The Construction of University Network Education System Based on Mobile Edge Computing in the Era of Big Data

This article first established a university network education system model based on physical failure repair behavior at the big data infrastructure layer and then examined in depth the complex common causes of multiple data failures in the big data environment caused by a single physical machine failure, all based on the principle of mobile edge computing. At the application service layer, a performance model based on queuing theory is first established, with the amount of available resources as a conditional parameter. The model examines important events in mobile edge computing, such as queue overflow and timeout failure. The impact of failure repair behavior on the random change of system dynamic energy consumption is thoroughly investigated, and a system energy consumption model is developed as a result. The network education system in colleges and universities includes a user login module, teaching resource management module, student and teacher management module, online teaching management module, student achievement management module, student homework management module, system data management module, and other business functions. Later, the theory of mobile edge computing proposed a set of comprehensive evaluation indicators that characterize the relevance, such as expected performance and expected energy consumption. Based on these evaluation indicators, a new indicator was proposed to quantify the complex constraint relationship. Finally, a functional use case test was conducted, focusing on testing the query function of online education information; a performance test was conducted in the software operating environment, following the development of the test scenario, and the server’s CPU utilization rate was tested while the software was running. The results show that the designed network education platform is relatively stable and can withstand user access pressure. The performance ratio indicator can effectively assist the cloud computing system in selecting a more appropriate option for the migrated traditional service system.

Designing and Energy Estimation of Photovoltaic Energy Generation System and Prediction of Plant Performance with the Variation of Tilt Angle and Interrow Spacing

The focus of this research is to design a ground-mounted photovoltaic system at optimal tilt angle and interrow space to meet high demand of electrical energy. The Department of Electrical Engineering and Technology, GC University Faisalabad has been considered to perform the simulation test. This study is conducted using Meteonorm software for solar resource assessment. Furthermore, HelioScope software is used for modeling of a ground-mounted photovoltaic system, study of PV system’s performance in terms of annual generation, system losses and performance ratio and analysis of photovoltaic module’s performance, current-voltage and power-voltage curves for different irradiance levels. From SLD, it is seen that 11 strings are connected to each inverter and inverters output power are combined by using 20.0 A circuit interconnects. The performance of photovoltaic systems is impacted by tilt angle and interrow spacing. From simulation results of all cases, it is concluded that the PV system installed at 15° tilt angle with 4 feet interrow spacing are more efficient than the other installed PV systems, because total collector irradiance is maximum (1725.0 kWh/m2) as compared to other tilt angles. At 15° tilt angle, the annual production of photovoltaic system is 2.265 GWh and performance ratio of PV system is 82.0%. It is envisioned that this work will provide the guidance to energy system designers, planners and investors to formulate strategies for the installation of photovoltaic energy systems in Pakistan and all over the world.

Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils

Power quality evaluation is the process of assessing the actual power network parameters with respect to the ideal conditions. However, several new assets and devices among the grid include mining the voltage and current quality. For example, the power converters needed for renewable energy sources’ connection to the grid, electric vehicles, etc., are some of the main sources of disturbances that inject high-frequency components into the grid. Consequently, instrument transformers (ITs) should be capable of measuring distorted currents and voltages with the same level of accuracy guaranteed for the ideal frequency (50–60 Hz). This is not a simple task if one considers that several other influence quantities endlessly act on the ITs. To this purpose, considering the lack of a standard, this work presents a measurement setup and specific tests for testing a commonly used type of low-power current transformer, the Rogowski coil (RC). In particular, the accuracy performance (ratio error and phase displacement) of the RCs was evaluated when measuring distorted signals while other influence quantities affected the RCs. Such quantities included positioning, burden, and magnetic field. The results indicate which quantities (or combination of them) have the greatest effect on the RC’s accuracy performance.

