Journal of Majlesi Journal of Mechatronic Systems
Volume:11 Issue: 3, Sep 2022

The understanding of services by customers and the appropriate website quality of organizations and companies as a portal to introduce the organization and the services provided can encourage the purchase of the organization's and company's products while increasing customer trust. Therefore, the purpose of the current research is to investigate the effect of perceived service quality, website quality and reputation on the intention to buy online, given to account the role of trust and understanding of risk in online shopping among customers of Shahr Bank in Isfahan. This research is applied in terms of purpose and descriptive survey. The statistical population includes the customers of Shahr Bank of Isfahan province, so a sample size of 384 people was considered using Cochran's formula by simple random sampling method. In order to collect data, a questionnaire (perceived service quality, website quality, website reputation, purchase intention and trust online shopping by Hamzah, et al. (2017) and a questionnaire on perceived risk among customers by Ariffin et al. (2018) was used. The validity of the questionnaires was confirmed by the supervisor and the advisor and was also confirmed by calculating the divergent validity. Reliability of questionnaires using Cronbach's alpha coefficient for variables (perceived service quality, website quality, website reputation, purchase intention, trust online shopping, perceived risk in customers, respectively 0.84, 0.84, 0.86, 75 0.0, 0.87, 0.78) which are all more than 0.7 and were confirmed. For statistical calculations, SPSS software was used for descriptive statistics and Smart PLS for inferential statistics and hypothesis calculations. The findings of the research showed that perceived service quality and website quality have a significant effect on the online purchase intention of Shahr Bank customers of Isfahan province with the mediating role of trust. However, the hypotheses of website reputation on online purchase intention with the mediating role of trust and trust online shopping on online purchase intention due to the moderating role of perceived risk have not been confirmed.

A Heat Recovery Steam Generator (HRSG) is in steady state when it converts wasted energy to useful energy for the system. Energy recovery systems are installed for using the wasted energy. Efficiency problems and disturbance decrease in boilers are controversial issues in a complex process with nonlinear parameters, uncertainty and load fluctuations. Predictive control can significantly improve total efficiency and energy recovery in nonlinear systems with high unpredictable losses. Process efficiency can be increased by heat recovery from stack gas flow. In order to increase the efficiency, predictive model is based on PSO algorithm. To decrease the loss, heat recovery boiler output pressure increase, water temperature increase and turbine output pressure decrease are suggested. A boiler in normal operation should have a desired constant steam pressure in drum output. Heat transfer mechanism to evaluate system dynamics includes key process variables responses such as steam pressure, temperature and mass flow. Simulation results show that acceptable error has a difference less than 10% compared to design data. Analyzed variables consist of: gas temperature, fluid temperature, drum pressure, drum fluid level and mass flow in each unit.

In this research, the performance of different turbulent flow models in the simulation of fluid flow passing through the chain of stator vanes of a turbine has been investigated. In this research, the Reynolds number is considered equal to 23.2×105. In the Navier-Stokes equations, discretization is done using the finite volume method on the computational grid, and Simple algorithm is used to couple the velocity and pressure equations. Different turbulence models include two-equation models, Realizable k-ε, RNG k-ε, SST k-ω and the five-equation model of the Reynolds stress method (RSM). The performance of various turbulent flow models has been investigated by calculating the pressure coefficient obtained from the present work in comparison with the laboratory results in ten different areas of the third blade of the chain. The results show that the accuracy of the aforementioned turbulence models in estimating the static pressure coefficient in different regions of the aforementioned vane chain are slightly different, but the five-equation Reynolds stress model has a better match with the experimental results.

In this paper, an adaptive neural fuzzy control method is proposed to control hyper-chaotic dynamic systems. Due to the inherent complexity of hyper-chaotic systems, the neural fuzzy network training architecture is described, which includes the type and number of training inputs. The training data for the adaptive neural fuzzy network is based on nonlinear control. In other words, the data of a nonlinear controller, which includes error vectors and control input vectors, is used for neural fuzzy training. The numerical simulation results show that the proposed method is suitable for controlling hyper-chaotic systems. After the controller design, an external disturbance is added to the model to evaluate the performance of the ANFIS controller. The time to reach zero error and the behavior of the control signal are discussed, this can be an important issue in real-world implementation and manufacturing.

One of the missions of electricity distribution companies is to minimize losses and deliver energy with high reliability to subscribers. Installing a new transformer in the distribution network is one of the ways to reduce losses and voltage drop in the weak pressure network. Optimum placement of the transformer requires monitoring of various components such as losses, determination of the contribution of each feeder and energy consumption area to the total amount of network losses, the number of subscribers on each feeder and the length of the feeder. In this article, we present a quick and practical method with QGIS software to locate the transformer by considering the center of gravity of the load, and then analyze the load section with the round-trip sweeper algorithm.

To be successful in the digital era and advanced industries, the maintenance and optimal use of automatic equipment and machines are significant. Thus, the role of instrumentation equipment for correct measurement of the sensitive parameters in tools appears. Every organization needs high-precision measuring instruments to maintain its production quality. Besides, maintaining precision in measuring equipment requires controlling it by repeating its calibration. Accurate prediction of recalibration is significant because a short calibration interval increases the stopping time in the production line and the depreciation of measuring equipment. As a result, the increase in the stopping time can increase measurement uncertainties, causing other problems, such as quality loss in the production line. The present study aims to develop a method for timing the calibration of instrumentation equipment using Failure Modes and Effects Analysis (FMEA) and reliability, using the Risk Priority Number (RPN) and Reliability-Centered Maintenance (RCM) and via self-organizing map (SOM) neural network clustering. This method was implemented for 220 pieces of instrumentation equipment in the water supply system of Isfahan Zob Ahan Co. The research results show that this clustering leads to the change of calibration intervals and cost reduction in this part of Isfahan Zob Ahan Co.