Calibration of an interferometric surface measurement system on an ultra-precision turning lathe

On-machine measurement avoids the time-consuming transposition operations between the measurement and machine coordinates. The present work integrates an interferometric probing system on an ultra-precision turning machine. Due to the relatively harsh environment in the machine tools, metrological characteristics of the surface measurement instrument would deviate from those tested under standard laboratory conditions. In order to improve the performance of on-machine measurement systems, it is necessary to calibrate the on-machine measurement (OMM) system and compensate for any systematic errors. Three key issues, including on-machine vibration, machine tool kinematics error, and linearity error are discussed in this study. Experimental investigation is conducted to prove the validity of proposed calibration methodology and the effectiveness of on-machine measurement

Load flow calculation for droop-controlled islanded microgrids based on direct Newton-Raphson method with step size optimisation

Load flow calculation for droop-controlled islanded microgrids (IMGs) is different from that of transmission or distribution systems due to the absence of slack bus and the variation of frequency. Meanwhile considering the common three-phase imbalance condition in low-voltage systems, a load flow algorithm based on the direct Newton-Raphson (NR) method with step size optimisation for both three-phase balanced and unbalanced droop-controlled IMGs is proposed in this study. First, the steady-state models for balanced and unbalanced droop-controlled IMGs are established based on their operational mechanisms. Then taking frequency as one of the unknowns, the non-linear load flow equations are solved iteratively by the NR method. Generally, iterative load flow algorithms are faced with challenges of convergence performance, especially for unbalanced systems. To tackle this problem, a step-size-optimisation scheme is employed to improve the convergence performance for three-phase unbalanced IMGs. In each iteration, a multiplier is deduced from the sum of higher-order terms of Taylor expansion of the load flow equations. Then the step size is optimised by the multiplier, which can help smooth the iterative process and obtain the solutions. The proposed method is performed on several balanced and unbalanced IMGs. Numerical results demonstrate the correctness and effectiveness of the proposed algorithm

A Novel Virtual Vector Modulation-based scheme of Model Power Predictive for VIENNA Rectifier

When the finite control set model predictive(FCS-MPC) algorithm is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the micro-controller, complex multi-objective optimization and limited output vector switching. In additional, the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. Furthermore, due to the limitation of finite set model prediction, it leads to the switching operation is not constant and the decrease of the grid-connected current quality. In this regard, an improved model predictive direct power control based on the combined virtual vector modulation (MPDPC-VM) is proposed by considering the influence of the filter inductance parameter mismatch. The finite control set and restricted vector switching of the Vienna rectifier are modeled to avoid excessive voltage jumps, and the predicted values of input power is obtained by the sliding-mode control (SMC) strategy. Then, a linear synthesis method of virtual vector modulation-based scheme is proposed, which increases the number of the available voltage vectors in a single switching period from 8 to 19. The grid-connected current ripple is improved by reducing the error between the expected voltage vector and the available voltage vector. Finally, the model reference adaptive system (MRAS) method is applied to improve the working reliability and reduce the influence of mismatching of inductance parameters. Extensive simulation and matching experimental results is given to demonstrate the validity of the proposed strategy under steady-state and transient responses conditions compared against the existing FCS-MPC

Comparative analysis of the efficacies of probiotic supplementation and glucose-lowering drugs for the treatment of type 2 diabetes: A systematic review and meta-analysis

The aim of this systematic review and meta-analysis was to evaluate the effects of probiotics and glucose-lowering drugs (thiazolidinedione [TZD], glucagon-like pep-tide-1 receptor agonists [GLP-1 RA], dipeptidyl peptidase IV inhibitors, and sodium glucose co-transporter 2 inhibitors [SGLT-2i]) in patients with type 2 diabetes from randomized con-trolled trials (RCTs). The PubMed, Web of science, Embase, and Cochrane Library databases were searched on the treatment effects of probiotics and glucose-lowering drugs on glycemia, lipids, and blood pressure metabolism published between Jan 2015 and April 2021. We performed meta-analyses using the random-effects model. We included 25 RCTs (2,843 participants). Overall, GLP-1RA, SGLT-2i, and TZD significantly reduce fasting blood sugar (FBS) and glycated hemoglobin (HbA1c), whereas GLP-1 RA increased the risk of hypoglycaemia. Multispecies probiotics decrease FBS, total cholesterol (TC), and systolic and diastolic blood pressure (SBP, DBP). Moreover, subgroup analyses indicated that participants aged >55 years, BMI ≥30 kg/m2, longer duration of intervention, and subjects from Eastern countries, showed significantly higher reduction in FBS and HbA1c, TC, TG and SBP. This meta-analysis revealed that including multiple probiotic rather than glucose-lowering drugs might be more beneficial regarding T2D prevention who suffering from simultaneously hyperglycemia, hypercholesterolemia, and hypertension

Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks

The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value

Hybrid K-means Algorithm and Genetic Algorithm for Cluster Analysis

Cluster analysis isa fundamental technique for various filed such as pattern recognition, machinelearning and so forth. However, the cluster number is predefined by users inK-means algorithm, which is unpractical to implement.  Since the number of clusters is a NP-completeproblem, Genetic Algorithm is employed to solve it. In addition, due to the largetime consuming in conventional method, an improved fitness function isproposed. According to the simulation results, the proposed approach isfeasible and effective. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.480

Intelligent Control in the Application of a Rotary Dryer for Reduction in the Over-Drying of Cut Tobacco

The drying process is fundamental for cut tobacco processing. However, there are some problems related to the drying process such as overheating, or inconsistent control of moisture content. This paper shows how an intelligent controller is designed for an industrial rotary drying system. This controller is applied to a tobacco production unit to reduce overdried cut tobacco and improve the overall unit performance. The proposed control system aims to keep the content of moisture at the dryer outlet as close as possible to the optimal value and improve the homogeneity of the product without any operator intervention. The study shows that, if a reduction of humidity in the cut tobacco drying process is achieved using AI, the quality of the final product improves. In particular, if compared to regulatory control, the proposed method constantly monitors and adjusts the moisture content level in order to reduce the amount of overdried product. The findings of this paper indicate that the suggested process can save at least 222.2 kg of cut tobacco for each batch in the first stage of the drying process