Showing 10 results for Egm
Sh. Shirvani Moghaddam, Z. Ebadi, V. Tabataba Vakili,
Volume 11, Issue 1 (3-2015)
Abstract
In this paper, a new combination of Minimum Description Length (MDL) or Eigenvalue Gradient Method (EGM), Joint Approximate Diagonalization of Eigenmatrices (JADE) and Modified Forward-Backward Linear Prediction (MFBLP) algorithms is proposed which determines the number of non-coherent source groups and estimates the Direction Of Arrivals (DOAs) of coherent signals in each group. First, the MDL/EGM algorithm determines the number of non-coherent signal groups, and then the JADE algorithm estimates the generalized steering vectors considering white/colored Gaussian noise. Finally, the MFBLP algorithm is applied to estimate DOAs of coherent signals in each group. A new Normalized Root Mean Square Error (NRMSE) is also proposed that introduces a more realistic metric to compare the performance of DOA estimation methods. Simulation results show that the proposed algorithm can resolve sources regardless of QAM modulation size. In addition, simulations in white/colored Gaussian noises show that the proposed algorithm outperforms the JADE-MUSIC algorithm in a wide range of Signal to Noise Ratios (SNRs).
S. R. Mousavi-Aghdam, M. R. Feyzi, N. Bianchi,
Volume 13, Issue 1 (3-2017)
Abstract
This paper presents analysis and comparative study of a novel high-torque three-phase switched reluctance motor (SRM) with magnetically isolated stator segments. In the proposed SRM, each segment has a concentric winding located on the center body of it and two diametrically opposite windings which form the motor phase. There are four salient poles in the stator segment. Two of them share their flux path in the center body of the segment. The rotor has a solid structure including twenty two salient poles. In this unique SRM, stator segments topology, number of the stator segments poles and the rotor poles, and angular distance of the stator segments are selected so that the motor properly operates in both directions. Two-phase design with different pole combination is also possible. During operation, there are short flux paths along two adjacent rotor poles and excited segment poles. Therefore, the proposed SRM has all benefits of the short flux path structures. The principle and fundamentals of the proposed SRM design are detailed in the paper. The motor is analysed using finite element method (FEM) and some comparisons are reasonably carried out with other SRM configurations. Finally, a prototype motor is built and experimental results validate the performance predictions in the proposed motor.
A. Jabbari,
Volume 14, Issue 3 (9-2018)
Abstract
Brushless permanent magnet surface inset machines are interested in industrial applications due to their high efficiency and power density. Magnet segmentation is a common technique in order to mitigate cogging torque and electromagnetic torque components in these machines. An accurate computation of magnetic vector potential is necessary in order to compute cogging torque, electromagnetic torque, back electromotive force and self/mutual inductance. A 2D analytical method for magnetic vector potential calculation in inner rotor brushless segmented surface inset permanent magnet machines is proposed in this paper. The analytical method is based on the resolution of Laplace and Poisson equations as well as Maxwell equation in a quasi- Cartesian polar coordinate by using sub-domain method. One of the main contributions of the paper is to derive an expression for the magnetic vector potential in the segmented PM region by using hyperbolic functions. The developed method is applied on the performance computation of two prototype surface inset magnet segmented motors with open circuit and on load conditions. The results of these models are validated through FEM method.
A. Jabbari,
Volume 17, Issue 1 (3-2021)
Abstract
In this paper, we present a semi-analytical model for determining the magnetic and electromagnetic characteristics of spoke-type permanent magnet (STPM) machine considering magnet segmentation and finite soft-material relative permeability. The proposed model is based on the resolution of the Laplace’s and Poisson’s equations in a Cartesian pseudo-coordinate system with respect to the relative permeability effect of iron core in a subdomain model. Two different magnet-segmented STPM machine was studied analytically and numerically. The effect of the iron core relative permeability on the STPM machine performances was investigated at no-load and on-load conditions with respect to certain values of iron core relative permeability by comparing cogging torque, electromagnetic torque ripple, and reluctance torque ripple waveforms. In order to validate the results of the proposed analytical model, the analytical and numerical results were compared. It can be seen that the analytical modeling results are consistent with the results of numerical analysis.
