Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient

We propose a novel mathematical algorithm to offer a solution for the inverse random dynamic force identification in practical engineering. Dealing with the random dynamic force identification problem using the proposed algorithm, an improved maximum entropy (IME) regularization technique is transformed into an unconstrained optimization problem, and a novel conjugate gradient (NCG) method was applied to solve the objective function, which was abbreviated as IME-NCG algorithm. The result of IME-NCG algorithm is compared with that of ME, ME-CG, ME-NCG, and IME-CG algorithm; it is found that IME-NCG algorithm is available for identifying the random dynamic force due to smaller root mean-square-error (RMSE), lower restoration time, and fewer iterative steps. Example of engineering application shows that L-curve method is introduced which is better than Generalized Cross Validation (GCV) method and is applied to select regularization parameter; thus the proposed algorithm can be helpful to alleviate the ill-conditioned problem in identification of dynamic force and to acquire an optimal solution of inverse problem in practical engineering

Causal relationship between Butyricimonas and allergic asthma: a two-sample Mendelian randomization study

BackgroundGrowing evidence has well documented the close association between the gut microbiome and allergic respiratory disease, which has been notably represented by allergic asthma. However, it is unclear whether this association is a causal link. Therefore, we investigated the potential causal associations between the gut microbiome and allergic asthma or other allergic diseases.MethodsIn this study, we performed two-sample Mendelian randomization (MR) analyses by using the publicly available genome-wide association study (GWAS) summary data. Single-nucleotide polymorphisms (SNPs) that significantly correlated were selected as instrumental variables. The inverse variance weighted (IVW) method was used to examine the potential causal gut microbial genera for allergic asthma and other allergic diseases. The robustness of the primary findings of the MR analyses was ensured by using different sensitivity analyses.ResultsCombining the findings from multiple analyses, the host genetic-driven increases in Butyricimonas at the genus level were positively correlated with the risk of allergic asthma. In addition, phylum Bacteroidetes and class Bacteroidia were also found to have negative associations with the risk of allergic asthma; genus Slackia was identified as having potential causal effects with allergic asthma. No clear evidence of pleiotropy and heterogeneity was observed in genus Butyricimonas. Butyricimonas was also found to have an association with allergic rhinitis, but not with other allergic diseases.ConclusionOur findings indicate that there are new gut microbial genera that were causally associated with the risk of allergic asthma and other allergic diseases, and offer novel insights into the pathogenesis of allergic respiratory diseases

Visualized Experiment of Bubble Behaviors in a Vertical Narrow Rectangular Channel Under Natural Circulation Condition

The characteristics of bubble behavior have been in particular interest for decades due to its significant contribution to understanding the mechanism of heat transfer. In the present work, visualized experiment is conducted to study the bubble characteristics in subcooled flow boiling of a narrow rectangular channel under natural circulation. The experiments were performed at pressures of 0.2 MPa, with inlet subcooling ranging from 20 to 60 K and heat flux ranging from 100 to 300 kW/m2. A high-speed digital camera is used to capture the pictures of bubble behaviors. A sequence of image processing algorithms is used deal with the original bubble images to get relevant bubble parameters. We observe the whole process of a single sliding bubble lifetime and found most of bubbles slide along the heating surface after detaching the nucleation sites. Five typical sliding bubble growth paths are observed in the present experimental conditions. According to the analysis of the experimental data, it can be found that the liquid subcooling and wall superheat are the main factors that affect the bubble size during sliding in narrow rectangular channel under natural circulation condition. Due to the difference of driving force, the sliding velocity of bubble in forced circulation is always larger than that in natural circulation. At the same time, the bubble velocity changes significantly at different heat flux and shooting location

Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator

Aiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted to reconstruct the input coal-rock infrared images, so as to reduce the noise interference, and then, the reconstructed image is transformed by gray level. Finally, we consider the frequency characteristics and long memory properties of fractional differential, the classical first-order Sobel and second-order Laplacian edge detection algorithms are extended to fractional order pattern, and a new pattern of fractional order differential image edge detection is constructed to realize the coal-rock fracture edge features identification. The results show that, compared with integer order differential, the error rate and omission rate of fractional order differential algorithm are smaller, the quality factor is larger, and the execution time and memory footprint are smaller. From the point of view of location criteria and location accuracy, the fractional order differential algorithm is better than the integer order. In addition, the proposed method is compared with Canny algorithm, B-spline wavelet transform, and multidirection fuzzy morphological edge detection method, can detect more coal-rock fracture infrared image edge details, and is more robust to noise

