Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the inductive link parameter variations on the wireless power transfer system efficiency

In our days, all portable electronic devices require energy to operate properly. Batteries' short lifetimes that supply these devices are a major disadvantage for their good use. The problem becomes even more delicate when it comes to medical implants, for surgical operations necessity to replace the battery after each life cycle. Therefore, Wireless Power Transfer (WPT) has been emerged as a promising solution to overcome any health problem or any dangerous side effect caused by surgery. In this work, we have designed and implemented two WPT systems using the Resonant Inductive Coupling (RIC) technique to ensure WPT. Each system contains a transmitter and a receiver circuit. The difference is that we propose to vary several inductive link parameters from one system to another like the supply voltage, the coils design and the transfer distance between coils. The objective behind these experiences is to investigate the influence of the inductive link parameter variations on the power transfer efficiency. Experimental results have highlighted that inductive link parameter variations largely influence the power transfer efficiency between the transmitter and the receiver coil.
Distributed Formation Flight Control With Translational and Rotational Maneuvering

This paper investigates a leader-following formation control problem with translational and rotational maneuvers for a group of missiles in 3D space. Benefit from the displacements defined in neighbors’ relative coordinate frames in the distributed control law, the whole formation is able to track the leader’s trajectory with translational movement and rotation after the desired formation shape is formed. By incorporating command filters into the backstepping-based control scheme, the flight state constraints and overload saturations can be satisfied. It is shown that the proposed control algorithm ensures cooperatively semi-globally uniformly ultimate boundedness of the formation tracking errors irrespective of unknown bounded disturbances and the tracking errors can be further decreased by properly choosing the control parameters. Finally, two numerical simulation examples are provided to validate the effectiveness of the proposed control algorithm.
On the Fusion of Energy Resolved Scatter and Attenuation Data for Limited-View X-Ray Materials Characterization With Application to Security Screening

Motivated by the use of X-rays for security screening, we demonstrate the utility of fusing energy-resolved observations of scattered photons with traditional attenuation data for the joint recovery of electron density and photoelectric coefficient in the context of limited-view tomographic imaging scenarios. We begin by developing a physical and associated numerical model for the scatter physics used within our processing. With this model, in this paper, we propose a variational approach for recovering the two material properties. In addition to the typical data-fidelity terms, the optimization functional includes regularization for both the electron density and photoelectric coefficients. We consider an edge-preserving method in the case of electron density. We employ a nonlocal regularization scheme that improves the stability with which we can recover the photoelectric coefficient. Imaging results using Monte-Carlo simulations demonstrate both qualitative and quantitative advantages associated with supplementing traditional attenuation data with scatter observations for mapping the two material properties. The work in this paper has focused on providing an initial proof-of-concept concerning the potential utility in fusing observations of scattered and attenuated photons. Furthermore, enhancements are required to adapt this method for use in deployed security screening systems.
An adaptive Fuzzy-PID control of a lower limb active prosthesis under external perturbations

There are numerous difficulties in designing a prosthetic device such as an artificial limb to mimic a real human limb. One of the prior challenges is the stability of the lower limb prosthesis under the physical constraints of real terrain. The prosthetic devices designed under laboratory conditions are limited in their applicability to real-life environments. In this work external perturbations applied to a real foot are taken into account and incorporated in the Ankle-Knee Active prosthesis dynamic model such as vertical and horizontal ground reaction forces and external perturbation applied to the ankle joint from uneven terrain. A combination of fuzzy logic with a PID controller is, then, implemented to control the designed model under these external perturbations during a normal walking gait cycle.
Efficiency and Productivity Change Evaluation of Paraná’s Credit Unions

The use of tools which can aid in the productive units evaluation may become a key-factor for the companies management, resulting in an important competitive advantage. The goal of this paper is to evaluate Paraná’s credit union’s efficiency and productivity change. For that, it was used Data Envelopment Analysis (DEA), Principal Components Analysis (PCA) and Malmquist Index (MI) techniques. The analysis considered 45 units (each credit union researched), each of one with 10 variables in each period (8 inputs and 2 outputs). This evaluation has taken into account quarterly credit union’s data, from January 2009 to July 2015 (27 periods). The results showed that DMUs 453, 498 and 517 were considered 100% efficient in all periods, considering them as benchmarks. There was no case that a DMU was not considered 100% efficient in at least one observation. The MI showed that the difference between the biggest and the smallest average was significant (varying between 19.837 for DMU 251 and 0.926 for DMU 450). The average between all MI was 4.735 with a standard deviation of 3.547, evidencing the different measures of efficiency between each DMU when compared to the others.
Real-Time FPGA-Based Hardware Neural Network for Fault Detection and Isolation in More Electric Aircraft

