Design & control of precision surgical device for otitis media with effusion

Ph.DDOCTOR OF PHILOSOPH

Adaptive Fractional-Order Sliding Mode Controller with Neural Network Compensator for an Ultrasonic Motor

Ultrasonic motors (USMs) are commonly used in aerospace, robotics, and medical devices, where fast and precise motion is needed. Remarkably, sliding mode controller (SMC) is an effective controller to achieve precision motion control of the USMs. To improve the tracking accuracy and lower the chattering in the SMC, the fractional-order calculus is introduced in the design of an adaptive SMC in this paper, namely, adaptive fractional-order SMC (AFOSMC), in which the bound of the uncertainty existing in the USMs is estimated by a designed adaptive law. Additionally, a short memory principle is employed to overcome the difficulty of implementing the fractional-order calculus on a practical system in real-time. Here, the short memory principle may increase the tracking errors because some information is lost during its operation. Thus, a compensator according to the framework of Bellman's optimal control theory is proposed so that the residual errors caused by the short memory principle can be attenuated. Lastly, experiments on a USM are conducted, which comparative results verify the performance of the designed controller.Comment: 9 pages, 9 figure

Tuning the feedback controller gains is a simple way to improve autonomous driving performance

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics. The aim of this paper is to highlight the limitations of this approach; it is shown that re-tuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness

Biocontrol of bacterial spot diseases of muskmelon using Paenibacillus polymyxa G-14

Paenibacillus polymyxa strain G-14 (PpG14) isolated from the muskmelon rhizosphere, produces antibiotic(s) that are active against Pseudomonas syringae pv. lachrymans and Acidovorax avenae subsp. citrulli (two pathogens that cause bacterial spot diseases). Strain G-14 strongly inhibited the growth of Pseudomonas syringae pv. lachrymans and Acidovorax avenae subsp. citruli in a dual-culture plate assay. The biocontrol activity of PpG14 was examined by pot and field tests. Results show that the strain significantly reduced the development and suppressed the incidence of bacterial spot diseases. Moreover, the prevention treatment was better than the therapy treatment when using this strain. Based on its main bacteriological properties, identification using VITEK 32 and analysis of the 16S rDNA gene sequence, showed that strain G-14 belonged to P. polymyxa. Optimal growth was studied; temperature and pH were 28°C and 7, respectively.Key words: Muskmelon, bacterial spot diseases, Paenibacillus polymyxa, biocontrol

Real-time generative design of diverse, "truly" optimized structures with controllable structural complexities

Compared with traditional design methods, generative design significantly attracts engineers in various disciplines. In thiswork, howto achieve the real-time generative design of optimized structures with various diversities and controllable structural complexities is investigated. To this end, a modified Moving Morphable Component (MMC) method together with novel strategies are adopted to generate high-quality dataset. The complexity level of optimized structures is categorized by the topological invariant. By improving the cost function, the WGAN is trained to produce optimized designs with the input of loading position and complexity level in real time. It is found that, diverse designs with a clear load transmission path and crisp boundary, even not requiring further optimization and different from any reference in the dataset, can be generated by the proposed model. This method holds great potential for future applications of machine learning enhanced intelligent design

More expressions of BDNF and TrkB in multiple hepatocellular carcinoma and anti-BDNF or K252a induced apoptosis, supressed invasion of HepG2 and HCCLM3 cells

<p>Abstract</p> <p>Background</p> <p>Brain-derived neurotrophic factor (BDNF) and its receptor Tropomysin-related kinase B (TrkB) are commonly up-regulated in a variety of human tumors. However, the roles of BDNF/TrkB in hepatocellular carcinoma (HCC) have been poorly investigated.</p> <p>Methods</p> <p>We evaluated the expressions of BDNF and TrkB in 65 cases of HCC by immunohistochemical staining. Moreover, in human HCC cell lines of HepG2 and high metastatic HCCLM3, the secretory BDNF in supernatant was measured by ELISA, the effects of BDNF neutralizing antibody or Trk tyrosine kinase inhibitor K252a on apoptosis and invasion were examined by flow cytometry and transwell assay respectively.</p> <p>Results</p> <p>Higher expression of BDNF (63.1%) or positive expression of TrkB (55.4%) was found in HCC specimens, which was significantly correlated with multiple and advanced stage of HCC. BDNF secretory level in HCCLM3 was higher than that in HepG2 cells. Both anti-BDNF and K252a effectively induced apoptosis and suppressed invasion of HepG2 and HCCLM3 cells.</p> <p>Conclusions</p> <p>These findings suggested that BDNF/TrkB are essential for HCC cells survival and invasion. BDNF/TrkB signaling should probably be an effective target to prevent HCC advancement.</p

