Leader-following consensus for lower-triangular nonlinear multi-agent systems with unknown controller and measurement sensitivities

summary:In this paper, a novel consensus algorithm is presented to handle with the leader-following consensus problem for lower-triangular nonlinear MASs (multi-agent systems) with unknown controller and measurement sensitivities under a given undirected topology. As distinguished from the existing results, the proposed consensus algorithm can tolerate to a relative wide range of controller and measurement sensitivities. We present some important matrix inequalities, especially a class of matrix inequalities with multiplicative noises. Based on these results and a dual-domination gain method, the output consensus error with unknown measurement noises can be used to construct the compensator for each follower directly. Then, a new distributed output feedback control is designed to enable the MASs to reach consensus in the presence of large controller perturbations. In view of a Lyapunov function, sufficient conditions are presented to guarantee that the states of the leader and followers can achieve consensus asymptotically. In the end, the proposed consensus algorithm is tested and verified by an illustrative example

Identification of hub genes associated with hepatitis B virus-related hepatocellular cancer using weighted gene co-expression network analysis and protein-protein interaction network analysis

Background. Chronic hepatitis B virus (HBV) infection is the main pathogen of hepatocellular carcinoma. However, the mechanisms of HBV-related hepatocellular carcinoma (HCC) progression are practically unknown. Materials and Methods. The results of RNA-sequence and clinical data for GSE121248 and GSE17548 were accessed from the Gene Expression Omnibus data library. We screened Sangerbox 3.0 for differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was employed to select core modules and hub genes, and protein-protein interaction network module analysis also played a significant part in it. Validation was performed using RNA-sequence data of cancer and normal tissues of HBV-related HCC patients in the cancer genome atlas-liver hepatocellular cancer database (TCGA-LIHC). Results. 787 DEGs were identified from GSE121248 and 772 DEGs were identified from GSE17548. WGCNA analysis indicated that black modules (99 genes) and grey modules (105 genes) were significantly associated with HBV-related HCC. Gene ontology analysis found that there is a direct correlation between DEGs and the regulation of cell movement and adhesion; the internal components and external packaging structure of plasma membrane; signaling receptor binding, calcium ion binding, etc. Kyoto Encyclopedia of Genes and Genomes pathway analysis found out the association between cytokine receptors, cytokine-cytokine receptor interactions, and viral protein interactions with cytokines were important and HBV-related HCC. Finally, we further validated 6 key genes including C7, EGR1, EGR3, FOS, FOSB, and prostaglandin-endoperoxide synthase 2 by using the TCGALIHC. Conclusions. We identified 6 hub genes as candidate biomarkers for HBV-related HCC. These hub genes may act as an essential part of HBV-related HCC progression

Computational Model and Numerical Simulation for Submerged Mooring Monitoring Platform’s Dynamical Response

Computational model and numerical simulation for submerged mooring monitoring platform were formulated aimed at the dynamical response by the action of flow force, which based on Hopkinson impact load theory, taken into account the catenoid effect of mooring cable and revised the difference of tension and tangential direction action force by equivalent modulus of elasticity. Solved the equation by hydraulics theory and structural mechanics theory of oceaneering, studied the response of buoy on flow force. The validity of model were checked and the results were in good agreement; the result show the buoy will engender biggish heave and swaying displacement, but the swaying displacement got stable quickly and the heaven displacement cause vibration for the vortex-induced action by the flow

Computational Model and Numerical Simulation for Submerged Mooring Monitoring Platform’s Dynamical Response

Computational model and numerical simulation for submerged mooring monitoring platform were formulated aimed at the dynamical response by the action of flow force, which based on Hopkinson impact load theory, taken into account the catenoid effect of mooring cable and revised the difference of tension and tangential direction action force by equivalent modulus of elasticity. Solved the equation by hydraulics theory and structural mechanics theory of oceaneering, studied the response of buoy on flow force. The validity of model were checked and the results were in good agreement; the result show the buoy will engender biggish heave and swaying displacement, but the swaying displacement got stable quickly and the heaven displacement cause vibration for the vortex-induced action by the flow

Static Deformation-Compensation Method Based on Inclination-Sensor Feedback for Large-Scale Manipulators with Hydraulic Actuation

Modern large-scale manipulators with hydraulic actuation like mobile concrete pump manipulators are increasingly used in industrial, construction, and other fields. Due to the large span of these manipulators, the static deformation accumulation to the endpoint has seriously affected the precise control of the endpoint. In this paper, we propose a static deformation-compensation method based on inclination sensor feedback for large-scale manipulators to reduce the deviation of the endpoint. Compared with the finite element method, this method does not need to consider many boundary conditions that are uncertain for flexible manipulators in most situations. It has appropriate accuracy and is universal for large-scale manipulators of different sizes and working under different loads. Based on a 24m-3R mobile concrete pump manipulator, the parametric simulation is carried out. The reliability of the static deformation-compensation method is verified, and the error is analyzed. The validity of the static deformation-compensation method is verified by comparing the theoretical endpoint position with the actual endpoint position after static deformation compensation. The compensation error under different loads is obtained, and the universality of the compensation method for different loads is verified