Intellectual Capital, Islamicity Performance Index, dan Kinerja Keuangan Bank Syariah di Indonesia (Studi Pada Tahun 2015-2020)

<p>This study aims to determine intellectual capital, Islamicity performance index, and financial performance on Islamic banks in Indonesia for 2015-2020. Islamicity performance index was proxied by a profit-sharing ratio, zakat performance ratio, and Islamic income vs. non-Islamic income, and financial performance was proxied by return on asset (ROA). This study uses causal associative research using a quantitative approach. The population of this study was all Islamic commercial banks in Indonesia for the 2015-2020 period, with a sample of 7 banks. Sampling was done by the purposive sampling method. Data collection uses secondary data, namely the documentation technique, by taking the annual report of each Islamic commercial bank in Indonesia for the 2015-2020 period. Data analysis technique using multiple linear regression analysis. The results of this study indicate that intellectual capital has a positive and significant effect on return on asset (ROA), profit sharing ratio has a significant negative effect on return on asset (ROA), zakat performance ratio has a positive and significant effect on return on asset (ROA), and Islamic income vs. non-Islamic income has a positive and significant effect on return on asset (ROA).</p><p><em>Keywords: Intellectual Capital; Profit Sharing Ratio; Zakat Performance Ratio Islamic Income vs Non-Islamic Income</em>; <em>and</em> <em>Financial Performance.</em></p>

Designing of a Hybrid Photovoltaic Structure for an Energy-Efficient Street Lightning System Using PVsyst Software

With the depletion of traditional fossil fuels, their disastrous impact on the environment and rising costs, renewable energy sources such as photovoltaic (PV) energy are rapidly emerging as sustainable and clean sources of power generation. The performance of photovoltaic systems is based on different factors such as the type of photovoltaic modules, irradiation potential and geographic location. In this research, PVsyst simulation software is used to design and simulate a hybrid photovoltaic system used to operate energy-efficient street lightning system. The simulation is performed to analyze the monthly/annual energy generated (kWh) by the hybrid system and specific power production (kWh/KWp). Additionally, various PV system losses are also investigated. The hybrid PV system has 4 parallel strings, and each string has 13 series-connected (mono crystalline 400 W Canadian Solar) PV modules. The energy storage system consists of 16 Narada (AcmeG 12 V 200) batteries with a nominal capacity of 1600 Ah. The simulation results show that the total annual energy production and specific energy production, were calculated to be 26.68 MWh/year and 1283 kWh/kWp/year, respectively. Simulation results also show the maximum energy injected into the utility grid in the month of June (1.814 MWh) and the minimum energy injected into the utility grid in the month of January (0.848 MWh). The battery cycle state of wear is 84.8%, and the static state of wear is 91.7%. Performance ratio (PR) analysis shows that the highest performance ratio of the hybrid system was 68.2% in December, the lowest performance ratio was 62.7% in May and the annual average performance ratio of a hybrid PV system is 65.57%. After identifying the major source of energy losses, the detailed losses for the whole year were computed and shown by the loss diagrams. To evaluate the cost effectiveness of the proposed system, a simple payback period calculation was performed.

Center of Gravity Coordinates Estimation Based on an Overall Brightness Average Determined from the 3D Vision System

In advanced manufacturing technologies (including complex automated processes) and their branches of industry, perception and evaluation of the object parameters are the most critical factors. Many production machines and workplaces are currently equipped as standard with high-quality special sensing devices based on vision systems to detect these parameters. This article focuses on designing a reachable and fully functional vision system based on two standard CCD cameras usage, while the emphasis is on the RS 232C communication interface between two sites (vision and robotic systems). To this, we combine principles of the 1D photogrammetric calibration method from two known points at a stable point field and the available packages inside the processing unit of the vision system (as filtering, enhancing and extracting edges, weak and robust smoothing, etc.). A correlation factor at camera system (for reliable recognition of the sensed object) was set from 84 to 100%. Then, the pilot communication between both systems was proposed and then tested through CREAD/CWRITE commands according to protocol 3964R (used for the data transfer). Moreover, the system was proven by successful transition of the data into the robotic system. Since research gaps in this field still exist and many vision systems are based on PC processing or intelligent cameras, our potential research topic tries to provide the price–performance ratio solution for those who cannot regularly invest in the newest vision technology; however, they could still do so to stay competitive.