More Electric Aircraft (MEA) has a great development prospect in the aviation industry thanks to the progressive power electronics technology and digital systems. Nevertheless, any fault occurring in the power electric system of MEA could be a fatal risk for the safety of the aircraft. To deal with the real-time fault detection and isolation (FDI) on the MEA, we put forward an FPGA-based neural network method which includes two stages: off-line construction using TensorFlow and real-time monitoring on the FPGA. Long Short-Term Memory (LSTM) network is applied because of its capability of learning from the long-term historical information in time series. A comprehensive MEA model based on the Boeing 787 power system created in PSCAD/EMTDC® and a commercial electric aircraft model based on Airbus E-Fan in Simscape are simulated to validate the effectiveness and generality of our proposed method. Adequate contrast experiments are conducted to acquire the most applicable architecture and configurations of the network. The results illustrate that the evaluation of the real-time condition can achieve accuracy over 99.5% within one sampling time on FPGA and reasonable hardware resource utilization.
Investigation of a moderate cortical model synchronization created using BRIAN simulator

Obtaining computational models for the functioning of the brain gives us a chance to understand the brain functionality thoroughly. This would help the development of better treatments for neurological illnesses and disorders. We created a cortical model using python language using BRIAN simulator. The dynamic connection model has multiple parameters to ensure an accurate simulation. We concentrated on the connection weights and studied their effect on the interactivity and connectivity of the cortical neurons in the same cortical layer and across multiple layers. As synchronization helps us to measure the degree of correlation between two or more neuronal groups, the synchronization between neuronal groups, which are connected across layers are considered.
Cache-Decision Policy using User Tastes

In recent years, the majority of internet traffic is video traffic such as YouTube and NetFlix, and reducing video traffic is an essential subject for network providers. Placing copy of original video contents in replication servers close to users has a chance to reduce the traffic in the networks. However, the storage capacity of a replication server is generally limited, so it is necessary to select video contents which will be requested in the near future. In the existing researches, temporarily or regionally popular contents are selected by focusing on the past popularity. In this paper, we would like to focus on another aspect of content request characteristics, user taste, and we propose to place contents which are not so popular but expected to be viewed by other users having similar tastes in the near future. Users with similar taste tend to request similar video contents, so we propose to select contents based on the measured tastes of users to place in the storage of replication servers. In the proposed method, cache storage is divided into two parts, a large static part and a small dynamic part. Cache-decision policy using user tastes is applied for the static part. In the dynamic part, LRU (Least Recently Used) cache-decision policy is applied to store contents having high popularity just after they are generated. The numerical results using MovieLens dataset show that the proposed method can make user groups with similar taste and improve the cache hit ratio compared with the LRU.
A Robust and Adaptive Control Method for Flexible-Joint Manipulator Capturing a Tumbling Satellite

A space manipulator needs to exhibit high stiffness for tracking, and good compliance for capturing a tumbling satellite. However, the elasticity caused by harmonic drives challenges both the high-precision motion and the flexibility of the manipulator. In this paper, we propose a novel control method based on a robust torque-tracking controller to eliminate the unexpected vibration resulting from elasticity and reject the perturbation caused by nonlinear friction of the motor. In order to achieve the high-precision motion of the manipulator with uncertain dynamics, we combined the method with an improved adaptive method. The position-based impedance control, taking both the translational and rotational impedance into account, is added to achieve the soft-capture of a tumbling satellite. The stability of the proposed method has been strictly proven by the Lyapunov method. The space manipulator is mounted on a controlled chaser, forming a free-flying space robot. A loose coordinated control between the chaser and the manipulator is adopted in the task simulation. The simulations for verifying controller performance and the task simulation reveal that the manipulator has high-precision motion performance in the pre-capture phase, achieves soft-capture in the capturing phase and offers a reliable connection between the chaser and the target in the de-tumbling phase.
An Improved Genetic Algorithm for the Scheduling of Virtual Network Functions

The scheduling of Virtual Network Functions (VN-Fs) is an important problem for Network Function Virtualization (NFV) resource allocation. In this paper, we investigate how to manage the Network Functions (NFs) efficiently to enhance the utilization of network resources. In the system model, we take into account the VNF transmission delay and processing delay at the same time. Our objective is to minimize the total end-to-end delay for all network services. To reduce the complexity of this issue, we propose a novel algorithm based on genetic algorithms by improving the method of crossover and mutation. The simulation results show that the proposed algorithm can reduce the total end-to-end delay at most 16.74%.
A Bridgeless Boost Half Bridge DC-DC Converter for Electrical and Hybrid Vehicle Applications

