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Plasma technology has become of great interest in a wide variety of industrial and domestic applications. Moreover, the application of plasma in the domestic field has increased in recent years due to its applications to surface treatment and disinfection. In this context, there is a significant need for versatile power generators able to generate a wide range of output voltage/current ranging from direct current (DC) to tens of kHz in the range of kVs. The purpose of this paper is to develop a highly versatile power converter for plasma generation based on a multilevel topology.

This paper proposes a versatile multilevel topology able to generate versatile output waveforms. The followed methodology includes simulation of the proposed architecture, design of the power electronics, control and magnetic elements and test laboratory tests after building an eight-level prototype.

The proposed converter has been designed and tested using an experimental prototype. The designed generator is able to operate at 10 kVpp output voltage and 10 kHz, proving the feasibility of the proposed approach.

The proposed converter enables versatile waveform generation, enabling advanced studies in plasma generation. Unlike previous proposals, the proposed converter features bidirectional operation, allowing to test complex reactive loads. Besides, complex waveforms can be generated, allowing testing complex patterns for optimized cold-plasma generation methods. Besides, unlike transformer- or resonant-network-based approaches, the proposed generator features very low output impedance regardless the operating point, exhibiting improved and reliable performance for different operating conditions.

Induction heating processes need to adapt to complex geometries or variable processes that require a high degree of flexibility in the induction heating setup. This is usually done using complex inductors or adaptable resonant tanks, which leads to costly and constrained implementations. This paper aims to propose a multi-level, versatile power supply able to adapt the output to the required induction heating process.

This paper proposes a versatile multilevel topology able to generate versatile output waveforms. The methodology followed includes simulation of the proposed architecture, design of the power electronics, control and magnetic elements and laboratory tests after building a 10-level prototype.

The proposed converter has been designed and tested using an experimental prototype. The designed generator is able to operate at 1 kVpp and 100 A at 250 kHz, proving the feasibility of the proposed approach.

The proposed converter enables versatile waveform generation, enabling advanced tests and processes on induction heating system. The proposed system allows for multifrequency generation using a single inductor and converter, or advanced tests for inductive and capacitive components used on induction heating systems. Unlike previous multifrequency proposals, the proposed generator enables a significantly improved versatility in terms of operational frequency and amplitude in a single converter.

This paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.

The aim of this paper is to introduce an accurate asymmetric fault index for the diagnosis of the faulty linear permanent magnet Vernier machine (LPMVM).

Three-dimensional finite element method is applied to model the LPMVM. The geometrical and physical properties of the machine, the effect of stator and translator teeth, magnetic saturation of core and nonuniform air gap due to asymmetric fault are taken into account in the simulation. The air gap asymmetric fault is proposed. This analytical method estimates the air gap flux density of an LPMVM.

This paper presents an analytical method to predict the performance of a healthy and faulty LPMVM. The introduced index is based on the frequency patterns of the stator current. Besides, the robustness of the index in different loads and fault severity is addressed.

Introducing index for air gap asymmetry fault diagnosis of LPMVM.

The purpose of this paper is to exploit the optimal performances of each magnetic material in terms of low iron losses and high saturation flux density to improve the efficiency and the power density of the selected motor.

This paper presents a study to improve the power density and efficiency of e-motors for electric traction applications with high operating speed. The studied machine is a yokeless-stator axial flux permanent magnet synchronous motor with a dual rotor. The methodology consists in using different magnetic materials for an optimal design of the stator and rotor magnetic circuits to improve the motor performance. The candidate magnetic materials, adapted to the constraints of e-mobility, are made of thin laminations of Si-Fe nonoriented grain electrical steel, Si-Fe grain-oriented electrical steel (GOES) and iron-cobalt Permendur electrical steel (Co-Fe).

The mixed GOES-Co-Fe structure allows to reach 10 kW/kg in rated power density and a high efficiency in city driving conditions. This structure allows to make the powertrain less energy consuming in the battery electric vehicles and to reduce CO₂ emissions in hybrid electric vehicles.

The originality of this study lies in the improvement of both power density and efficiency of the electric motor in automotive application by using different magnetic materials through a multiobjective optimization.

