A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture

USING A MULTI-CRITERIA DECISION-MAKING MODEL TO EVALUATE AND SELECT AN E-COMMERCE PLATFORM

The COVID-19 pandemic has led to disruptions in consumers' lifestyles and purchases, as well as businesses' online business models. Online platforms are increasingly used for shopping purposes. To evaluate and choose an e-commerce platform requires using many criteria and decision makers. Therefore, the process of evaluating and selecting an e-commerce platform is viewed as a multi-criteria decision-making problem. The objective of this study is to develop a multi-criteria decision-making model to help consumers evaluating the e-commerce platforms. In the proposed model, the ratings of alternatives and the weights of the criteria are evaluated using the linguistic variable. Simulation examples are used to show the effectiveness of the model in practice.  Keywords: Fuzzy TOPSIS, E-Commerce Platform, Mcdm, Fuzzy Sets

Artefactual Suboptimal Fixation Effect to Nuclear Staining on Erythrocytes of Lutjanus kasmira (Forsskål 1775)

This research presents the unusual staining characteristic of the teleosts red blood cells–the common bluestripe snapper Lutjanus kasmira, caught at Toc Tan (Allison) Reef, Truong Sa Archipelago, Vietnam, due to side effects of the delayed staining procedure. In addition to the quantitative data measured, the results of microscopic image research showed that, on slides performed with the delayed Romanovsky staining procedure using Giemsa dye, the cell "nucleus" was not dye-stained as seen in the erythrocytes of much other fish. The AFM and SEM images of blood slides show that the structure of the nuclear zone and the nuclear membrane was destroyed. Although methanol is used as one of the common fixatives in biology, the delayed staining still causes artifactual side effects, disrupts the structure of the red blood cell nucleus and can cause confusion when studying cytogenetics

Efficient machine learning models for prediction of concrete strengths

In this study, an efficient implementation of machine learning models to predict compressive and tensile strengths of high-performance concrete (HPC) is presented. Four predictive algorithms including support vector regression (SVR), multilayer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost) are employed. The process of hyperparameter tuning is based on random search that results in trained models with better predictive performances. In addition, the missing data is handled by filling with the mean of the available data which allows more information to be used in the training process. The results on two popular datasets of compressive and tensile strengths of high performance concrete show significant improvement of the current approach in terms of both prediction accuracy and computational effort. The comparative studies reveal that, for this particular prediction problem, the trained models based on GBR and XGBoost perform better than those of SVR and MLP

Fuzzy gain scheduling control apply to an RC Hovercraft

The Fuzzy Gain Scheduling (FGS) methodology for tuning the Proportional – Integral – Derivative (PID) traditional controller parameters by scheduling controlled gains in different phases, is a simple and effective application both in industries and real-time complex models while assuring the high achievements over pass decades, is proposed in this article. The Fuzzy logic rules of the triangular membership functions are exploited on-line to verify the Gain Scheduling of the Proportional – Integral – Derivative controller gains in different stages because it can minimize the tracking control error and utilize the Integral of Time Absolute Error (ITAE) minima criterion of the controller design process. For that reason, the controller design could tune the system model in the whole operation time to display the efficiency in tracking error. It is then implemented in a novel Remote Controlled (RC) Hovercraft motion models to demonstrate better control performance in comparison with the PID conventional controller

Influence Of Fabrication Condition on the Microstructural and Optical Properties of Lead-Free Ferroelectric Bi0.5_{0.5}Na0.5_{0.5}TiO3_{3} Materials

Lead-free ferroelectric materials have attracted considerable attention due to the increasing potential application in environmental benign materials. Among lead-free ferroelectric materials, the Bi0.5Na0.5TiO3 (BNT) materials were more studied because it exhibited the good ferroelectric and piezoelectric properties which could be promising candidate materials replacing Pb(Zr,Ti)O3. In this work, the lead-free ferroelectric BNT materials were synthesized by sol-gel method. The effects of fabrication process to microstructural and optical properties were studied which includes Na precursor concentration and calcining temperature. The result indicated that the Na precursor concentration were higher 40 mol.% and the calcining temperature

NetFPGA Based OpenFlow Switch Extension for Energy Saving in Data Centers

The increasing demand for data centers in both scale and size has led to huge energy consumption. The cost and environmental impact of data centers increases due to large amounts of carbon emissions. One solution to this problem is to intelligently control the power consumption of switches used in data centers. This paper proposes an extension to OpenFlow switches to support different power saving modes. The extension includes defining new messages in the OpenFlow protocol stack and designing an OpenFlow Switch Controller (OSC) that is able to turn on/off switches and disable/enable ports. To prove the soundness of the proposed extension, the functions of an OSC has been integrated in a NetFPGA based OpenFlow switch used in the ECODANE framework. The results presented in this paper can also be used by the OpenFlow compliant switches manufacturer or by power aware research community