The present work is to propose an AC-DC converter as a battery charger for hybrid or electric vehicles based on the Boost Half Bridge (BHB) topology. Such a converter has as its main characteristics Bridgeless, it is a single stage, it has galvanic isolation, it allows to correct the power factor at the input of the converter, to promote zero voltage switching for each of its switches through the asynchronous pulse width modulation (A-PWM), in addition to having a reduced number of semiconductors. Furthermore, all the study and analysis, about the power structure, as well as the loop control and the implemented modulation will be presented in the following topics, in order to explain and prove the operation of the proposed structure.
A collocation scheme for deep uncertainty treatment

: Considering an uncertain correlation length of the input random ﬁelds described by a Karhunen-Loève expansion leads to a probability-box approach for the stochastic ﬁnite element computation. But, these computations are highly costly. Then, a stochastic collocation method using sparse grids within a Smolyak algorithm is proposed to reduce the computational cost, particularly in the context of non-linear computations. The interest and the development of the Smolyak algorithm for stochastic model with non-linear ﬁnite element methods regarding mixed, aleatory and epistemic, uncertain inputs are here introduced. The limitations of Smolyak algorithm are critically discussed and suggestions for improvement are made.
Human Exposure to Wireless Charging Systems for Electric Vehicles in Multistorey Carpark

This paper presents a study on the in-situ electric field strength inside the human body due to the exposure to low-frequency magnetic field generated by the wireless charging system (WCS) for electric vehicle inside a multistorey carpark. As the permeability of concrete is the same as that of air, the magnetic field generated by the WCS will penetrate through the floor under the primary coil, and hence result in an intense magnetic field downstairs. In this paper, the magnetic field strength, as well as the induced electric field strength inside a human body standing downstairs, are numerically assessed; the effect of the steel rebars on the dosimetry quantities is also investigated.
Deadline Effects in Regulatory Drug Review: A Methodological and Empirical Analysis

This paper elaborates two sets of statistical models for the analysis of regulatory review deadlines or “review time goals” and their influence upon regulatory decisions. In the first set, we elaborate dynamic duration models of time to decision, with particular focus on semi-parametric methods from which the behavioral structure of a regulatory review can be induced or “backed out. ” In the second set, we consider different estimators (generalized linear models with random effects, and two different matching estimators) for observational analysis of whether the deadlines in question influence the “quality” of the reviews or the likelihood of error. We apply these methods to the imposition of review-time goals by the Prescription Drug User-Fee Act (PDUFA) and its amended successors upon new drug review by the U.S. Food and Drug Administration (FDA). Using the first set of methods, we find broad evidence that the deadlines have induced a piling of approvals right before the deadline elapses. Using the second set of methods, and controlling for various correlates of drug quality and conditions of the review, we find that these “just-before-deadline” approvals are subject to substantially different post market experiences than drugs approved at other times in the review process. While our empirical analysis is focused upon drug approval in the United States, applications in other national settings and in many other regulatory policies are possible.
Testability Analysis in Neural Network Based Fault Diagnosis of DC-DC Converter

The importance of testability analysis in neural network based fault diagnosis of DC-DC converters is discussed in this paper. Theoretical fundamentals and an applicative example are presented, by taking into account the single-fault hypothesis. A program for testability analysis of switched DC-DC converters is used in the example. It relies on symbolic analysis techniques, which may be used to simulate switched circuits in a very easy and fast way.
Local volt/var Control Strategy for Smart Grids using Photovoltaic Smart Inverters

This paper presents further study for a new local volt/var control (VVC) methodology, using the smart inverters applied in the connection of photovoltaic (PV) distributed generation (DG) to the electric grid. The VVC is a highly beneficial functionality to an electrical distribution system, especially when considering its modernization, with further use of communication technologies and on-site generation. Thus, by using DG inverters on medium and low voltage levels, this system benefits from both those concepts, as it helps the voltage regulation by either absorbing or supplying reactive power to the distribution network. A modified IEEE 34 buses test grid is used to demonstrate the local VVC strategy with the addition of 8 photovoltaic generators. The results were obtained from the OpenDSS and MATLAB softwares for model control and simulation. The methodology was analyzed and tested considering two different cases: following the ABNT NBR 16149 standard and disregarding its limits.
Automatic Citrus Fruit Disease Detection by Phenotyping Using Machine Learning

