Miracle-dz/UniFAR-FTUs huggingface dataset:
Battery Cells Characterization for Subsequent Operation in Battery Models used in Mobile Charging Station Designing

This paper deals with a battery cell characterization, having a lithium iron phosphate (LiFeSO4) chemistry, based on first order electrical equivalent circuit (EEC), within the charging station application. More precise, the reliability of the method is taken into account in the context of a new mobile charging station development. The analysis is numerically and experimentally employed. The results between the simulation and real battery measurements are compared based on international standards and regulations which refer to Electrical Vehicle (EV) charging stations. The Li-Ion cell characterization model and simulation has been developed in Matlab/Simulink software and a dSpace/MicroLabBox realtime board for the measurements. A very good agreement was found between the simulated and tests obtained results.
Characterization at Logical Level of Magnetic Injection Probes

Intentional electromagnetic interference is an effective mean to jeopardize the security of integrated circuits. In this paper, we propose a new approach to evaluate the efficiency of magnetic probes used to radiate a disturbance: measuring its impact within the target of the attack, more precisely on the propagation delay of a combinational path. The characterization of five probes carried out using three different integrated circuits is reported. In all cases, bespoke, handmade probes outperform commercial ones. Experimental results also show that the electromagnetic coupling between the probes and the integrated circuits is mainly due to global, bonding wires.
Defect detection on EL images based on deep feature optimized by metric learning for imbalanced data

The defect detection based on machine vision is of great significance for improving the production efficiency and product quality of photovoltaic cells. The common problem in the field of defect detection is the unbalanced distribution between defective data and defect-free data because of the rarity of defective data. However, Convolutional Neural Network (CNN) cannot achieve better processing for unbalanced data sets. In this paper, we propose a novel feature extraction framework that learns the backbone CNN network parameters, optimizes feature space by metric learning in end-to-end training process, which solves the problem of over-fitting of CNN model on imbalanced and small-scale data sets. To verify the effectiveness of this method, extensive experiments on EL image dataset. The comprehensive results demonstrate that the proposed methods can identify the different type of defects with more accuracy rates than the state-of-the-art methods in several cases.
Epilepsy Classification in Ictal Stage From EEG Signals for Children

An electroencephalogram (EEG) is a recording of the electrical activity of the brain. EEG signals are mainly used for the detection and visualization of many neurological disorders. It is also used for the diagnosis of epilepsy. Epilepsy is a chronic disease and 1% of the world's population is affected. Epilepsy results in seizures associated with loss of consciousness, including long-term loss of consciousness and affecting patient quality of life. It is possible to determine the detection of epilepsy from EEG data. However, there are some difficulty to detect epilepsy because of the length and complexity of EEG. There are many studies for the classification and detection of epilepsy. Most of these studies are realized with artificial neural networks and machine learning. In this study, epilepsy detection was performed by using machine learning over EEG data with the features obtained by time-frequency domain analysis during seizure. As a result of the study, Logistic Regression classifier gave the best training results for 23 different EEG channels and tested. In the test results; 87.5% accuracy, 75.0% sensitivity and 100% specificity values were obtained.
Dealing with Energy-Generation Faults to Improve the Resilience of Microgrids: A Survey

In recent years, the Control Systems community has given certain attention to the optimal management and dispatch of (renewable) microgrids, specially in terms of energy generation planning. Nonetheless, modern technological processes are very susceptible to faults, specially due to instrumentation issues, and literature seems to lack works that assess their incipient effects in the performance of modern microgrids. This paper surveys the recent works that indeed investigate how to detect, estimate and deal with faults in such systems from a tertiary level point-of-view. Later, Fault Estimation (and Fault-Tolerant control) design methods for microgrids are discussed: open problems are pointed and (future) research threads are raised.
High-Accuracy Calibration of On-Site Multi-Vision Sensors Based on Flexible and Optimal 3D Field

Global calibration of multi-vision sensors in the railway fields is easily affected by on-site complex environments, such as lighting and self-occlusion, which makes it difficult for existing methods to achieve high-accuracy calibration. In this paper, a high-accuracy and flexible calibration method of multi-vision sensors in the outdoor railway fields based on flexible and optimal 3D data field through the combination of articular arm and metal target embedded with luminescent LEDs is proposed. Firstly, a high-accuracy and flexible 3D data field with multiple angles and views is constructed by the articular arm and the metal target rapidly; Secondly, the high-frequency multi-exposure imaging mode and multi-scale image feature points extraction are adopted, and the high-accuracy reposition is achieved through Kalman filter, which can reduce the impact of image noise efficiently; Finally, the RANSAC method is utilized to optimize the 3D data field, forming the optimal 3D data field, and the optimal target chains corresponding to each group of cameras are established. The maximum likelihood solution of global parameters is solved by nonlinear optimization. Simulation experiments verify the feasibility of the proposed method. Meanwhile, physical experiment results show this method can reduce the outdoor environment impact and improve the calibration and measurement precision effectively. In addition, the computational efficiency of the proposed method is about 11.2 times than multi-images averaging method, the calibration and measurement accuracy improve about 20 and 2.9 times respectively, which is suitable for the high-accuracy and flexible global calibration in the complex railway environment.
Self-learning of multivariate time series using perceptually important points

