Cost reduction of any design process is always of interest for industries. Simulation work packages tackle this problem since they can quickly provide reliable results that permit detection of critical design issues prior to the prototype phase. A trade-off is then often made between model accuracy and computation speed. In the particular case of electric machines, homogenization techniques are used in order to keep high accuracy while running fast calculations. They are involved in multiple disciplines in which the machine performances are verified such as elec tromagnetic, mechanical, thermal and acoustic domains.This paper aims at defining whether these homogenization methods can be extended from one discipline to another by reviewing them independently of the physical domain.

Synchronous reluctance machines become more popular nowadays. Development of power electronics allowed reluctance motors to be used in many drive applications. Due to increased interest in electric vehicles and high prices of PMs, the research on electric drives is mainly focused on reluctance machines. Lack of rotor winding lowers copper losses, simplifies power supply system and increases machine's robustness. SynRM produces torque due to rotor's magnetic anisotropy, which is achieved in machines with transversally laminated rotor by introducing flux barriers. The main goal when designing rotor's flux barriers is to achieve the highest saliency ratio possible. However, since operation of the machine depends on interaction between stator magnetic field and the rotor, both stator and rotor's topologies should be taken into account when designing the machine. This paper presents a comparison of SynRMs with stators of three different slot numbers and two different rotor's topologies.

In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.

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