It is really a frustrating occurrence of how fruit diseases can cause a reduction in production and the economy in the agricultural field all over the world. A growing body of research proves that fruits are critical to promoting good health. In fact, fruits should be the foundation of a healthy diet. This paper presents a better and modern proposed solution for the detection of fruit diseases using their physical attributes. The proposed methods are composed of K-Means clustering technique, ANN and SVM. K-Means is used for the image Segmentation. It has a function of mapping images to their corresponding disease classes based on the phenotypic characteristics such as the texture, color, structure of holes on the fruit and physical make-up. ANN (Artificial Neural Network) is pragmatic in achieving enhanced results in relations to the accuracy of detection and classification have some advantages over the other algorithms. They utilize quite little pre-processing in regards to other image classification methods. It implies the filters were studied by the network that in traditional methods were hand-engineered. This autonomy from earlier information and human exertion in feature design is a major advantage. A Support Vector Machine SVM also is used with the ANN to increase the high rate of classification. The proposed solution can significantly engineer precise detection and automatic classification of fruit diseases.
Literature Based Derivation of a Framework to Evaluate Engineering Change Requests

This paper describes a systematical approach to identify existing methods to evaluate engineering change requests in order to make better decisions on the approval or dismissal of those requests. The motivation to do this research originates in the recognition of practical problems in companies, which are missing suitable methods to evaluate risks and benefits of engineering change requests for their products. The scope of this investigation is focused on the "data-based decision support for engineering change requests", which has been published in academic literature. Therefore, a keyword search is used as a literature review technique. In this context, keywords are defined and complemented by synonyms to identify a higher amount of relevant literature. Different sources are covered within the investigation. The review does not include adjacent areas like organizational change and change management in terms of cultural changes but is focused on technical and design changes to products in all stages of their life cycle. After the identification of current methods, new possibilities of the evaluation of engineering change requests are discussed. Therefore, different requirements which should be fulfilled by new methods are described. In addition, areas for further research are identified. Therefore, a framework which describes the field of actions to improve decisions of engineering change requests is derived from the findings within the literature review.
Discussion about the Weather Impact on the Daily Outages in Urban Distribution System

In this paper, an evaluation approach for analyzing the weather’s impact on the number of daily outages in the urban distribution system is explored. By dividing the number of outages into two levels, the task could be carried out as a binary classification problem. In this study, the actual outage data from the distribution system operator is analyzed together with the local weather condition records. First, the tendency of different outage levels to weather conditions is described by the Principal Component Analysis (PCA). Then, the Support Vector Machine (SVM) algorithm is adopted to build the classification model for predicting the outage levels based on the weather condition. An oversampling method is introduced to manage the severe imbalance between the two outage levels. At the end, the performance of the classification model is assessed with the Receiver Operating Characteristic (ROC) curve.
Comparative Investigation of Bus-Clamping PFC Strategies for IM Drives

Various predictive torque control (PTC) strategies have been developed as powerful control schemes for the two-level B6 inverter fed induction motor (IM) drives. To achieve simultaneous control of the stator flux magnitude and the electromagnetic torque in conventional PTC scheme, the weighting factor for flux must be carefully adjusted as well as it has a great impact on the drive performance. Unfortunately, it is really a nontrivial process to obtain the optimized value of this weighting factor due to the lack of theoretical guideline. In order to cope with this problem, the predictive flux control (PFC) has been suggested based on removing the weighting factor from the implementation scheme. In addition to the conventional PFC (C-PFC), this paper proposes two bus-clamping PFC (BC-PFC) schemes where during each 60° sub-cycle, one stator phase is clamped to the positive or negative terminals of the dc-bus voltage. The performance of the three considered PFC strategies are evaluated based on several simulation results at transient and steady-state operations then by considering two comparison criteria, namely: (i) the average total harmonic distortion of the stator currents, and (ii) the average switching frequency.
Faster-Than-Nyquist Signaling for Physical Layer Security on Wireless Smart Grid

Wireless networks offer great flexibility and ease of deployment for the rapid implementation of smart grids. However, these data network technologies are prone to security issues. Especially, the risk of eavesdropping attacks increases due to the inherent characteristics of the wireless medium. In this context, physical layer security can augment secrecy through appropriate coding and signal processing. In this paper we consider the use of faster-than-Nyquist signaling to introduce artificial noise in the wireless network segment of the smart grid; with the aim of reinforce the information security at the physical layer. The results show that the proposed scheme can significantly improves the secrecy rate of the channel. Guaranteeing, in coexistence with other security mechanisms and despite the presence of potential eavesdroppers, a reliable and secure flow of information for smart grids.