Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduction of Harmonic Content for Power Electronic Applications using Diode-Clamped-Multi Level Inverters

Multilevel Inverters have widespread industrial drive applications, HVDC and UPS. Multi-level Inverters also find major application in the Stand-alone and Grid- connected Systems. The conventional two-level inverters have some limitations in operating at high frequencies mainly due to switching losses and constraints in device rating. The problem is resolved by means of Diode- clamped multilevel inverters (MLIs) which utilize lower switching frequencies and give high voltages with improved total harmonic distortion (THD) without use of filter. This paper presents the principle of operation, analysis and Simulink implementation for three phase -5-level and three-phase -7-level Diode-clamped Multi Level Inverters. The comparison of 5-level and 7-level diode-clamped Multi level inverters is presented in terms of THD, Output Voltage.
Comparaison de mthodes d'identification des paramtres d'une machine asynchrone

Les algorithmes genetiques sont des methodes adaptatives de plus en plus utilisee pour la resolution de certains problemes d'optimisation. Le present travail consiste d'une part, a mettre en œuvre un A.G sur des problemes d'identification des machines electriques, et d'autre part a comparer ses performances avec les methodes classiques tels que la methode du maximum de vraisemblance et la methode electrotechnique basee sur des essais a vides et en court-circuit. Ces methodes sont appliquees sur des machines asynchrones de differentes puissances. Les resultats obtenus sont compares selon certains criteres, permettant de conclure sur la validite et la performance de chaque methode.
Deep Learning for Model Parameter Calibration in Power Systems

In power systems, having accurate device models is crucial for grid reliability, availability, and resiliency. Existing model calibration methods based on mathematical approaches often lead to multiple solutions due to the ill-posed nature of the problem, which would require further interventions from the field engineers in order to select the optimal solution. In this paper, we present a novel deep-learning-based approach for model parameter calibration in power systems. Our study focused on the generator model as an example. We studied several deep-learning-based approaches including 1-D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), which were trained to estimate model parameters using simulated Phasor Measurement Unit (PMU) data. Quantitative evaluations showed that our proposed methods can achieve high accuracy in estimating the model parameters, i.e., achieved a 0.0079 MSE on the testing dataset. We consider these promising results to be the basis for further exploration and development of advanced tools for model validation and calibration.
Noise-Resistant Algorithm for Sorting Cardio Complexes for the Task of Analyzing Statistical Characteristics of ECG

According to the WHO, cardiovascular diseases are the leading cause of death worldwide, so an accurate and timely diagnosis of cardiovascular diseases is an important task. One of the most common and effective methods for diagnosing CVD is to record an electrocardiogram using Holter monitoring. In order to significantly reduce the time required to decrypt the recording of ECS, cardiologists need special programs for automated analysis of the electrocardiogram. This is especially important for the long-term monitoring task. The program for the automated analysis of pacemakers should perform the clustering of cardiac complexes, thus dividing the electrocardiogram into groups of individual cardiac complexes. Only reference cardiocomplexes obtained by statistical averaging from each such group are subjected to further analysis. When registering an electrocardiogram, interference of various physical origins arises, artifacts that significantly complicate the analysis of ECS, therefore, automated analysis programs must also carry out preliminary processing of the electrocardiogram.
Reliability evaluation for meshed distribution networks based on minimal cut sets method

The paper proposes a reliability evaluation for meshed distribution networks based on minimal cut sets method. Distribution networks with meshed layout but radially operated have been commonly used in urban environments since they give some advantages in terms of lines thermal limit and voltage drop. The evaluation procedure is divided into three steps: firstly, it determines the minimal cut sets from each source node to each load nodes of the DN; secondly, it calculates the failure rate and the outage time for each minimal cut sets identified; lastly, it calculates the system average interruption frequency index and the system average interruption duration index. A sensitivity analysis of two specific system layouts, which we refer to as 1-step ladder and 2-steps ladder, has been carried out varying the maximum power flow allowed during normal operation. The method shows that, to improve reliability, as more as the distribution network is loaded as more as it should be meshed.
Similarity Attack on Cancelable Biometric Templates using Particle Swarm Optimization

Biometrics models use behavioral and physiological characteristics for automatically recognizing the identity of individuals. Since the biometric information is immutable, it needs to be stored in a secure way. Cancelable Biometrics (CB) is one such technique that is used for the purpose of biometric template protection. CB schemes typically utilize a transformation function to convert the biometric features into their corresponding cancelable forms. Although the cancelable templates are irreversible, the distance between two feature templates gets preserved during the transformation process. This similarity preserving property of CB causes a Similarity-based Attack (SA), in which the pre-images of the transformed features can be reproduced. In this paper, the applicability of Particle Swarm Optimization (PSO) is analyzed for performing the similarity-based attack on cancelable biometric templates. During our investigation, we have found that not every metaheuristic can be efficiently used for attacking the CB scheme. Furthermore, the performance of the metaheuristic varies depending on the underlying CB scheme. Hence our study provides a detailed outline of the applicability of PSO for launching a SA on a generic CB scheme.
Adaptive LMAT Control Approach for Grid Coupled SPV System

