Miracle-dz/UniFAR-FTUs huggingface dataset:
Selecting latent factors when predicting student performance in online campus by using recommender systems

Recommender Systems is a popular method of Collaborative Filtering in Machine Learning. They are applied to many applications for prediction purposes, for example, the Predicting Student Performance problem. In this problem, we start from a database where the scores of some academic tasks (exercises, tests, exams) are stored for the corresponding students, considering a particular subject in the academic course. Since some scores for a particular student could be unknown (tasks not completed, submitted or evaluated), they can be predicted considering his behavior in the remaining tasks and the behavior of the other students for these unknown tasks. The prediction is performed by means of Matrix Factorization where the error (Root Mean Squared Error) is minimized by the Gradient Descent algorithm. Nevertheless, two problems arise: the best selection for the main parameters of the algorithms (learning rate and regularization factor) and the best selection for the number of latent factors. In this work, we propose a direct search of the optimal value of latent factors where the calculation for each number of latent factors is driven by a metaheuristic that select, at the same time, the optimal values of learning rate and regularization factor. Using this method, we can determine the best number of latent factors to be applied in further predictions for the similar databases.
Computational modelling of heat transfer in a porcelain-housed surge arrester

Surge arresters are equipment essential to the protection of the electric system against overvoltages. In an arrester, the housing temperature is a key factor to the assessment of its condition of operation. In this paper, a method to correlate the varistors temperature and the housing temperature of ceramic housing arresters is proposed. Models to evaluate the housing temperature from a given power input in the varistors are compared. Computational simulations based on the finite element method (FEM) were performed using a model of a 69 kV arrester with ceramic housing. The value of the housing's convective heat flux coefficient was estimated. The results obtained allow to quantify the heat transferred to the ambient by each mechanism and, thus, to define a model that is accurate and yet requires low computational effort.
Detection of self-paced reaching movement intention from EEG signals

MRCPs (movement related cortical potentials) are slow negative potentials observed in EEG preceding movement, which represents the processing of the cerebral cortex during planning and preparation. Clinical studies have shown that, rehabilitation therapy with the active participation of the central nervous system can rebuild related nerve function and restore the patient's neural plasticity. Characterizing the intention to move by means of electroencephalographic activity can be used in rehabilitation protocols with patients cortical activity taking an active role during the intervention. We describe our method framework including protocol, data processing and pattern recognition. Then the experimental results are analyzed. The tests were carried out with healthy people and achieved satisfactory performance both in trial and asynchronous detection. Our purpose is to apply to restoration of neural pathway of patients.
Provisioning of 5G services employing machine learning techniques

This study proposes a modeling framework for optimal online 5G service provisioning, based on low computational complexity machine learning techniques such as Neural Network (NNs). NNs are trained to take optimal decisions adopting an offline Integer Liner Programming (ILP) model. This framework is used to solve the generic joint Fronthaul (FH) and Backhaul (BH) service provisioning problem over a converged high capacity and flexibility optical transport aiming at minimizing the overall energy consumption of the 5G infrastructure. Our modeling results indicate that the proposed approach adopting NN based real time service provisioning can provide very similar performance to the one derived adopting the high complexity but accurate ILP approach.
Analysis of isolated high boost quasi-two switch boosting switched-capacitor converter

This paper proposes a novel isolated high boost quasi-two-switch-boosting switched-capacitor (ITBSC) converter. It consists of a quasi-two-switch boosting switched-capacitor converter cell and the isolated transformer cell. Two small value resonant inductors were injected into the charging and the discharging loops, respectively. The high spikes of the input current were therefore reduced. What's more, the continuous input currents were achieved. The first stage quasi-switched-capacitor converter provides the voltage step-up function with the simple topology. In combination with the isolated transformer in the second stage, the proposed SC converter could provide high voltage conversion ratio and stable performance. The topology of proposed quasi-switched-capacitor in this paper could be used in the low and high input voltage conditions. It can be used as the isolated DC-DC power supply in the electrical vehicles and the solar energy systems. A 300W power rating quasi-two-switch boosting SC converter was constructed. The robust performance of the proposed SC converter was presented and demonstrated. The analysis and the modeling method was verified. The proposed SC converter has a wide operation range. It could facilitate the application of SC converters.
Low cost monitoring system for distribution transformers