In machine learning, positive-unlabelled ( PU ) learning is a special case within semi-supervised learning. In positive-unlabelled learning, the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes. Positive-unlabelled learning has gained attention in many domains, especially in time-series data, in which the obtainment of labelled data is challenging. Examples which originate from the negative class are especially difficult to acquire. Self-learning is a semi-supervised method capable of PU learning in time-series data. In the self-learning approach, observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached. The model is retrained after each addition with the existent labels. The main problem in self-learning is to know when to stop the learning. There are multiple, different stopping criteria in the literature, but they tend to be inaccurate or challenging to apply. This publication proposes a novel stopping criterion, which is called Peak evaluation using perceptually important points, to address this problem for time-series data. Peak evaluation using perceptually important points is exceptional, as it does not have tunable hyperparameters, which makes it easily applicable to an unsupervised setting. Simultaneously, it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.
Optimal Generalized Predictive Functional Control with Applications to Extended PID Control for Quadruple Tank

Algorithm of optimal Generalized Predictive Functional Control is presented towards the control problem of linear discrete-time state-space multivariable systems. A quadruple tank system is simulated to demonstrate typical results. The proposed controller has been structured in a reduced order arrangement including a set of specified linear transfer-functions along with gains that are found to minimize a GPC cost-index. This approach enables a range of classical controller structures to be used in the feedback loop such as extended PI, PID or Lead-lag or a more general transfer function form. A MATLAB simulation was used to test the performance of the restricted structure controller for regulation and tracking problems and was compared against a classical PI Controller.
Partial Discharge Waveform Identification using Image with Convolutional Neural Network

The use of Neural Network as a tool for Partial Discharge (PD) Identification has been used since the 1990s, where Backpropagation Neural Network (BPNN) with input in the form of data values was the most widely used method. However, for the past five years, Convolutional Neural Network (CNN) has been used to identify Partial Discharge with input in the form of images. Pattern Recognition of Partial Discharge Image Based on One-dimensional Convolutional Neural reported by Wan et al. in CMD 2018 was using images of 5 cycles repeated waveform pattern images as input. In our research, we used two-dimensional images of time domain single PD waveform obtained from experiments using three sensors (High-Frequency Current Transformer (HFCT), Surface Current Sensor (SCS), and Transient Earth Voltage (TEV) sensor) for input to CNN. The images were extracted using Gaussian Filter on the Convolutional layer, then searched the maximum value for each group of pixels to make computing easier on the Pooling Layer, and finally identified on the Fully Connected Layer. The purpose of this study is to develop a PD diagnostic technique that could simplify the identification of PD sources by using a signal waveform image for identification. The experimental results show that the proposed method has sufficient accuracy in identifying Partial Discharge, and it may be possible to identify Partial Discharge in HV voltage equipment by comparing field data.
Analysis of Bio-Signals for Drivers' Stress Level Detection

In this study, the individual performances of the selected features, obtained from the ECG, GSR, EMG and RESP measurements by applying Pearson correlation analysis on the features accepted in the literature, were examined for the stress level detection. Accordingly, 2-, 1- and 3-dimensional feature sets were generated from ECG, Foot GSR and RESP measurements, respectively. These feature sets are classified by LLC, k-NN (k = 5), RF, DT and SVM algorithms. The feature set generated from the foot GSR measurement shows the best success with an accuracy of 66.67% when the LLC algorithm is used. This result indicates that the selected features are descriptive for stress level when they are used together.
Preparation of Serum Albumin Loaded Injectable Silica-Gel Matrix

Biomolecules play an active role in tissue regeneration. It is suggested that these biomolecules can be more efficient once the delivery is provided locally in the meant of with the local drug delivery systems. In the literature, the use of silicon dioxide gel matrix as a drug delivery system for different therapeutic agents are presented. The aim of this study is to prepare a silica gel drug delivery system that can be used as a bioactive molecule delivery system and to investigate the release profile of biomolecules by considering the biocompatibility of silica gel matrices and their positive effects on tissue regeneration. The methods intended to be followed to accomplish the stated objectives are to produce biocompatible silicon dioxide gel matrix by sol-gel method and to characterize the resulting silica gel matrices by testing both chemical content and physical properties and injectability. The release profile of BSA (Bovine Serum Albumin) protein from silica matrices as biomolecules is determined from optimized silica gel matrices. In this study, it was found that silica gel matrices are capable of delivering effective biomolecules and providing a sustained drug release profile.
Estimation of Electric Vehicles in Dynamic Environment