RESEARCH ON BOLT CONNECTION LOOSE MECHANISM UNDER DYNAMIC TENSION COMPRESSION AND SHEAR LOAD

Aiming at the loosening of bolt connection in the rotating mechanism under dynamic tension compression and shear load,the failure mechanism and its influencing factors are studied by using virtual prototyping technology and finite element analysis method. First,based on the virtual prototype technology,the load of failure bolt connection structure is extracted;Secondly,the finite element method is used to analyze the influence factors of bolt number,preload,thickness of the connecting piece and spring gasket. The evaluation index system of digital analysis is set up to guide the design of the measures for improving the anti loosening. In this paper,the design and research on the bolt of a servo device rotating mechanism is taken as an example. The method of digital design and analysis is carried out,which solves the problem of bolt connection loose. The conclusion shows that the improved design is consistent with the practical engineering,and bolt connection structure is free from loosening and failure

Ce-Doped LaMnO₃ Redox Catalysts for Chemical Looping Oxidative Dehydrogenation of Ethane

As a novel reaction mode of oxidative dehydrogenation of ethane to ethylene, the chemical looping oxidative dehydrogenation (CL-ODH) of ethane to ethylene has attracted much attention. Instead of using gaseous oxygen, CL-ODH uses lattice oxygen in an oxygen carrier or redox catalyst to facilitate the ODH reaction. In this paper, a perovskite type redox catalyst LaMnO3+δ was used as a substrate, Ce3+ with different proportions was introduced into its A site, and its CL-ODH reaction performance for ethane was studied. The results showed that the ratio of Mn4+/Mn3+ on the surface of Ce-modified samples decreased significantly, and the lattice oxygen species in the bulk phase increased; these were the main reasons for improving ethylene selectivity. La0.7Ce0.3MnO3 showed the best performance during the ODH reaction and showed good stability in twenty redox cycle tests

D³CNNs: Dual Denoiser Driven Convolutional Neural Networks for Mixed Noise Removal in Remotely Sensed Images

Mixed (random and stripe) noise will cause serious degradation of optical remotely sensed image quality, making it hard to analyze their contents. In order to remove such noise, various inverse problems are usually constructed with different priors, which can be solved by either model-based optimization methods or discriminative learning methods. However, they have their own drawbacks, such as the former methods are flexible but are time-consuming for the pursuit of good performance; while the later methods are fast but are limited for extensive applications due to their specialized tasks. To fast obtain pleasing results with combination of their merits, in this paper, we propose a novel denoising strategy, namely, Dual Denoiser Driven Convolutional Neural Networks (D3CNNs), to remove both random and stripe noise. The D3CNNs includes the following two key parts: one is that two auxiliary variables respective for the denoised image and the stripe noise are introduced to reformulate the inverse problem as a constrained optimization problem, which can be iteratively solved by employing the alternating direction method of multipliers (ADMM). The other is that the U-shape network is used for the denoised auxiliary variable while the residual CNN (RCNN) for the stripe auxiliary variable. The subjectively and objectively comparable results of experiments on both synthetic and real-world remotely sensed images verify that the proposed method is effective and is even better than the state-of-the-arts

MD³: Model-Driven Deep Remotely Sensed Image Denoising

Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods

The Protective Effect of Qishen Granule on Heart Failure after Myocardial Infarction through Regulation of Calcium Homeostasis

Qishen granule (QSG) is a frequently prescribed traditional Chinese medicine formula, which improves heart function in patients with heart failure (HF). However, the cardioprotective mechanisms of QSG have not been fully understood. The current study aimed to elucidate whether the effect of QSG is mediated by ameliorating cytoplasmic calcium (Ca2+) overload in cardiomyocytes. The HF rat model was induced by left anterior descending (LAD) artery ligation surgery. Rats were randomly divided into sham, model, QSG-low dosage (QSG-L) treatment, QSG-high dosage (QSG-H) treatment, and positive drug (diltiazem) treatment groups. 28 days after surgery, cardiac functions were assessed by echocardiography. Levels of norepinephrine (NE) and angiotensin II (AngII) in the plasma were evaluated. Expressions of critical proteins in the calcium signaling pathway, including cell membrane calcium channel CaV1.2, sarcoendoplasmic reticulum ATPase 2a (SERCA2a), calcium/calmodulin-dependent protein kinase type II (CaMKII), and protein phosphatase calcineurin (CaN), were measured by Western blotting (WB) and immunohistochemistry (IHC). Echocardiography showed that left ventricular ejection fraction (EF) and fractional shortening (FS) value significantly decreased in the model group compared to the sham group, and illustrating heart function was severely impaired. Furthermore, levels of NE and AngII in the plasma were dramatically increased. Expressions of CaV1.2, CaMKII, and CaN in the cardiomyocytes were upregulated, and expressions of SERCA2a were downregulated in the model group. After treatment with QSG, both EF and FS values were increased. QSG significantly reduced levels of NE and AngII in the plasma. In particular, QSG prevented cytoplasmic Ca2+ overload by downregulating expression of CaV1.2 and upregulating expression of SERCA2a. Meanwhile, expressions of CaMKII and CaN were inhibited by QSG treatment. In conclusion, QSG could effectively promote heart function in HF rats by restoring cardiac Ca2+ homeostasis. These findings revealed novel therapeutic mechanisms of QSG and provided potential targets in the treatment of HF