This paper introduces the development and implementation of an electric power quantities monitoring system, for real-time measurement of power, under sinusoidal or non-sinusoidal, balanced and unbalanced conditions. The system is applied to distribution transformer power measurement under real time. The power calculations method is based on the definitions given by the IEEE Std. 1459-2010.
Using asynchronous collaborative attestation to build a trusted computing environment for mobile applications

Nowadays, mobile applications like mobile payments become more popular in public life, but users are eager to acquire a trusted execution system to protect its secrets. This paper presents the design, implementation, and evaluation of the Trusted Computing Environment for the Mobile Application, which protects the integrity and confidentiality of the software from the risk of untrusted mobile platform. The mechanism based on TrustZone and non-interactive verifiable computing provides the Asynchronous Collaborative Attestation Mechanism (ACAM) to runtime attest the active system. TrustZone is a strong hardware-feature based isolated execution technique, but it can not defend against the timing attacking. ACAM can effective prevent this kinds of risk by asynchronous remote attestation and reduces the overhead from real-time protection. The security analysis and evaluation shows that the approach has major potential in mobile trusted computing system and provides a higher secure level environment for mobile users.
Differential Sensors Using Microstrip Lines Loaded With Two Split-Ring Resonators

This paper presents a microwave sensor using a pair of split-ring resonators (SRRs). The sensor is designed by loading a microstrip transmission line by two identical SRRs on its sides. Differential permittivity sensing is performed by loading the SRRs with dielectric samples. One transmission notch is observed for the identical loads, whereas the non-identical samples produce two split notches. The sensor’s operating principle is described through a circuit model analysis. A prototype of the designed sensor is fabricated and experimentally validated for verifying the differential sensing concept. The developed device can be used to compare or differentially characterize solid dielectric samples improving the robustness to environmental factors producing cross-sensitivity or miscalibration.
Demand-side management of hierarchical energy distribution based on a joint intention protocol

One important concept of smart grids is the Demand side management (DSM), which enables peers to take decisions on their energy consumption and assists the energy providers in decreasing the peak load demand. Thus, the whole grid has its sustainability improved, while its overall operational cost and carbon emission levels are reduced. Most of the existing demand side management approaches use local strategies, such as heuristics and optimization algorithms, to carry out this management. However, these approaches are limited since they do not consider the state or needs of other components that compose the distribution network. This paper specifies a communication strategy that considers the grid peers as hierarchical agents, which exchange events by means of a joint intention protocol. Our focus is on the specification of the communication protocol and how it affects the management of energy demand plans in different hierarchical levels. A descriptive analysis is then carried out to stress the features and advantages of this approach.
Determining neural network architecture using data mining techniques

This paper presents techniques used to determine the optimal neural network architecture using pattern recognition and data mining. Clustering techniques highlight a number of common characteristics of input forms, which are classified into groups based on a given criterion. In the proposed method, the number of groups obtained using clustering techniques on the training data of a neural network represents the main factor for determining the optimal number of hidden layers for a multi-layer neural network. Use of this method allows the design of an optimal neural network to be unsupervised and will decrease its build time.
iPand: Accurate Gesture Input with Smart Acoustic Sensing on Hand

Finger gesture input is emerged as an increasingly popular means of human-computer interactions. In this demo, we propose iPand, an acoustic sensing system that enables finger gesture input on the skin, which is more convenient, user-friendly and always accessible. Unlike past works, which implement gesture input with dedicated devices, our system exploits passive acoustic sensing to identify the gestures, e.g. swipe left, swipe right, pinch and spread. The intuition underlying our system is that specific gesture emits unique friction sound, which can be captured by the microphone embedded in wearable devices. We then adopt convolutional neural network to recognize the gestures. We implement and evaluate iPand using COTS smartphones and smartwatches. Results from three daily scenarios (i.e., library, lab and cafe) of 10 volunteers show that iPand can achieve the recognition accuracy of 87%, 81% and 77% respectively.
Optimal control strategy of three-port DC energy router based on TCM modulation

Three-port DC/DC converter can be used in the DC micro-grids as an energy router. To achieve higher efficiency, the optimal modulation scheme of triangular current mode modulation (TCM) is proposed, which considers the current stress and system losses. First, the operation mechanism of TCM is analyzed. The power transmission characteristic, voltage and current characteristics of each component are analyzed. Appropriate voltage stability control strategy is designed. Then, the simulation model of three-port energy router is built. And the characteristics of load power fluctuations or power flow switching are analyzed. The results show that, the power flow and dc bus voltage between different ports can be effectively controlled. Finally, the effectiveness of the proposed TCM strategy applied to the triple active bridge (TAB) converter is validated by using MATLAB/Simulink.
Analysis of CMBR using nano-satellite