The market of electric and hybrid vehicles is still small compared to the market of combustion vehicles. However, this is an ever-expanding market. This paper aims to model the increase in electrical vehicles market in Brazil, which could help the electrical demand forecasting for electricity distribution utilities. The System Dynamics and the Bass Diffusion Model are used to represent the proposed system of the electrical vehicle market, first with a qualitative and then with a quantitative model. Three scenarios are simulated to test and validate it. The results show that the methodology used in this paper is adequate to model the system using different nature parameters, such as behavioral, strategic and economic.
Wireless Charging System Integrated in a Small Unmanned Aerial Vehicle (UAV) with High Tolerance to Planar Coil Misalignment

An innovative coil configuration for inductively-based wireless power transfer (WPT) technology is presented to recharge the battery of a compact unmanned aerial vehicle (UAV) (i.e., a remotely controlled drone). To improve the tolerance of the WPT system to coil misalignment caused by imperfect landing, a large primary coil is proposed in combination with a single turn, secondary coil. The on-board secondary coil is integrated in a part of the drone used as protection of the propellers. The single turn configuration of the secondary coil is efficient from an electrical point of view and very convenient as the additional weight installed on-board the drone is very limited.
ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient

Electrocardiogram (ECG) is a method used by physicians to detect cardiac disease. Requirements for batch processing and accurate recognition of clinical data have led to the applications of deep-learning methods for feature extraction, classification, and denoising of ECGs; however, deep learning requires large amounts of data and multi-feature integration of datasets, with most available methods used for ECGs incapable of extracting global features or resulting in unstable, low quality training. To address these deficiencies, we proposed a novel generative adversarial architecture called RPSeqGAN using a training process reliant upon a sequence generative adversarial network (SeqGAN) algorithm that adopts the policy gradient (PG) in reinforcement learning. Based on clinical records collected from the MIT-BIH arrhythmia database, we compared our proposed model with three deep generative models to evaluate its stability by observing the variance of their loss curves. Additionally, we generated ECGs with five periods and evaluated them according to six metrics suitable for time series. The results indicate that the proposed model showed the highest stability and data quality.
THE CONTRIBUTION OF COMMON FACTORS TO THERAPEUTIC OUTCOMES FROM THE CLINICIAN'S PERSPECTIVE: A MIXED METHOD STUDY TO EXPLORE COMMON MECHANISMS OF CHANGE

While the Common Factors (CFs) model is becoming more popular it has been criticized for the lack of empirical evidence compared to empirically supported treatments and the lack of capacity to guide clinicians on what to do and when to do it in the course of therapy. This parallel mixed methods study addressed both of these critiques. In phase one, a Common Factors Questionnaire (Karimi-CFQ) was developed to collect empirical data of CFs. In phase two, the Karimi-CFQ was administered to 391 clinicians in the United States to survey the therapists’ perspective regarding the contribution of common factors to therapy outcomes. In this phase data was also collected to assess the relationship between clinicians’ demographic characteristics and their perceived contribution of common factors to therapeutic change. The CFQ Cronbach’s Alpha and Split-half reliability were . 84 and . 87, respectively. Content Validity Index by expert panel, concurrent validity, and construct validity including Exploratory Factors Analysis (EFA) and Confirmatory Factor Analysis (CFA, χ=797.96, df=326; RMSEA=.06; CFI=.83) evaluated the validity of the scale. Clinicians across five clinical orientations (Integrative, Cognitive Behavior Therapy, Humanistic, Psychodynamic, Postmodern) and three mental health disciplines (marriage and family therapy, psychology, counseling) attributed 69% of therapeutic change to common factors versus 31% to model-specific factors. Clinicians attributed different contributions to specific components of the CFs model: client (25%), therapist (20%), relationship (23%), hope (12%), nontheory specific (11%), and systemic (9%). Particular clinicians’ characteristics (e.g., gender, year THE CONTRIBUTION OF COMMON FACTORS iii of experience) were found to be associated with contributions of specific components of CFs. In phase three, six CF experts were invited to respond to open-ended questions via Wiki that explored how and when experienced therapists use specific common factors in the course of therapy to reach to their therapy goals. Thematic Analysis (TA) generated a chronological map with specific themes that can guide clinicians on how and when specific CFs can be used in each of the three stages of therapy (i.e. Initial, Intermediate, Termination). Findings further indicated that CFs and modelspecific factors can be operationally defined and empirically studied within the same evidencebased practice framework. Clinical and research implications of the results are also discussed. THE CONTRIBUTION OF COMMON FACTORS iv DEDICATION This dissertation is dedicated to my family. THE CONTRIBUTION OF COMMON FACTORS v
An SDN-based NAT Traversal Mechanism for End-to-end IoT Networking

