Wan Mohd Nooriman Wan Yahya
Thumbnail Image
Preferred name
Wan Mohd Nooriman Wan Yahya
Official Name
Wan Mohd Nooriman, Wan Yahya
Alternative Name
Nooriman, W. M.
Nooriman, Wan Mohd
Main Affiliation
Scopus Author ID
55628430700
Researcher ID
DJO-3055-2022

Research Output

Filters

5
5
Reset filters

Settings
Now showing 1 - 5 of 5
  • Publication
    Design and Development of IoT based Garbage Monitoring and Management System
    ( 2021-12-01)
    Wan Mohd Nooriman Wan Yahya
    ;
    Lim R.Y.
    ;
    Rudzuan M.N.
    ;
    Sofi Y.
    ;
    Fauzi M.M.
    ;
    Abu Hassan Abdullah
    Daily garbage production causes increasing in garbage management and cleansing cost which required an approach for better monitoring and management system to be applied. Currently, demand of IoT keep increasing as a part of Industrial Revolution 4.0 (IR4.0). Eventually, this IoT based Garbage Monitoring and Management System has been developed. For hygiene, self-opening dustbin lid is applied and IoT technology is used to integrate better garbage monitoring and management into an innovative and effective system. The development of this smart bin uses a pair of infrared sensors and an ultrasonic, push notifications were developed in Blynk application, and user-friendly infographic data is designed on webpage for monitoring and garbage management purposes. The smart dustbin notifies the user when the garbage level exceeds 80%. The dustbin sends a push notification to the user's phone to alert the user to take actions needed before the bin exceed the limit.
  • Publication
    Validation of Electrical Noise of a DC Motor through Controlled Varistor Cracking: An Experimental Study
    ( 2023-01-01)
    Mohd Rudzuan Mohd Nor
    ;
    Zainudin G.
    ;
    Wan Mohd Nooriman Wan Yahya
    ;
    Sofi Y.
    ;
    Nordiana S.
    ;
    Norlaili S.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Zuradzman Mohamad Razlan
    The varistor is an electronic component that protects the DC motor's circuitry from electrical noise or transients that can cause damage. It works as a voltage-dependent resistor that can change its resistance according to the applied voltage. Once the voltage surpasses a specific threshold, the varistor conducts and directs the excess voltage away from the motor's circuitry. In small DC motor manufacturing, ring varistors are vital for reducing electrical noise, minimizing spark-induced damage to the commutator and brush, and extending the motor's lifespan. Additionally, they prevent damage to electronic parts in the customer's mechanism set. The objective of this study is to investigate the impact of varistor cracks or chips that may occur during the soldering process of varistors to the commutator. To confirm the occurrence of cracks or chips, intentional damage will be inflicted on the varistors. The study aims to determine how the presence of cracked or chipped varistors affects the electrical noise produced by a DC motor during its operation. The resulting spark was observed through an oscilloscope, and it was found that the effect could be substantial, up to 5 to 10 times the rated voltage supplied to the motor. In the next phase of this study, further tests will be conducted on motors without varistors to provide a comparison.
  • Publication
    Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles
    ( 2021-12-01)
    Wan Mohd Nooriman Wan Yahya
    ;
    Abu Hassan Abdullah
    ;
    Norasmadi Abdul Rahim
    ;
    Erdy Sulino Mohd Muslim Tan
    Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia's state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree's demands is critical. The aim of this study is to create a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.
      1
  • Publication
    CFD Analysis of Pure Waterjet Nozzle for Fruit Peeling and Cutting Process
    ( 2024-01-01)
    Seran Y.
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Abdul Halim Ismail
    ;
    Kamarulzaman Kamarudin
    ;
    Lam Chee Kiang
    ;
    Norasmadi Abdul Rahim
    ;
    Wan Mohd Nooriman Wan Yahya
    ;
    Yew T.K.
    ;
    Min L.W.
    Waterjet Technology has been used vastly in our world nowadays due to its advantages and it can be implemented in many industrial sectors or even in the medical sector and food industry sector. Nozzle is a component that has been utilized in waterjet which is employed in a wide range of engineering applications to control the rate of flow, velocity, and the jet pressure of the water. This paper discusses the CFD analysis on a pure waterjet nozzle to obtain the output of the water that jets out from three different diameters of nozzle and select the effective nozzle diameter to be used for the fruit peeling and cutting process. The pressure used for the analysis are 200MPa, 300MPa and 400MPa, which was analysed for three different nozzle diameter 0.76mm, 1.02mm and 1.27mm. From CFD analysis, it is established that as the pressure loss of the water jet increases, the outlet velocity of the jet increases. Furthermore, for fruit peeling and cutting process the impact angle of the nozzle should be prioritised as the peeling of the fruit should be smooth and even before cutting the fruit. Thus, the most suitable parameters were found to be 400MPa and 1.02mm of pressure and nozzle diameter respectively. This will allow for the intended fruit cutting process with a stand-off distance that can be ranged from 1mm to 9mm.
      2
  • Publication
    Vision-Based Edge Detection System for Fruit Recognition
    ( 2021-12-01)
    Tan S.H.
    ;
    Lam Chee Kiang
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Halim Ismail
    ;
    Norasmadi Abdul Rahim
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Wan Mohd Nooriman Wan Yahya
    ;
    Sneah G.K.
    ;
    Seng M.L.
    ;
    Hai T.P.
    ;
    Lye O.T.
    There are variety of fruits around the world, different types of fruits contain different types of nutrients and vitamins which could benefits our health. In order to understand which fruit can provide specific type of nutrients, we need to identify the types of fruits. However, fruits grow in a different shape, colour and texture based on the country they were planted and the environment of the land. Implementing a machine vision-based recognition on the fruits can help people recognize them easily. In this paper, an edge detection method is applied using computer vision approach to recognize different types of fruits. The fruits are classified based on the features extracted from their images. In the experiment, a total of 450 images of three types of fruit are used, which are apples, lemons and mangoes. Pre-processing steps are applied on the captured image to improve the quality of fruit details and the edge features are extracted using Canny Edge Detection method. Classification of the fruits is accomplished using two different types of learning model, the deep leaning model, Convolution Neural Network (CNN) and machine learning model, Support Vector Machines (SVM). The performance of both classifiers is compared and the model with the best performance, SVM is chosen as the model for the system. The system can achieve 86% classification accuracy with the SVM model, which is good enough for fruit recognition.
      1