This paper intends to design a payload for a Nanosat to record and analyze the Cosmic Microwave Background Radiation (CMBR). Since its discovery in 1965, CMBR has become a much-discussed topic with the speculation that it might hold the key to the birth of our Universe. The big bang theory not only scattered the matter which condensed into galaxies, but also a massive amount of radiation which can be detected by using appropriate instruments, albeit as microwaves due to massive redshift (Redshift is shifting of the wavelength of light or some other form of electromagnetic radiation to the red end of the spectrum. It occurs when a light source moves away from the observer) of our ever-expanding universe. The space missions for detection of CMBR have not been cost efficient, and the Earth-based laboratories deal with too much interference from the surrounding sources. The radiation that was first discovered was of the wavelength 10cm at a reported temperature of 3.5 ± 1 K. The data received about the cosmic microwave background radiation can provide us much information about the anisotropic nature of the observable universe, the cosmic inflation era as well as the recombination era. In the present scenario, CMBR can be detected at a wavelength of about 2 mm. The challenge lies in using a Nanosat in the low earth orbit (LEO) to study CMBR because, being a form of radiation in itself, it is very susceptible to noise from the surrounding sources. The radiation from the sun, the earth as well as the satellite itself will contribute to the noise which needs to be calibrated off since the radiation due to the Cosmic Microwave Background is faint. The interference due to the ambient plasma will also be added to the noise. The antennas are designed with reflectors and calibrated to deal away with all of the aforementioned noises. The use of appropriate rectifiers and amplifiers for selection and amplification of the desired data has also been employed. The whole setup is cooled externally and the orbit selected to minimise the interfering radiation. The spinning of the satellite is controlled to gather desired amount data from a particular region of space. A detailed analysis for noise mitigation and selection of appropriate equipment has been discussed in this paper.
Improved SIFT algorithm based on adaptive contrast threshold

How to adjust the feature points number adaptively according to the images in different scenes is one of the key issues in improving detection efficiency. In this paper, an improved SIFT algorithm based on adaptive contrast threshold was proposed. Firstly, back propagation neural network and analytic hierarchy process were used to analyze the mathematical models of feature points number, image information and SIFT contrast threshold in different scenes from the perspective of image complexity, so as to realize the dynamic adjustability of contrast threshold. Then, a new SIFT algorithm framework was constructed by using the adaptive control module based on the mathematical model, and ultimately the number of feature points was coordinated. Compared with the two existing algorithms, the experimental data verified that the proposed algorithm had higher efficiency and accuracy, and that it realized the efficient control of feature point number in multi-scene.
Deep learning NATO document labels: A preliminary investigation

This paper provides an introductory, mostly nontechnical exposition of recent Deep Learning concepts, as relevant to NATO information processing requirements. A baseline case of learning confidentiality classification rules for NATO documents is described, originally performed by applying classical machine learning methods. In the course of the investigation described in this paper, the classical approaches have been complemented by Deep Learning experiments. These experiments shed some light on the potential advantages of using Deep Learning approaches for NATO document processing and beyond.
HW issues of current sensing by DC-link shunt resistor

This paper deals with issues and challenges of current sensing using DC-link shunt resistor in three-phase power inverter motor control applications. Main parameters of shunt resistors relevant to the DC-link current sensing are discussed. Main factors influencing the accuracy are described, including the shape of the footprint and soldering. An example of a sensing circuit is introduced, discussing the key parameters and challenges needed to be dealt with in terms of the accuracy of the measurement and mainly the proper operation of the sensing circuit in the whole range of operation.
Surrogate modelling technique in multi-parametric laser welding process

A possibility of using surrogate model for fast determining weld depth in multi-parametric hybrid laser welding process (laser welding with induction preheating) is studied. The dependence of weld depth on laser beam power and velocity of its movement is investigated. For selected combinations of laser power and speed, FEM simulations of 3D coupled problem combining heat transfer and electromagnetic field are performed. The surrogate model is then created based on results allowing to predict depth for any combination of power and speed. Main motivation to use surrogate modeling technique is to substantially save the computational time.
Comparison of DC conductivity of the insulating oil and moisturized electrical pressboard impregnated with insulating oil