In this paper an adaptive least mean absolute third (LMAT) control for a 3-phase double stage solar photovoltaic (SPV) grid integrated system to improvise the quality of power. It consists of a DC-DC boost converter, a ripple filter, a VSC (Voltage source converter) and three phase loads. The VSC is coupled at DC-link for feeding SPV power into the utility grid with harmonics elimination of load currents and grid currents balancing. Here, the LMAT adaptive control technique is used to evaluate the fundamental weight of load current to provide the fast convergence. Here, for the solar PV generation, a feed-forward term is used to enhance the dynamic performance. Therefore, this adaptive LMAT control technique is tuned by a simple way. The harmonics in grid currents are shown under the IEEE-519 std.
Time integration methods for flexible pendulum with slider

This paper studies the four time integration methods for the simulation of flexible pendulum with slider. The equations of motion are formulated as differential–algebraic equation by applying augmented Lagrangian method. The classical Newmark time integrator and its modifications are used to solve second order differential and algebraic equations. The solutions and stability of applied integrators are illustrated by numerical tests.
Adaptive & Coordinated Traffic Signal System

Traffic congestion is a major issue all over the world. Despite numerous research the aforesaid problem has not optimally and sufficiently solved. In this research we introduce an adaptive traffic signaling system based on road traffic density to facilitate optimal traffic signal control as well as effective traffic management. We also proposed effective coordination of traffic. The proposed scheme uses live video as an input provided to a deep Q network to provide adaptive phase timings as the system output. Compared to the existing work, we introduced per car unit (PCU) as a novel input to represent the effect of each vehicle type on the traffic condition. Extensive tests on real time data amply proves that the proposed system enhances the average speed of traffic by 82% in a single junction scenario and 18% when considering the multiple junction scenario. Further research in progress to identify and accommodate emergency situations: accidents and high priority vehicle as well as to consider movement patterns of pedestrians as an input to the system.
Every Mutation Should Be Rewarded: Boosting Fault Localization with Mutated Predicates

Many fault localization (FL) techniques have been proposed to facilitate software debugging. Due to being lightweight, spectrum-based fault localization (SBFL) is one of the most popular FL families and widely deployed in program repair tools. SBFL ranks program elements by recording the program coverage under a test suite and calculates the suspiciousness score of each element with a ranking formula. Despite numerous formulae proposed, SBFL still suffers from providing no new sources of information other than program coverage. Mutation-based fault localization (MBFL) iteratively mutates a faulty program and suggests fault locations through mutants that overturn failed test cases. However, due to its explosive search space, MBFL has been adopted by only few program repair tools.In this paper, we aim at exploiting the advantages of MBFL and boosting SBFL with low overhead by building a practical FL tool. We propose Flip, an FL technique with inferences from mutated predicates. Based on SBFL, we leverage and extend the predicate switching technique to infer fault locations no matter whether the mutated predicate can overturn a failed test case or not. Finally, we compute a new ranking list with a joint inference that combines program coverage and mutation inferences.We use Defects4j (version 1.5.0), containing 438 real-world faults from six projects to evaluate Flip. All the seven stateof-the-art SBFL techniques benefit from Flip (e.g., by ranking up to 46.4% more faults in top-1) with low overhead (e.g., by incurring less than 2-minute average overhead for each fault). We also offer some insights on how to further improve FL on real-world faults based on the empirical results.
DelveFS - An Event-Driven Semantic File System for Object Stores