Ali Jabbari, Ali Badran,
Volume 19, Issue 3 (9-2023)
Abstract
Cost reduction, increased efficiency and reliability, extended service life, reduced noise and vibration, and environmental friendliness are critical for new generation wind turbines and electric vehicles. Segmented Hybrid Permanent Magnet (SHPM) machines, on the other hand, which are primarily segmented PMs combined with different materials, dimensions, and magnetization directions, offer a way to meet these needs. In this study, we present nine topologies of segmented PM-rotor SHPM generators based on the Taguchi experimental design method, while presenting a simple and accurate model based on subdomain method for estimating the magnetic performance characteristics of SHPM machines. An analytical model is provided. Magnetic partial differential equations (MPDEs) are represented in a pseudo-Cartesian coordinate system, and with appropriate boundary conditions (BC) and interface conditions (IC), the general solution and its Fourier coefficients are extracted using a variable separation approach. The performance characteristics of nine of the SHPM machines studied were compared semi-analytically and numerically. Two prototype SHPM machines were manufactured and semi-analytical modeling results were compared with finite element analysis (FEA) methods and experimental testing (load mode) on a generator. The FEA simulation and experimental test results have a maximum error rate of about 3, confirming the high accuracy of the provided semi-analytical model. We compare the induced voltage, torque ripple and magnetic torque among the investigated topologies.
Priyanka Handa, Balkrishan Jindal ,
Volume 20, Issue 1 (3-2024)
Abstract
The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios. The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory.
Haniye Merrikhi, Hossein Ebrahimnezhad,
Volume 20, Issue 4 (11-2024)
Abstract
Robots have become integral to modern society, taking over both complex and routine human tasks. Recent advancements in depth camera technology have propelled computer vision-based robotics into a prominent field of research. Many robotic tasks—such as picking up, carrying, and utilizing tools or objects—begin with an initial grasping step. Vision-based grasping requires the precise identification of grasp locations on objects, making the segmentation of objects into meaningful components a crucial stage in robotic grasping. In this paper, we present a system designed to detect the graspable parts of objects for a specific task. Recognizing that everyday household items are typically grasped at certain sections for carrying, we created a database of these objects and their corresponding graspable parts. Building on the success of the Dynamic Graph CNN (DGCNN) network in segmenting object components, we enhanced this network to detect the graspable areas of objects. The enhanced network was trained on the compiled database, and the visual results, along with the obtained Intersection over Union (IoU) metrics, demonstrate its success in detecting graspable regions. It achieved a grand mean IoU (gmIoU) of 92.57% across all classes, outperforming established networks such as PointNet++ in part segmentation for this dataset. Furthermore, statistical analysis using analysis of variance (ANOVA) and T-test validates the superiority of our method.
Nurul Hidayah Rodzuan, Ili Najaa Aimi Mohd Nordin, Ahmad ‘athif Mohd Faudzi, Noraishikin Zulkarnain, Muhammad Rusydi Muhammad Razif, Nik Normunira Mat Hassan, Muhamad Hazwan Abdul Hafidz,
Volume 21, Issue 2 (6-2025)
Abstract
Rehabilitation devices like assistive gloves require bending-type soft actuators for controlled, repetitive finger movements essential for therapy. However, non-segmented actuators often struggle to replicate natural finger articulation, which can cause discomfort and reduce patient compliance. This paper presents the design and assembly of a segmented bending pneumatic soft actuator to achieve index finger flexion, aiming to improve comfort and support natural finger movement at low pressure. The actuator is integrated into a glove with a flexible bend sensor to measure the flexion angle of the metacarpophalangeal joint. Ecoflex 0-50 A-B silicone rubber is used in the fabrication, with air bubbles removed to ensure consistent actuator performance. The study investigates the actuator's performance and the sensor's ability to accurately measure joint flexion. The results, presented through detailed graphs, analyze the actuator’s flexibility, bending, and elongation under different pressure scenarios, offering insights into its effectiveness in improving patient comfort, joint articulation, and rehabilitation outcomes.
Humairah Mansor, Shazmin Aniza Abdul Shukor, Razak Wong Chen Keng, Nurul Syahirah Khalid,
Volume 21, Issue 2 (6-2025)
Abstract
Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings.
Mahdi Khourishandiz, Abdollah Amirkhani,
Volume 21, Issue 3 (8-2025)
Abstract
Protecting privacy in street view imagery is a critical challenge in urban analytics, requiring comprehensive and scalable solutions beyond localized obfuscation techniques such as face or license plate blurring. To address this, we propose a novel framework that automatically detects and removes sensitive objects, such as pedestrians and vehicles, ensuring robust privacy preservation while maintaining the visual integrity of the images. Our approach integrates semantic segmentation with 2D priors and multimodal data from cameras and LiDAR to achieve precise object detection in complex urban scenes. Detected regions are seamlessly filled using a large-mask inpainting technique based on fast Fourier convolutions (FFC), enabling efficient generalization to high-resolution imagery. Evaluated on the SemanticKITTI dataset, our method achieves a mean Intersection over Union (mIoU) of 64.9%, surpassing state-of-the-art benchmarks. Despite its reliance on accurate sensor calibration and multimodal data availability, the proposed framework offers a scalable solution for privacy-sensitive applications such as urban mapping, and virtual tourism, delivering high-quality anonymized imagery with minimal artifacts.