Analysis of the Driving Factors of Building Energy Consumption Growth in Smart Cities Based on the STIRPAT Model

Energy consumption in smart cities relates to every energy consumed to carry out an activity, produce something, or exist in a structure. The most common measurement of energy efficiency is energy consumption per square meter in city residential areas. The states’ problematic energy consumption characteristics in smart cities may include climatic change, rainfall issues, water scarcity, and electricity generation. Thus, based on the states of households, an expanded proposed system of statistic determination impact conversion by positive, accurate technology (STIRPAT) model has been developed. STIRPAT model is collaborative research that aims to learn about the dynamic connections between human systems and the surrounding environment. There are two methods of the STIRPAT model to satisfy the characteristic of the proposed approach. The energetic counseling framework is an emerging technique that overcomes climatic change, electricity generation, and rainfall issues by sensing it in the environment. An algorithmic approach of the standard genetic method offers to conclude the problems into a cloud block mechanism for visualizing the states. Thus, the integrated technique of these two methods shows the factual implementation to overcome the statistical problems. Further research shows that, since the significant effect had been taken into account, the energy consumption per square meter in metropolitan residential buildings peak occurred eleven years later than without considering the dilution effect. The performance ratio of the STIRPAT model is estimated to be 98.3% by comparing with overall researches.

Artificial Intelligence-Based Quality Management and Detection System for Personalized Learning

Recently, the teaching and learning method in the conventional engineering education system needs a group of learners with personalized learning paths. The introduction of technologies like Artificial Intelligence will aid the learners to identify and detect learning opportunities utilizing historical information, present student profile and success data from an institution, and recommend learning measures to enhance their performance. This study proposes an Artificial Intelligence-based Meta-Heuristic Approach (AIMHA) for personalized learning detection systems and quality management. The proposed model has been utilized to optimize learning effectiveness by considering the nature of the learning path and the number of simultaneous visits to every learning action. In addition, a quality resolution can be determined by a meta-heuristic approach. The simulation findings of the learning actions have been utilized to examine the efficiency of the suggested method. The proposed method is evaluated learning activities achieved an efficiency ratio of 92.3%, sensitivity analysis ratio of 88.4%, performance ratio of 92.3%, precision ratio of 94.3% compared to other existing models.

Acceptance Ratio Analysis in Grid-Connected Photovoltaic System: Is There Any Difference Between DC and AC?

The performance status of a grid-connected photovoltaic (GCPV) system is denoted by performance indices, namely performance ratio, capacity factor, and even through power acceptance ratio (AR), as documented in Malaysia Standard (MS) procedures for acceptance test of GCPV testing and commissioning (TNC). Even though AR analysis can be either on the DC or AC side, the MS TNC procedures implemented analysis on the AC side. Therefore, the question arises whether there is any significant difference when using AC AR analysis compared to DC AR analysis in evaluating the system performance. Thus, this paper evaluates the differences between applying DC AR analysis and AC AR analysis in accessing the performance of the ten kWp GCPV system in Malaysia. The AR analytical analysis employed the 2019 one-year historical data of solar irradiance, module temperature, DC power, and AC power. The results demonstrated that the monthly AC AR were consistently lower than DC AR with a percentage difference of approximately 3%. The percentage discrepancy was due to the variation of actual inverter efficiencies compared to the declared constant value by the manufacturer used in the AR prediction model. These findings have verified a significant difference between DC AR analysis and AC AR analysis. Most importantly, this study has highlighted the significance of AC AR analysis compared to DC AR analysis as a tool to evaluate GCPV system performance because AC AR has taken an additional factor into consideration, which is the inverter efficiency variation.