Search

In this paper, we propose a novel state estimation plan using Adaptive Neuro-Fuzzy Inference System (ANFIS). To increase the speed and accuracy of estimation, an individual ANFIS is used for each bus. To improve the accuracy of training, the grasshopper optimization algorithm (GOA) is used for the training and optimization of ANFIS parameters. The main advantage of GOA training of ANFIS parameters is the increase in speed and accuracy in estimating power system state variables. One of the main features of the proposed design is its ability to provide an appropriate state estimation when incomplete data is sent to the control center due to the disconnection of communication or the failure of the measurement devices. The recovery of the missed data is implemented by the Group Method of Data Handling (GMDH) neural network. The GMDH neural network is widely used due to its proper speed for function estimation and approximation. Incomplete information obtained from measurements to estimate the state is processed by the GMDH neural network to recover lost information. The output of this neural network, which is the retrieval of complete measurement information, is given to ANFIS to estimate the state of the power system as input. [ABSTRACT FROM AUTHOR]

Objective: Smart ion-releasing restorative materials may increase the success and survival rate of composite dental restorations. This study aimed to evaluate the impact of sintering temperature on the mechanical properties and ionreleasing behavior of fluorohydroxyapatite (FHA)-filled dental resin composites. Methods: The FHA was synthesized via a sol-gel method and sintered at three temperatures (250, 500, and 1000ºC). FHA fillers were characterized using X-ray diffraction (XRD) and Fourier Transform Infrared (FTIR) spectroscopy, then coated with citric acid and incorporated (40% w/w) into a photo-curable resin matrix (Bis-GMA, TEGDMA, HEMA; weight ratio 2:1:1). Flexural strength (FS) and diametral tensile strength (DTS) were measured, as well as the depth of cure, which was determined by the Vickers microhardness test. The release of calcium and fluoride ions was monitored for a month in diluted citric acid (pH=4.3), and the effect of acidic storage on mechanical properties was evaluated via DTS testing. The filler distribution was observed via scanning electron microscopy. The influence of sintering temperature on the measured variables was statistically analyzed using one-way and two-way ANOVA (α=0.05). Results: The XRD results revealed increased crystallinity with higher sintering temperatures. Correspondingly, mechanical properties improved with increasing sintering temperature (P<0.05), whereas the depth of cure, as well as ion release (Ca+2 and F-), decreased (P<0.05). Conclusions: Sintering at elevated temperatures yielded a greater amount of crystalline apatite structure, and enhanced mechanical properties of FHA-filled dental resin composites, but reduced ion release. Based on these findings, FHA fillers sintered at approximately 500ºC appear optimal for creating smart dental resin composite. [ABSTRACT FROM AUTHOR]