Automotive Mechanical Vehicle Starter

Kader, Mohamed Mydin M. Abdul; Razali, Zol Bahri; Mustafa, Wan Azani; Saidi, Syahrul Affandi; Gunny, Ahmad Anas Nagoor; Setumin, Samsul; Osman, Muhammad Khusairi; Idris, Mohaiyedin; Akbar, M.S.; Sarbini, M A A; Mizam, Nor Syamina Sharifful

doi:10.1088/1742-6596/2107/1/012021

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Overview of VEHMS Risk Management Framework issues, accelerometer sensors with the continuous wavelet transform analysis for signal's peak value over time, RMS, mean, Kurtosis, and Skewness are preferred [11]. Magnetic pickup and voltage sensors are used to detect engine misfires and starting failures simultaneously using engine output power [12] and voltage variation [13]. Furthermore, it is proposed to use an online sensor to continuously monitor the physiochemical parameters of engine lubricating oil in real-time [14].…”

Section: Methodsmentioning

confidence: 99%

An Intelligent Risk Management Framework for Monitoring Vehicular Engine Health

Rahim

Rahman

et al. 2022

IEEE Trans. on Green Commun. Netw.

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The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence would be vital in the Industry 4.0 era to eliminate these issues for seamless activity and ultimate productivity. This article presents a risk management framework that includes an efficient decision model for monitoring and diagnosing vehicular engine health and condition in real-time using vulnerable components information and advanced techniques. To do this, we used the vulnerability identification frame to identify the vulnerable objects. We created a decision model that used an infrastructure vulnerability assessment model and sensor-actuator data to diagnose and categorise engine conditions as good, minor, moderate, or critical. We used machine learning and deep learning algorithms to assess the effectiveness of the risk management system's decision model. The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. Anticipating the proposed framework might assist the automotive sector in advancing with cutting-edge facilities that are up to date.

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Section: Methodsmentioning

confidence: 99%

An Intelligent Risk Management Framework for Monitoring Vehicular Engine Health

Rahim

Rahman

et al. 2022

IEEE Trans. on Green Commun. Netw.

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“…One is ventilated using fans for air inlet & outlet, while the other is without fan. This approach sounds simple, however very effective [13].…”

Section: Project Workflowmentioning

confidence: 99%

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

Mohamed Mydin M. Abdul Kader,

Muhammad Naufal Mansor,

Wan Azani Mustafa

et al. 2024

ARASET

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As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth- related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).

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“…La Tabla 3, expresa los accesorios que adicionalmente se requieren para el desarrollo, operatividad y funcionamiento del prototipo. Después de haber identificado los recursos, características y accesibilidad, se diseñó y ensamblo los microcontroladores mediante el software Fritzing, por ser una herramienta dinámicas, libre y con un ambiente sencillo, fácil e inmediato que permite implementar de forma segura, concreta y automatizada el modelo del circuito [22], tal como se indica en la Fig. 1.…”

Section: Diseñounclassified

Prototype mini-weather station with cloud service through Arduino and PHP

Cáceres¹,

More²,

Nima³

et al. 2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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Information technologies are the perfect medium for the development of multidisciplinary proposals that contribute to the development of digital media for the benefit of society on the tracking and monitoring of the weather. The objective of this research is to develop a prototype of a low-cost mini-weather station with cloud service through Arduino, PHP and MySQL. The methodological design comprises in the identification of technological resources, design, coding, testing and implementation. In this first approach the mini-meteorological station allows obtaining data on ambient temperature, relative humidity and atmospheric pressure. The tests were conducted during 2017,2018 and 2019 in order to assess the operability of the prototype in different seasons of the year. The data quality performance represents an accuracy of 93.76% for the environmental temperature variable, 99% on atmospheric pressure and 50% for relative humidity. It is recommended that a FC-37 rain sensor and aJL-FS2 anemometer to measure wind speed be integrated to the prototype in future research.

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Automotive Mechanical Vehicle Starter

Cited by 5 publications

References 10 publications

An Intelligent Risk Management Framework for Monitoring Vehicular Engine Health

An Intelligent Risk Management Framework for Monitoring Vehicular Engine Health

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

Prototype mini-weather station with cloud service through Arduino and PHP

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