Data-driven applications are becoming increasingly important in numerous industrial and scientific fields, growing the need for scalable data storage, such as object storage. Yet, many data-driven applications cannot use object interfaces directly and often have to rely on third-party file system connectors that support only a basic representation of objects as files in a flat namespace. With sometimes millions of objects per bucket, this simple organization is insufficient for users and applications who are usually only interested in a small subset of objects. These huge buckets are not only lacking basic semantic properties and structure, but they are also challenging to manage from a technical perspective as object store file systems cannot cope with such directory sizes. DelveFS is the first object store file system that solves this challenge by offering the ability to compose a custom semantic file system that allows multiple unique views onto the object store. Through flexible filters, users can specify each view's content, tailored to their unique interests or an application's requirements. By processing object store events which describe changes in the object store, DelveFS is able to keep all views eventually consistent. DelveFS allows to operate concurrently through the object and file system interfaces on the same set of objects, delivering similar file system throughput compared to the native object store interfaces or other file system connectors. DelveFS is the first object store file system that solves this challenge by offering the ability to compose a custom semantic file system that allows multiple unique views onto the object store. Through flexible filters, users can specify each view's content, tailored to their unique interests or an application's requirements. By processing object store events which describe changes in the object store, DelveFS is able to keep all views eventually consistent. DelveFS allows to operate concurrently through the object and file system interfaces on the same set of objects, delivering similar file system throughput compared to the native object store interfaces or other file system connectors.
Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Rehabilitation robots play an increasingly important role in the recovery of motor function for stroke. To ensure a natural physical human-robot interaction (pHRI) and enhance the active participation of subjects, it is necessary for the robots to understand the human intention and cooperate actively with humanlike characteristics. This study proposed a hybrid active control algorithm with human motion intention detection. The motion intention was defined as the desired position and velocity, which were continuously estimated according to the human upper-limb model and minimum jerk model, respectively. The motion intention was then fed into a hybrid force and position controller of an upper-limb cable driven rehabilitation robot (CDRR). And a three-dimensional reaching task without predefined trajectory was employed to validate the effectiveness of the proposed control algorithm. Experimental results showed that the control algorithm could continuously recognize the human motion intention and enabled the robot better movement performance indicated as smaller offset error, smoother trajectory, and lower impact. The proposed method could guarantee a natural pHRI and improve the engagement of the subjects, which has great potential in clinical applications.
Hydrodynamic Modelling of AUV and Computational Study on Tail Geometry

Hydrodynamic effects are critical in the performance of an underwater vehicle. This paper mainly focuses on the hydrodynamic modelling of the Autonomous Underwater Vehicle (AUV) and the simulation study of the AUV with different tail angles and length using ANSYS Fluent. The tail angle of −45° to 45° and a tail length of 0 to 40% of the length of the hull are considered for simulation works. Computational studies are carried out with a constant velocity of 1 m/s with a realizable K-ε model. Finally, the results are compared with the increase in buoyancy of the vehicle to deduce an optimum tail length and angle for the AUV.
Design of Tunable Metasurface Using Deep Neural Networks for Field Localized Wireless Power Transfer

Wireless power transfer (WPT), a convenient method for powering multiple devices, enables a truly wireless connection, eliminating the need for periodic charging and replacing a battery. To further enhance WPT, the unique characteristics of metamaterial, such as its field focusing and evanescent wave amplification, have been successfully utilized. With subwavelength characteristics, computational challenges arise when the number of metamaterial unit cells is increased. In this work, we investigate a deep neural network (DNN)-based design of the tunable metamaterial for WPT. Using structures specifically designed for different tasks, the DNN predicts the frequency spectra and synthesizes the unit cell’s design parameters. When trained using a set of ~23000 randomly selected designs, we achieve an accumulated mean square error (MSE) of less than $1.5\times 10^{-3}$ for 97.3% of the 1929 test set. For synthesizing the unit cell’s design parameters, the MSE is less than $2.5\times 10^{-3}$ for 95.7% of the test set. The data-driven method is further extended to a generative adversarial network (GAN) to create the WPT paths and predict the frequency spectra of them. To achieve high efficiency, we propose a cost function focusing on the spectra’s transmission peak. After training using 80 000 measured data, the GAN can create WPT paths that efficiently connect the transmitter and the receiver on the metasurface. The results show that the DNN provides an alternative and efficient design method for the metamaterial, replacing traditional EM-simulation-based approaches.
Convolutional Neural Network Assisted Optical Orbital Angular Momentum Identification of Vortex Beams

The rapid and accurate identification of large-scale orbital angular momentum (OAM) modes is crucial for expanding the application of vortex beams (VBs). In this paper, an OAM mode recognition method based on convolutional neural networks (CNNs) is proposed and investigated. We construct an 8-layer CNN possesses complex feature extraction capability and train it to own powerful anti-turbulence competence by feeding the intensity patterns of VBs interfered by Gaussian beam. After supervised training of a large sample set, the CNN model takes on excellent network generalization ability and can well detect VBs with the mode range of [−50,50]. The simulation results indicate that under the influence of weak and medium turbulences, the average recognition accuracy exceeds 99%. Even under strong turbulence, the accuracy also reaches 98.54%. Meanwhile, the identification time is only 1.55ms per OAM mode with Intel(R) Xeon(R) Gold 6148 CPU. Moreover, the influence of different Gaussian beam waists, VB orders, input training sets, and CNN structures on OAM mode recognition performance, is fully studied. These results demonstrate that our proposed method can achieve higher accuracy and higher order OAM mode detection at a fast speed, which contributes a more effective method for the recognition of VBs.
Inter-Language Static Analysis for Android Application Security