Densifying Hydration Products of Alite by a Bio-Inspired Admixture

A bio-inspired, plant-derived polyphenol, tannic acid (TA) was identified as a renewable admixture to improve the compressive strength of concretes. Aiming to understand the underlying mechanism responsible for this strength improvement, this study examines how TA mediates the hydration of tricalcium silicate (alite). Experimental study shows that TA can form complex with calcium ions through chelating, retarding the hydration of the alite and changing of the hydration products. Particularly, X-ray diffraction analysis shows that TA makes calcium hydroxide preferentially grow on the [0 0 1] face. Fourier-transform infrared spectroscopy and 29Si MAS NMR results reveal that the mean chain length of calcium silicate hydrate (C[sbnd]S[sbnd]H) is reduced by TA. More importantly, mercury intrusion porosimetry testing reveals that pores with size near 30 nm was almost eliminated by adding TA, leading to higher elastic modulus of the produced C[sbnd]S[sbnd]H and higher compressive strength of the produced concrete

AutoEncoding Tree for City Generation and Applications

City modeling and generation have attracted an increased interest in various applications, including gaming, urban planning, and autonomous driving. Unlike previous works focused on the generation of single objects or indoor scenes, the huge volumes of spatial data in cities pose a challenge to the generative models. Furthermore, few publicly available 3D real-world city datasets also hinder the development of methods for city generation. In this paper, we first collect over 3,000,000 geo-referenced objects for the city of New York, Zurich, Tokyo, Berlin, Boston and several other large cities. Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation. Specifically, we first propose a novel Spatial-Geometric Distance (SGD) metric to measure the similarity between building layouts and then construct a binary tree over the raw geometric data of building based on the SGD metric. Next, we present a tree-structured network whose encoder learns to extract and merge spatial information from bottom-up iteratively. The resulting global representation is reversely decoded for reconstruction or generation. To address the issue of long-dependency as the level of the tree increases, a Long Short-Term Memory (LSTM) Cell is employed as a basic network element of the proposed AETree. Moreover, we introduce a novel metric, Overlapping Area Ratio (OAR), to quantitatively evaluate the generation results. Experiments on the collected dataset demonstrate the effectiveness of the proposed model on 2D and 3D city generation. Furthermore, the latent features learned by AETree can serve downstream urban planning applications

Classification of coal-bearing strata abnormal structure based on POA–ELM

In order to identify and classify the abnormal structures in coal-bearing strata more accurately, a POA−ELM model based on the pelican optimization algorithm (POA) and the extreme learning machine (ELM) is proposed. The performance of extreme learning machine is unstable because the input weights and hidden layer bias are generated randomly. The POA can be used to optimize the input weights and hidden layer bias of extreme learning machine, so as to improve the performance of extreme learning machine model. The POA−ELM model is applied to identify and classify the abnormal structures in coal-bearing strata. Firstly, three coal-bearing strata simulation models of small fault, scour zone and collapse column are established with the COMSOL Multiphysics5.5. The Ricker wave is the source signal. The in-seam wave signals are collected by wave transmission method, and the in-seam wave data set is established. Then the z-score method is used to standardize the in-seam wave data and the principal component analysis (PCA) is used to reduce the dimension. Secondly, the POA is used to optimize the extreme learning machine, and the POA−ELM classification model is constructed with MATLAB. The POA−ELM model is used to classify small fault, scour zone and collapse column. The classification performance of ELM and POA−ELM is evaluated and compared by cross-validation method and evaluation indices such as accuracy, precision and recall rate. The results show that the POA can effectively optimize the ELM, and the POA−ELM model has higher classification accuracy and better stability. The classification accuracy of POA−ELM for abnormal structures can reach more than 99%. Thirdly, in order to verify the classification effect of POA−ELM in practical applications, after wavelet de-noising, z-score standardization and PCA dimensionality reduction, the real fault in-seam wave data are used as the test set and imported into the POA−ELM model for classification. The results show that the identification accuracy of POA−ELM model for real fault can reach more than 97%. Finally, based on the same data set, the classification effects of POA−ELM, ELM, support vector machine (SVM) and BP neural network are compared. The results show that the identification and classification accuracy of POA−ELM model is the highest. Through research and analysis, the POA can effectively optimize the ELM, and the POA−ELM model can accurately classify different geological structures and effectively identify real faults, which is better than other methods