This paper aims to develop a money laundering risk management model for multiple-purpose financial institutions (SOFOMES, Spanish acronym for “Sociedades Financieras de Objeto Múltiple”) based on the best international practices.

A study of a sample of several SOFOMES is carried out through representative surveys and focus groups to collect information to develop a causal model of risk management under a Bayesian network approach together with a Monte Carlo simulation.

The probability that SOFOMES has a high incidence to be used as a mean of money laundering is 29.3%, correspondingly with a probability of 33.1% of having medium incidence and 37.4% of low incidence.

Only nine SOFOMES were willing to provide information for this study.

In Mexico, there is a large registry in the Ministry of Finance and the Attorney General’s Office of this type of practices in the SOFOMES sector, impacting tax collection and affecting the growth of the real sector. The proposed model serves to establish several preventive policies that reduce the incidence of this type of crime.

As far as the authors know, there is no other study as this one in Mexico or in the rest of the world.

This paper aims to describe several artificial intelligence (AI)-based applications that librarians can use to serve and design virtual library instruction, so it will be more effective and efficient.

The approach involves a comprehensive review of AI-based applications that bring benefits to librarian to enhance the virtual instructional services (AI). This study explores the existing papers to reveal the potential use of AI for research consultation, designing the instructional services and conducting evaluation of the program.

There are some AI-based applications that are available for free that will help instructional librarian jobs. Librarians use the AI to increase effectiveness of the services. The AI-based applications that can be used to support instructional services on research inquiries include virtual assistance, knowledge mapping and note making, and to support designing virtual instruction, librarians can use design apps, image generators, voice generator, grammar checker and paraphrasing.

There are many studies on AI at the library; however, it’s still rare a paper studied AI-based application that potentially will bring benefit for virtual instructional services. This paper will give overview of AI application that will help instructional librarian on transactions with users and help librarians to create innovative instructional media.

This paper aims at developing an improved method, based on binary search algorithm (BSA) for the steady-state analysis of self-excited induction generators (SEIGs), which are increasingly used in wind energy electric conversion systems. The BSA is also compared with linear search algorithm (LSA) to bring out the merits of BSA over LSA.

All the parameters of SEIG, including the varying core loss of the machine, have been considered to ensure accuracy in the predetermined performance values of the set up. The nodal admittance method has been adopted to simplify the equivalent circuit of the generator and load. The logic and steps involved in the formulation of the complete procedure have been illustrated using elaborate flowcharts.

The proposed approach is validated by the experimental results, obtained on a three-phase 240 V, 5.0 A, 2.0 kW SEIG, which closely match with the corresponding predicted performance values. The analysis is shown to be easy to implement with reduced computation time.

A novel improved and simplified technique has been formulated for estimating the per unit frequency (a), magnetizing reactance (X_m) and core loss resistance (R_m) of the SEIG using the nodal admittance of its equivalent circuit. The accuracy of the predetermined performance is enhanced by considering the SEIG’s varying core loss. Only simple MATLAB programming has been used for adopting the algorithms.

The purpose of this paper is to modernize the generator system of wind turbine concept that not only improves the efficiency and power density but also reduces the system cost making design simpler and less expensive, especially in large-scale production.

This paper presents a new permanent magnet transverse flux generator (PMTFG) for wind energy production. The key feature of its composition is the double armature coil in a semi-closed stator core. The main structural difference of the presented design is the use of double coil in the same space of semi-closed stator core and reduced number of stator pole pairs and rotor magnets from 12/24 to 10/20. 3D simulations are performed using finite element analysis (FEA) to measure induced voltage and magnetic field distribution at no load. The FEA is performed to quantify the change in flux linkage, induced voltage and output power as a function of different speeds and load current.

Results show that PMTFG with double coil configuration has improved electromagnetic performance in terms of flux linkage, induced voltage, output power and efficiency. The power density of 10/20 PMTFG with the double coil is 0.0524 KW/Kg, about an 18% increase compared to the conventional design.

The proposed PMTFG is highly recommended for direct drive applications such as wind power.

Four models are simulated by FEA with single and double coil configuration, and load analysis is performed on all simulated models. Finally, results are compared with conventional PMTFG.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.