The Android application market will conduct various security analysis on each application to predict its potential harm before put it online. Since almost all the static analysis tools can only detect malicious behaviors in the Java layer, more and more malwares try to avoid static analysis by taking the malicious codes to the Native layer. To provide a solution for the above situation, there's a new research aspect proposed in this paper and defined as Inter-language Static Analysis. As all the involved technologies are introduced, the current research results of them will be captured in this paper, such as static analysis in Java layer, binary analysis in Native layer, Java-Native penetration technology, etc.
DSVM based Predictive Torque Control of Induction motor for EV

This paper presents discrete space vector modulation (DSVM) based predictive torque control (PTC) of induction motor for Electric Vehicle (EV). In the conventional PTC, only seven voltage vectors are utilized for determination of flux and torque. In order to utilize the converter control region effectively without decreasing the sampling time of the controller and to decrease the ripple in the torque, flux and current, DSVM based PTC is applied for induction motor. According to the DSVM, each sampling time is divided into 3 equal parts and by applying standard voltage vectors totally 37 virtual voltage vectors are generated. These voltage vectors are used for flux and torque prediction. To decrease the computational burden of DSVM-PTC, number of voltage vectors used for the prediction is reduced by direct torque control (DTC) based sector selection method. The starting current of the motor is limited by a nonlinear function added in the cost function. The speed of the motor is estimated by rotor flux based speed estimation technique. Torque ripple, motor current THD are analysed and compared with conventional techniques in different modes of operation of the motor. The DSVM – PTC algorithm is evaluated in simulation and experimental platform, which gives excellent response even in low sampling frequency compared to conventional methods.
The Stimulus Transduction Artifact from Headphones in hdEEG during the ASSR Experiments: a Phantom Study

The auditory steady-state response is considered to be a biomarker of neuropsychiatric diseases in electroencephalography. Many studies use headphones to deliver a click train stimulus (40 Hz) to evoke the brain oscillations. However, headphones can generate a stimulus transduction artifact. The aim of this study is to investigate a stimulus transducer artifact due to click train stimulation. We further describe the influence of the artifact to final data analysis. We recorded hdEEG from a human head phantom to control the experimental conditions. The intertrial phase clustering was computed to evaluate phase locking across trials before and during stimulation by headphones and speakers (control condition). Results show, that headphones generated artifact in higher harmonic components of 40 Hz. Time-frequency analysis proves that the artifact doe to headphones is phase locked to stimulation onset. On the other hand, the speakers did not create this artifact. In future work, we will compare our results with results on human subjects.
Performance Evaluation of Four Grid-Forming Control Techniques with Soft Black-Start Capabilities

Grid-Forming Converters (GFC) can be controlled as independent, self-starting, voltage sources. This feature is essential for power converters to achieve successful black-start sequence initiation. Conventional grid-following converters are not capable of self-starting an islanded network. GFC control thus exploits wider grid support and network restart potential. This study analyzes and compares four GFC controllers to assess their generic and soft black-start (ramping voltage) capabilities. The compared techniques are: Droop Control, Power Synchronizing Control (PSC), Virtual Synchronous Machine (VSM), and Matching control. These techniques are selected based on their direct voltage reference control flexibility. Various simulations are performed with common parameters to assess the response of each technique under similar conditions against load, DC voltage and active power reference disturbances, in addition to their soft-start readiness. The results demonstrate the high-level compatibility of these four controllers with soft black-start through successful and timely ramping voltage reference tracking. Moreover, the four considered control techniques achieve satisfactory performance, with VSM demonstrating more flexibility due to its tunable virtual inertia parameter (J).
Implantable Wireless Transmission Rectenna System for Biomedical Wireless Applications

We introduce a novel RF to DC wireless power transmission implantable rectenna system at Industrial, Scientific and Medical bands (ISM 902.8-928 MHz). An embedded circular antenna receives the externally emitted energy from a patch antenna and converts it to DC power by a rectifying circuit. The system is associated with a metallic reflector which is placed behind the human body enhancing the reception. The rectifying circuit efficiency and the total system’s efficiency are determined for different external antenna to human body distances and various embedded depths with or without the reflector. Experimental results, obtained in a realistic environment, validate the predicted performance and provide an initial evaluation of the transmission link. Porcine tissue and bovine tissue are both used during the measurements and results are compared against simulations.