In Internet of Things (IoT) environment, devices often do not have public IPs to communicate with other devices due to addresses getting exhausted. Therefore, Network Address Translation (NAT) is used to translate IPs. Furthermore, NATs often also filter messages just like firewalls, and different types of NATs have different behavior on filtering. This causes difficulties for devices direct communication. However, although there are some NAT traversal methods can be used to traverse NATs, those methods may produce extra overhead or require need to distinguish the types of NATs at first. In this paper, an SDN-based NAT traversal mechanism is proposed to deal with the NAT traversal problem without previous mentioned problems. The centralized management property of software-defined networking (SDN) is utilized to develop this SDN-based NAT traversal mechanism. The functions of NATs are implemented on the controller as an NAT application. The controller controls the translating of addresses and ports, so the controller can own all information to traverse all kinds of NATs. From simulation results, the proposed NAT traversal method can successfully use the same process to traverse all kinds of NATs without producing extra overhead. Furthermore, the NATs are OpenFlow switches in the data plane, so it is flexible to adjust the settings of NATs.
Implementation of Multi-level Modulated-Backscatter Communication System Using Ambient Wi-Fi Signal

Wi-Fi Backscatter communication is method for zero-energy wireless connectivity using the ambient RF signals without needing an additional wireless communication infrastructure. Because this communication method uses Wi-Fi signal, there is a limit that cannot transmit a backscatter data faster than Wi-Fi packet transmission rate in the time-domain. This paper presents multi-level modulated-backscatter communication system using ambient Wi-Fi signal. We propose a method to improve data rate of backscatter communication using Wi-Fi signal. We implement and test Wi-Fi backscatter communication system applying transmitting multi-level load modulate data. The backscatter data transmission is achieved at a data rate of 60 kbps by the proposed multi-level backscatter modulator using Wi-Fi signal.
Comparison of Electromagnetic and Eletromechanical Transient Simulations for AC Power System

This paper provides a comparison of electromagnetic and electromechanical transient simulations for the AC power system. In both simulation tools, based on the identical system parameters, a single machine test case is developed, and power flow results are analyzed. The response of the IEEE simplified dynamic controller models, used in the studied electromechanical transient simulation tool is compared with the built-in generic internal controllers and user-defined external controller models in the considered electromagnetic transient simulation tool. The time domain transient stability of the single machine test case is compared by executing load change and three-phase balanced fault disturbances in both simulation tools. It is shown that similar controller models can be used in both simulation tools. The capability of the electromechanical transient simulations to accurately represent the transient response, decreases during fault and initial post-fault duration, as the severity of disturbance increases.
Development and optimization of an energy harvesting circuit for multiple piezoelectric elements integrated into a smart air spring

This paper presents the design of an energy harvesting interface for multiple piezoelectric elements. The latter is targeted to be integrated in an air spring to convert the mechanical energy in electrical one usable to power a wireless sensor node. Modelling of the energy harvesting electronic circuit has been performed to optimize the management of the multiple signals provided by the piezoelectric elements in function of a variable electrical load. An experimental activity has been conducted to validate the circuit design and the tests results demonstrate a good agreement with the numerical ones, confirming the effectiveness of the proposed interface.
Energy management and modeling of a grid-connected BIPV system with battery energy storage

The increased penetration of renewables in power system generation has introduced several stability issues to existing power systems. Their intermittent nature in combination with the lack of rotational inertia made system operation even more difficult and frequency/voltage fluctuations larger. Storage systems have been identified as an ideal solution for mitigating these problems since their integration across the grid can reduce generation-load imbalances and assist in primary frequency regulation. Considering the importance storage systems have gained during the last years, in this paper we propose an energy management algorithm for a grid-connected PV system with battery storage. This model contains a Building Integrated Photovoltaic (BIPV) system connected to the grid through a DC-DC boost converter, a DC-AC inverter and a battery storage system in active parallel configuration. Considering that the consumption of the building is satisfied from either the PV, the low voltage grid and/or the battery storage system, a specific energy management algorithm is presented for this model, in order to provide an efficient power flow between the aforementioned sources and the building load. The proposed model is implemented and verified through several simulations in Matlab/Simulink.