a class of uncertain nonlinear systems with unknown signs of high-frequency gain and unmeasured states. In the recursive design, neural networks are employed to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaum gain functions are introduced to handle the unknown sign of the virtual control direction. By incorporating the switching function σ algorithm, the adaptive backstepping scheme developed in this paper does not require the real value of the actuator failure. It is mathematically proved that the proposed adaptive robust fault tolerant control approach can guarantee that all the signals of the closed-loop system are bounded, and the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples

Visualization Measurement of the Flame Temperature in a Power Station Using the Colorimetric Method

AbstractThis paper presents a study on the measurement of the temperature distribution in a power station based on an optical flame/temperature visualization system. This system operates upon the colorimetric principle combining advanced optical sensing and digital image processing techniques. The system was calibrated using a blackbody furnace as standard temperature source. Experimental results are obtained from a 300MW power station boiler. As the measurement height changed, the temperature captured by the system also changed. The maximum temperature occurs on the upper level of the burners. The temperature decreased when the load went down and tended to be stable when the load remained steady. Experimental results also reveal that this system is capable of online measurements of the temperature distribution in a combustion zone. This system can potentially be applied to many areas such as power generation, metallurgy or chemical engineering

Causality of particulate matter on cardiovascular diseases and cardiovascular biomarkers

BackgroundPrevious observational studies have shown that the prevalence of cardiovascular diseases (CVDs) is related to particulate matter (PM). However, given the methodological limitations of conventional observational research, it is difficult to identify causality conclusively. To explore the causality of PM on CVDs and cardiovascular biomarkers, we conducted a Mendelian randomization (MR) analysis.MethodIn this study, we obtained summary-level data for CVDs and cardiovascular biomarkers including atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), ischemic stroke (IS), stroke subtypes, body mass index (BMI), lipid traits, fasting glucose, fasting insulin, and blood pressure from several large genome-wide association studies (GWASs). Then we used two-sample MR to assess the causality of PM on CVDs and cardiovascular biomarkers, 16 single nucleotide polymorphisms (SNPs) for PM2.5 and 6 SNPs for PM10 were obtained from UK Biobank participants. Inverse variance weighting (IVW) analyses under the fixed effects model were used as the main analytical method to calculate MR Estimates, followed by multiple sensitivity analyses to confirm the robustness of the results.ResultsOur study revealed increases in PM2.5 concentration were significantly related to a higher risk of MI (odds ratio (OR), 2.578; 95% confidence interval (CI), 1.611–4.127; p = 7.920 × 10−5). Suggestive evidence was found between PM10 concentration and HF (OR, 2.015; 95% CI, 1.082–3.753; p = 0.027) and IS (OR, 2.279; 95% CI,1.099–4.723; p = 0.027). There was no evidence for an effect of PM concentration on other CVDs. Furthermore, PM2.5 concentration increases were significantly associated with increases in triglyceride (TG) (OR, 1.426; 95% CI, 1.133–1.795; p = 2.469 × 10−3) and decreases in high-density lipoprotein cholesterol (HDL-C) (OR, 0.779; 95% CI, 0.615–0.986; p = 0.038). The PM10 concentration increases were also closely related to the decreases in HDL-C (OR, 0.563; 95% CI, 0.366–0.865; p = 8.756 × 10−3). We observed no causal effect of PM on other cardiovascular biomarkers.ConclusionAt the genetic level, our study suggested the causality of PM2.5 on MI, TG, as well HDL-C, and revealed the causality of PM10 on HF, IS, and HDL-C. Our findings indicated the need for continued improvements in air pollution abatement for CVDs prevention

Descriptions and Barcoding of Five New Chinese Deuterophlebia Species Revealing This Genus in Both Holarctic and Oriental Realms (Diptera: Deuterophlebiidae)

The monotypic family Deuterophlebiidae of China was recorded twice previously from far northwest upon adults, the most parts of this country have not been investigated, leaving a huge blank of knowledge on their morphology, diversity, biology, or distribution. After deliberated collecting and rearing in recent years, we obtained more than one thousand specimens of Deuterophlebiidae, they are classified into five new species herein: Deuterophlebia sinensis sp. nov., D. yunnanensis sp. nov., D. wuyiensis sp. nov., D. acutirhina sp. nov. and D. alata sp. nov. Detailed descriptions and photographs of gathered life stages are given for these new species. Adults of them can be identified by chaetotaxy and length ratio of flagellomeres and legs, microtrichia on postgena and shape of their clypeus, pupae can be recognized by thoracic spines and abdominal chitin bands, and larvae can be separated by setae on thorax and abdomen. Genetic distances between species are 0.086–0.175 based on their COI genes. This contribution represents the first database of the enigmatic Deuterophlebiidae from China and shows a new distribution pattern of Deuterophlebia. In addition, the discovery throws some light on the origin and biogeography of the genus and family

Adversarial Fine-tuning for Backdoor Defense: Connecting Backdoor Attacks to Adversarial Attacks

Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, in this paper we find an intriguing connection between them: for a model planted with backdoors, we observe that its adversarial examples have similar behaviors as its triggered samples, i.e., both activate the same subset of DNN neurons. It indicates that planting a backdoor into a model will significantly affect the model's adversarial examples. Based on this observations, we design a new Adversarial Fine-Tuning (AFT) algorithm to defend against backdoor attacks. We empirically show that, against 5 state-of-the-art backdoor attacks, our AFT can effectively erase the backdoor triggers without obvious performance degradation on clean samples and significantly outperforms existing defense methods

Construction of Hierarchical CNT/rGO-Supported MnMoO₄ Nanosheets on Ni Foam for High-Performance Aqueous Hybrid Supercapacitors

Rationally designed conductive hierarchical nanostructures are highly desirable for supporting pseudocapacitive materials to achieve high-performance electrodes for supercapacitors. Herein, manganese molybdate nanosheets were hydrothermally grown with graphene oxide (GO) on three-dimensional nickel foam-supported carbon nanotube structures. Under the optimal graphene oxide concentration, the obtained carbon nanotubes/reduced graphene oxide/MnMoO₄ composites (CNT/rGO/MnMoO₄) as binder-free supercapacitor cathodes perform with a high specific capacitance of 2374.9 F g^–1 at the scan rate of 2 mV s^–1 and good long-term stability (97.1% of the initial specific capacitance can be maintained after 3000 charge/discharge cycles). The asymmetric device with CNT/rGO/MnMoO₄ as the cathode electrode and the carbon nanotubes/activated carbon on nickel foam (CNT-AC) as the anode electrode can deliver an energy density of 59.4 Wh kg^–1 at the power density of 1367.9 W kg^–1. These superior performances can be attributed to the synergistic effects from each component of the composite electrodes: highly pseudocapacitive MnMoO₄ nanosheets and three-dimensional conductive Ni foam/CNTs/rGO networks. These results suggest that the fabricated asymmetric supercapacitor can be a promising candidate for energy storage devices