Fuzzy Inference System for Speed Bumps Detection Using Smart Phone Accelerometer Sensor

Recently, a significant amount of research attention has been given to monitoring the road surface anomalies such as potholes and speed bumps. In this paper, speed bump detection method based on a fuzzy inference system (FIS) is proposed. The fuzzy inference system detects and recognizes the speed bumps from the variance of the vertical acceleration and the speed of the vehicle. The proposed method utilizes the embedded sensor (accelerometer) in the Smartphone. The proposed method is tested and evaluated under different speed levels. The results show that the proposed method is promising for bumps detection

Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second. Doi: 10.28991/ESJ-2023-SPER-05 Full Text: PD

Sizing, economic, and reliability analysis of photovoltaics and energy storage for an off‐grid power system in Jordan

Remote areas in Jordan often rely on expensive and polluting diesel generators to meet their electricity demand. This study investigates 100% renewable solutions to supply the electricity demand of off‐grid energy systems through optimal sizing of photovoltaics and energy storage systems. A linear programming approach is proposed to minimise the annualised cost of electricity supply including capital costs of equipment and their operation and maintenance costs. The optimisation determines the size of photovoltaics and energy storage required to satisfy electricity demand at every hour of a selected year. A Jordan campsite was used as a case study to assess and compare the performance of PV‐battery storage and PV‐hydrogen storage systems from economic and reliability perspectives. The results show that hydrogen storage was more economical for a 100% renewable energy system. However, introducing some diesel generation gave the battery system a significantly lower annualised cost of energy

Power Quality Investigation of Single Phase Grid-connected Inverter of Photovoltaic System

There is a growing demand for renewable energy resources in countries all around the world. Among renewable energy resources, solar energy is a prominent and promising alternative to meet future electricity needs. Recently, the renewable energy regulations in Jordan have been modified to allow customers to install their own photovoltaic (PV) generators to cover their full energy consumption. This study investigated the power quality profile of single-phase grid-connected PV system in a typical Jordanian low voltage electrical system. The following electrical parameters were monitored: voltage, current, harmonics contents, total harmonics distortion (THD), active power, reactive power, and power factor. Detailed investigations and analyses were made

Experimental Study on the Effect of Dust Deposition on a Car Park Photovoltaic System with Different Cleaning Cycles

For a decade, investments in solar photovoltaic (PV) systems have been increasing exponentially in the Middle East. Broadly speaking, these investments have been facing tremendous challenges due to the harsh weather in this particular part of the world. Dust accumulation is one the challenges that negatively affects the performance of solar PV systems. The overall goal of this paper is to thoroughly investigate the effect of dust accumulation on the energy yield of car park PV systems. With this aim in mind, the paper presents scientific values for further research and opens the horizon for attracting further investments in solar PV systems. This study is based on a real PV system in the Sultanate of Oman and considers different cleaning cycles for 16 months (from 29 July 2018 to 10 November 2019). Furthermore, four different PV groups were assessed, and the system was monitored under different cleaning frequencies. In general, it was found that dust accumulation has a significant impact; under 29-day, 32-day, 72-day, and 98-day cleaning cycles, the average percentages of energy loss due to soiling were 9.5%, 18.2%, 31.13%, and 45.6%, respectively. In addition, the dust effect has a seasonal variation. The study revealed that dust accumulation has a more negative impact during summer than during winter. During summer, the energy losses due to soiling were 8.7% higher than those during winter. The difference was attributed to different environmental conditions, with high humidity and low wind speed being the main factors that worsen the impact of dust during summer. Based on the findings of this research, a monthly cleaning program is highly recommended in the city of Muscat

An Optimal Nonlinear Type-2 Fuzzy FOPID Control Design Based on Integral Performance Criteria Using FSM

A fractional-order fuzzy proportional integral derivative (PID) controller is a controller that combines the benefits of fractional calculus and fuzzy logic with the conventional PID controller. In this paper, a four-stage optimization algorithm is proposed for the design of a Type-2 Fuzzy fractional-order PID controller based on the Fourier Series Method (FSM). Three distinct control structures are introduced: Type-2 fuzzy fractional PD + fractional PI controller, Type-2 fuzzy fractional PID, and Type-2 fuzzy fractional PD + Type-2 fuzzy fractional PI controller. In addition to a modified multi-performance criterion cost function, four integral performance criteria are employed as cost functions for each stage. The suggested algorithm avoids the utilization of the approximation equivalent for the fractional-order system and instead employs FSM. Furthermore, the approach optimizes the nonlinearity within the upper membership function (UMF) and the uncertainty range through the lower membership function, as opposed to arbitrary selection. By considering variations in the membership functions, the outcomes exhibit a superior response compared to previous investigations. The results of the three control structures are compared with the traditional PID controller, and simulation results demonstrate the feasibility of this technique. The findings suggest that by optimizing different integral performance criteria using this design technique, controllers for both integer and fractional-order plants can yield favorable step responses. The proposed algorithm is validated by comparing its step response performance with that of previous research, followed by a discussion on sensitivity analysis and computational requirements