Modelling Of Suspension And Motoring Force For Bearingless Permanent Magnet Synchronous Motor

Bearingless permanent magnet synchronous motor (PMSM) is the combination of the characteristic of conventional permanent magnet synchronous motor with magnetic bearing. It is a kind of high performance motor because having both advantages of PMSM and magnetic bearing such as no friction, high speed and long operating life. It is also suitable for high speed application such as compressor, turbines and pump. The purpose of this research is to modelling of motoring torque and suspension force for bearingless permanent magnet synchronous motor by using Maxwell 2D of ANSYS Finite Element Method (FEM). The designed bearingless PMSM consist of two sets of stator winding namely motoring torque winding and suspension force winding. Bearingless PMSM is developed by using the method of suspension force and the mathematical model of electromagnetic torque and suspension force. This mathematical model is built by using Simulink/Matlab and the other parameter values such as current, voltage, airgap length and force are identified. The relationship among configuration of windings, radial suspension force and current are complicated, so finding these relationship is important for modelling the bearingless PMSM. The final suspension force result obtained is compared between FEM and Matlab. Then by using Matlab, the controller for bearingless PMSM is developed to realize the controllable of rotor that consist of position controller and speed controller. This research covered the principle of suspension force, the mathematical model, Proportional Intergral (PI) control system of bearingless PMSM and also FEM analysis. Finally, the recommendation for future research studies is included to improve the research on bearingless PMSM

Analysis of controllers in suppressing the structural building vibration

Two degree of freedom (2 DOF) mass spring damper system is used in representing as building structure that dealing with the earthquake vibration. The real analytical input is used to the system that taken at El Centro earthquake that occurred in May 1940 with magnitude of 7.1 Mw. Two types of controller are presented in controlling the vibration which are fuzzy logic (FL) and sliding mode controller (SMC). The paper was aimed to improve the performance of building structure towards vibration based on proposed controllers. Fuzzy logic and sliding mode controller are widely known with robustness character. The mathematical model of two degree of freedom mass spring damper wasis derived to obtain the relationship between mass, spring, damper, force and actuator. Fuzzy logic and sliding mode controllers were implemented to 2 DOF system to suppress the earthquake vibration of two storeys building. Matlab/Simulink was used in designing the system and controllers to present the result of two storeys displacement time response and input control voltage for uncontrolled and controlled system. Then the data of earthquake disturbance was taken based on real seismic occurred at El Centro to make it as the force disturbance input to the building structure system. The controllers proposed would minimize the vibration that used in sample earthquake disturbance data. The simulation result was carried out by using Matlab/Simulink. The simulation result showed sliding mode controller was better controller than fuzzy logic. In specific, by using the controller, earthquake vibration can be reduced

Building a Smart Gardening System and Plant Monitoring Using IoT

Gardening activities usually required a lot of time and gardeners may face varieties of problems such as sticking to the designated watering schedule. Thus, this paper intends to solve these problems by introducing the Smart Gardening System. By using this system, users will be able to control and monitor the watering schedule and the sufficiency of lights while ensuring that the plants are taken care of accordingly. The smart gardening system is different from the normal gardening products that are already available in the market because of the implementation of Internet of Things (IoT) in the system to facilitate the work for its users. By using this home-based system, users can set the watering and lighting schedule automatically by using the designated application via smartphone. Besides, users will also be notified on the moisture level of soil, light-exposure, and the water level in water tank through the application. This will allow the users to monitor the watering system and only come to refill it when the water tank is empty. This system is an advantage as it can run automatically. From this research, the benefit of smart gardening system is proven via the execution of IoT which requires less human intervention for the system to operate. Moreover, the sensors are used to gather and update all the data that is convenience to the user to keep updated on the parameter and information about the plant in a real time without physical present

Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach

An accurate image retrieval technique is required due to the rapidly increasing number of images. It is important to implement image annotation techniques that are fast, simple, and, most importantly, automatically annotate. Image annotation has recently received much attention due to the massive rise in image data volume. Focusing on the agriculture field, this study implements automatic image annotation, namely, a repetitive annotation task technique, to classify the ripeness of oil palm fruit and recognize a variety of fruits. This approach assists farmers to enhance the classification of fruit methods and increase their production. This study proposes simple and effective models using a deep learning approach with You Only Look Once (YOLO) versions. The models were developed through transfer learning where the dataset was trained with 100 images of oil fruit palm and 400 images of a variety of fruit in RGB images. Model performance and accuracy of automatically annotating the images with 3500 fruits were examined. The results show that the annotation technique successfully annotated a large number of images accurately. The mAP result achieved for oil palm fruit was 98.7% and the variety of fruit was 99.5%

Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: a review

settingsOrder Article Reprints Open AccessReview Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review by Normaisharah Mamat 1,Mohd Fauzi Othman 1ORCID,Rawad Abdoulghafor 2,*ORCID,Samir Brahim Belhaouari 3,*ORCID,Normahira Mamat 4 andShamsul Faisal Mohd Hussein 1 1 Department of Electronic System Engineering, Malaysia-Japan International Institute of Technology, University Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia 2 Computational Intelligence Group Research, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia 3 Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Education City, Doha P.O. Box 34110, Qatar 4 Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau 02600, Malaysia * Authors to whom correspondence should be addressed. Agriculture 2022, 12(7), 1033; https://doi.org/10.3390/agriculture12071033 Received: 10 June 2022 / Revised: 3 July 2022 / Accepted: 4 July 2022 / Published: 15 July 2022 (This article belongs to the Section Agricultural Technology) Download Browse Figures Versions Notes Abstract The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed

Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach

An accurate image retrieval technique is required due to the rapidly increasing number of images. It is important to implement image annotation techniques that are fast, simple, and, most importantly, automatically annotate. Image annotation has recently received much attention due to the massive rise in image data volume. Focusing on the agriculture field, this study implements automatic image annotation, namely, a repetitive annotation task technique, to classify the ripeness of oil palm fruit and recognize a variety of fruits. This approach assists farmers to enhance the classification of fruit methods and increase their production. This study proposes simple and effective models using a deep learning approach with You Only Look Once (YOLO) versions. The models were developed through transfer learning where the dataset was trained with 100 images of oil fruit palm and 400 images of a variety of fruit in RGB images. Model performance and accuracy of automatically annotating the images with 3500 fruits were examined. The results show that the annotation technique successfully annotated a large number of images accurately. The mAP result achieved for oil palm fruit was 98.7% and the variety of fruit was 99.5%

Seismic vibration suppression of a building with an adaptive nonsingular terminal sliding mode control

This study investigates the control performance of a structural building system during a seismic scenario using an adaptive nonsingular terminal sliding mode control. To realize the structural integrity of a building, it is necessary to equip the building with a structural control device. This research is focused on a hybrid control device that has excellent characteristics of passive and active control devices and implemented in a three degree-of-freedom system. The system, actuator, and controllers are designed by using the mathematical model developed in MATLAB/Simulink. The input excitation to the structure is taken from the El Centro earthquake that occurred in the 1940s with a magnitude of 6.9 Mw and the Southern Sumatra earthquake that occurred in 2007 with a magnitude of 8.4 Mw. Adaptive nonsingular terminal sliding mode control is the new proposed control strategy to be applied in structural control field is investigated in terms of controller performance in suppressing the vibrations, and then, compared with sliding mode control and fuzzy logic controller strategies. Sliding mode control is chosen to be compared with adaptive nonsingular terminal sliding mode control because of its advantages of robust performance, whereas fuzzy logic controller is chosen because of its intelligent control base. The effectiveness of the proposed controllers is evaluated based on the displacement response, performance indices, and the probability of building damage. The results have shown that the new proposed controller, an adaptive nonsingular terminal sliding mode control, reduced vibrations better and has superior performance compared with fuzzy logic controller and sliding mode control

Bearingless Permanent Magnet Synchronous Motor using Independent Control

Bearingless permanent magnet synchronous motor (BPMSM) combines the characteristic of the conventional permanent magent synchronous motor and magnetic bearing in one electric motor. BPMSM is a kind of high performance motor due to having both advantages of PMSM and magnetic bearing with simple structure, high efficiency, and reasonable cost. The research on BPMSM is to design and analyse BPMSM by using Maxwell 2-Dimensional of ANSYS Finite Element Method (FEM). Independent suspension force model and bearingless PMSM model are developed by using the method of suspension force. Then, the mathematical model of electromagnetic torque and radial suspension force has been developed by using Matlab/Simulink. The relation between force, current, distance and other parameter are determined. This research covered the principle of suspension force, the mathematical model, FEM analysis and digital control system of bearingless PMSM. This kind of motor is widely used in high speed application such as compressors, pumps and turbines

Analysis Of A Bearingless Permanent Magnet Synchronous Motor

Bearingless permanent magnet synchronous motor (BPMSM) is known as both conventional permanent magnet synchronous motor and traditional magnetic bearings. For the purpose of this paper, the design and analysis of BPMSM using a Matlab/Simulink and 2-D ANSYS Maxwell are presented. The principle of electromagnetic force is utilized to ensure a stable levitation operation of BPMSM. The BPMSM mathematical model that is established to represent the dynamic of motoring force and radial suspension force is simulated in Matlab/Simulink environment. The ANSYS Maxwell, as an accurate finite element method (FEM) software in solving static, frequency-domain, and time-varying electromagnetic and electric fields is used to assist the development of the BPMSM model before the integration with the control system is performed. The excitation of motoring winding and suspension winding are independently controlled through separate PI controller. The applications for BPMSM are suitable for high speed electrical machines such as compressors, turbines, pumps, and mixers