A Novel Improved Maximum Entropy Regularization Technique and Application to Identification of Dynamic Loads on the Coal Rock

A new signal processing algorithm was proposed to identify the dynamic load acting on the coal-rock structure. First, the identification model for dynamic load is established through the relationship between the uncertain load vector, and the assembly matrix of the responses was measured by the machinery dynamic system. Then, the entropy item of maximum entropy regularization (MER) is redesigned using the robust estimation method, and the elongated penalty function according to the ill-posedness characteristics of load identification, which was named as a novel improved maximum entropy regularization (IMER) technique, was proposed to process the dynamic load signals. Finally, the load identification problem is transformed into an unconstrained optimization problem and an improved Newton iteration algorithm was proposed to solve the objective function. The result of IMER technique is compared with MER technique, and it is found that IMER technique is available for analyzing the dynamic load signals due to higher signal-noise ratio, lower restoration time, and fewer iterative steps. Experiments were performed to investigate the effect on the performance of dynamic load signals identification by different regularization parameters and calculation parameters, pi, respectively. Experimental results show that the identified dynamic load signals are closed to the actual load signals using IMER technique combined with the proposed PSO-L regularization parameter selection method. Selecting optimal calculated parameters pi is helpful to overcome the ill-condition of dynamic load signals identification and to obtain the stable and approximate solutions of inverse problems in practical engineering. Meanwhile, the proposed IMER technique can also play a guiding role for the coal-rock interface identification

Load Identification Method Based on Interval Analysis and Tikhonov Regularization and Its Application

In order to study the dynamic force identification method of an end-plate pick of shearer, a dynamic force identification technique based on interval theory was proposed. The dynamic force interval identification model is established by describing and quantifying the identified parameters. By using the interval analysis method of the first-order Taylor expansion, the dynamic force identification is transformed into two kinds of deterministic inverse problems at the midpoint of the uncertain parameter and its gradient identification. The Tikhonov regularization method is used to solve two kinds of deterministic problems, and the upper and lower boundaries of dynamic force of the end-plate pick are determined. The results show that the deviations between the identified dynamic force and the actual dynamic force are basically within 2% and 5%, and the average uncertainties are up to 7% and 10%. Therefore, the proposed method can effectively determine the upper and lower boundaries of dynamic force of the end-plate pick, improve the solving efficiency, and provide a new research method for studying the coal rock mechanism of the pick cutting load

A Novel Fractional Tikhonov Regularization Coupled with an Improved Super-Memory Gradient Method and Application to Dynamic Force Identification Problems

This paper presents a novel inverse technique to provide a stable optimal solution for the ill-posed dynamic force identification problems. Due to ill-posedness of the inverse problems, conventional numerical approach for solutions results in arbitrarily large errors in solution. However, in the field of engineering mathematics, there are famous mathematical algorithms to tackle the ill-posed problem, which are known as regularization techniques. In the current study, a novel fractional Tikhonov regularization (NFTR) method is proposed to perform an effective inverse identification, then the smoothing functional of the ill-posed problem processed by the proposed method is regarded as an optimization problem, and finally a stable optimal solution is obtained by using an improved super-memory gradient (ISMG) method. The result of the present method is compared with that of the standard TR method and FTR method; new findings can be obtained; that is, the present method can improve accuracy and stability of the inverse identification problem, robustness is stronger, and the rate of convergence is faster. The applicability and efficiency of the present method in this paper are demonstrated through a mathematical example and an engineering example

Dynamic Force Identification Problem Based on a Novel Improved Tikhonov Regularization Method

The main purpose of this paper is to identify the dynamic forces between the conical pick and the coal-seam. According to the theory of time domain method, the dynamic force identification problem of the system is established. The direct problem is described by Green kernel function method. The dynamic force is expressed by a series of functions superposed by impulses, and the dynamic response of the structure is expressed as a convolution integral form between the input dynamic force and the response of Green kernel function. Because of the ill-conditioned characteristics of the structure matrix and the influence of measurement noise in the process of dynamic force identification, it is difficult to deal with this problem by the usual numerical method. In present content, a novel improved Tikhonov regularization method is proposed to solve ill-posed problems. An engineering example shows that the proposed method is effective and can obtain stable approximate solutions to meet the engineering requirements