In the paper presents compares the DC conductivity of the insulating oil manufactured by Nynas and electrical pressboard Pucaro companies impregnated with the same insulating oil with different moisture contents. The study of conductivity for both materials were made for the measuring temperature in the range of 20 °C to 80 °C and measuring voltages in the range from 20 V to 1000 V. In the Arrhenius plots for both materials were been performed linear approximations of experimental results. On the basis of Arrhenius plots for the oil and the sample for the pressboard, for all measuring voltages, the value of the activation energy value of the DC conductivity and the determination coefficient R2 was determined. The value of the determination coefficient close to one indicates the high accuracy of conductivity measurements. Comparing the activation energy value of the DC conductivity for insulating oil and pressboard impregnated with the same oil, it can be concluded that the conduction in pressboard is not caused by the presence of oil in the solid component of the paper-oil insulation.
Power conversion efficiency calculation models for electric vehicle fast charger system

Efficiency is the key design parameter for the electric vehicle (EV) fast charger system. This paper proposes the calculation models for the 15kW power module that is usually used in parallel in the EV fast charger system. The power module consists of the Vienna rectifier and the isolated dc-dc converter. Quantitative power losses comparisons among the half bridge three level LLC (HBTL-LLC) resonant converter, the half bridge three level phase shifted (HBTL-PS) converter and the phase shifted full bridge (PSFB) converter using Silicon Carbide (SiC) components are carried out to evaluate their efficiency and performance. The results indicate that the LLC resonant converter and HBTL converter have nearly the same losses while SiC based PSFB converter has less losses compared to them within the operating ranges of the 15kW power supply. It is demonstrated that the proposed calculation models are a handy toolset to evaluate the efficiency of the different new topologies for the EV charger. To validate the calculation models, experiments are performed on a 15kW power supply platform.
Rolling Bearing Fault Diagnosis Based on Optimal Notch Filter and Enhanced Singular Value Decomposition

The impulsive fault feature signal of rolling bearings at the early failure stage is easily contaminated by the fundamental frequency (i.e., the rotation frequency of the shaft) signal and background noise. To address this problem, this paper puts forward a rolling bearing weak fault diagnosis method with the combination of optimal notch filter and enhanced singular value decomposition. Firstly, in order to eliminate the interference of the fundamental frequency signal, the original signal was processed by the notch filter with the fundamental frequency as the center frequency and with a varying bandwidth to get a series of corresponding notch filter signals. Secondly, the Teager energy entropy index was adopted to adaptively determine the optimal bandwidth to complete the optimal notch filter analysis on the raw vibration signal and obtain the corresponding optimal notch filter signal. Thirdly, an enhanced singular value decomposition de-nosing method was employed to de-noise the optimal notch filter signal. Finally, the envelope spectrum analysis was conducted on the de-noised signal to extract the fault characteristic frequencies. The effectiveness of the presented method was demonstrated via simulation and experiment verifications. In addition, the minimum entropy deconvolution, Kurtogram and Infogram methods were employed for comparisons to show the advantages of the presented method.
Green computing: Awareness and practices

This study is an attempt to measure the level of green computing knowledge and awareness of university students. A quantitative research approach was used in this study, and data were collected through a survey designed to this purpose. Different statistical methods were utilized including factor analysis and t-test regression to analyze the data and to test the study hypotheses. The results indicated that the level of knowledge of green computing among students is high. However, even with having that amount of knowledge, their daily practices of green computing are not adequate. The results also showed that there is a need to provide workshops and training programs to students for increasing their awareness about green computing.
Self-consumption enhancement on a low-voltage grid-connected photovoltaic system

The electricity production from photovoltaic systems is seen as a real alternative to the electric power supply. The current global trend is to financially support the self-consumption through a remuneration of the PV energy production that is locally consumed by the owner of PV system. This paper conducts an analysis of the self-consumption indices related with the energy production of a low-voltage grid-connected PV system, installed on Faculty of Electrical Engineering, Technical University of Iasi, Romania.
Transient and d.c. analysis of the operation mechanism of light-emitting electrochemical cells

Light-emitting electrochemical cells (LECs) made of electroluminescent polymers were studied by d.c. and transient current-voltage and luminance-voltage measurements to elucidate the operation mechanisms of this kind of device. The time and external voltage necessary to form electrical double layers (EDLs) at the electrode interfaces could be determined from the results. In the low- and intermediate-voltage ranges (below 1.1 V), the ionic transport and the electronic diffusion dominate the current, being the device operation better described by an electrodynamic model. For higher voltages, electrochemical doping occurs, giving rise to the formation of a p-i-n junction, according to an electrochemical doping model.