The paper presents an approach to minimization of the cogging torque in permanent magnet (PM) machines using surface-mounted magnets with discrete skew angle. For the purpose of determining the proper arrangement of PM-pole slices, an optimization procedure based on a genetic algorithm is applied. The torque and objective function are determined from a simplified model for torque calculation only partially supported by three-dimensional (3-D) field solution. The results are validated against the 3-D finite-element model as well as experimental data obtained from a prototype machine. A new outer-rotor brushless dc motor motor for an electric fan is considered as a sample model.

Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets and omics to quickly interpret large datasets in order to generate health and disease indicators and to improve sampling and longitudinal analysis. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of apatient’s condition, treatment progress, and adjustments to the treatment plan), especially in the environments of smart cities and smart territories and through the wider use of 6G, blockchain (and soon maybe quantum cryptography), and the Internet of Things (IoT), as well as through medical technologies, such as multiomics. From a practical point of view, this requires not only efficient validation but also seamless integration with the existing healthcare infrastructure.

This paper provides a series of new results in both steady-state accuracy and frequency-domain analyses for two Laguerre-based approximators to the Grünwald-Letnikov difference. In a comparative study, the Laguerre-based approximators are found superior to the classical Tustin- and Al-Alaoui-based approximators, which is illustrated in simulation examples.

In this paper, controversial views on the use of an integer- and fractional-order derivatives in the theory and practice of electric circuits are discussed. Maxwell's equations are definitely useful in classical circuit analyses but empirical, fractional-order modeling is advocated in specific applications, including an exemplary supercapacitor charging circuit. Thus, both methodologies can be employed in various, application-specific modeling tasks.

This paper presents a new simple form of a polynomial matrix σ-inverse introduced as a result of research works on minimum variance control (MVC) for LTI MIMO nonsquare systems. A new approach to construction of a σ-inverse of a nonsquare polynomial matrix can result in e.g. pole-free design of MVC, which is provided by specially selected degrees of freedom of the σ-inverse. A simulation example in the Matlab ^® environment illustrates theoretical achievements of the paper.

Accurate prediction of power loss distribution within an electrical device is highly desirable as it allows thermal behavior to be evaluated at the early design stage. Three-dimensional (3-D) and two-dimensional (2-D) finite element analysis (FEA) is applied to calculate dc and ac copper losses in the armature winding at high-frequency sinusoidal currents. The main goal of this paper is showing the end-winding effect on copper losses. Copper losses at high frequency are dominated by the skin and proximity effects. A time-varying current has a tendency to concentrate near the surfaces of conductors, and if the frequency is very high, the current is restricted to a very thin layer near the conductor surface. This phenomenon of nonuniform distribution of time-varying currents in conductors is known as the skin effect. The term proximity effect refers to the influence of alternating current in one conductor on the current distribution in another, nearby conductor. To evaluate the ac copper loss within the analyzed machine a simplified approach is adopted using one segment of stator core. To demonstrate an enhanced copper loss due to ac operation, the dc and ac resistances are calculated. The resistances ratio ac to dc is strongly dependent on frequency, temperature, shape of slot and size of slot opening.