Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Expert System for the Evaluation of Student Action in Virtual Worlds and Training Systems

One of the approaches to applied competences' development of future engineers is the use of virtual workplaces. In order to train a specialist on real equipment is often economically inefficient and involves risks to health, and sometimes it is not possible. The success of computer technologies intensified the rapid development of virtual simulators and allowed developers to design virtual laboratories. However, the task of trainees' actions assessment in simulation systems and virtual systems has not yet been solved in its full completeness.
The Motor Imagination EEG Recognition Combined with Convolution Neural Network and Gated Recurrent Unit

For the common electroencephalogram(EEG) feature extraction methods, the temporal sequence of EEG signals is often neglected. A new model called Convolutional Gated Recurrent Neural Network that combines Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. The model extracts the combinatorial features of the preprocessed Motor Imagination EEG by CNN, it enriches the GRU input, and then it uses GRU to extract some sequence information hidden in the EEG signals to improve the recognition accuracy of MI EEG signals. In addition, batch normalization (BN) is added between the CNN and the GRU to speed up the operation of the network, and a dropout is introduced to sparse networks' connection to prevent over-fitting. Experiments results show that the average recognition accuracy of the EEG data collected by ourselves is 93.57%. In the open data set BCI competition IV data set 2b, compared with the traditional CSP algorithm, it can improve the recognition rate of EEG by 2.56% on average.
Adaptive Kernel Data Streams Clustering Based on Neural Networks Ensembles in Conditions of Uncertainty About Amount and Shapes of Clusters

The neural network's approach for data stream clustering task, that in online mode are fed to processing in assumption of uncertainty about amount and shapes of clusters, is proposed in the paper. The main idea of this approach is based on the kernel clustering and idea of neural networks ensembles, that consist of the T. Kohonen's self-organizing maps. Each of the clustering neural networks consists of different number of neurons, where number of clusters is connected with the quality of these neurons. All ensemble members process information that sequentially is fed to the system in the parallel mode. Experimental results have proven the fact that the system under consideration could be used to solve a wide range of Data Mining tasks when data sets are processed in an online mode.
Correlation-Based Pre-Filtering for Context-Aware Recommendation

With the increasing use of connected devices and IoT, users’ contextual information is more and more available and used in different information systems. One of the domains where the use of contextual information is promising is that of recommendation. As a matter of fact, context-aware recommender systems (CARSs) have demonstrated that taking contextual information about users into account can improve the effectiveness of recommendation, by generating more relevant recommendations to the users in their specific contextual situation. In this paper we propose a new context representation and approach to integrate this kind of information into a recommender system. We make a strong representation of the context, based on the influence of context on ratings, calculated using the Pearson Correlation Coefficient. We do a pre-filtering recommendation based on this representation. Our evaluations demonstrate that our approach can outperforms the state of the art.
Distributed Fog Computing Based on Batched Sparse Codes for Industrial Control

In an industrial automation system, one of the most important parts is control loop. Fog computing is a potential solution for industrial control in time-critical applications as it provides distributed computing services closer to the connected devices. However, a huge amount of data exchanging among fog nodes causes high communication load, which constrains the overall response time from fog nodes to actuators. In this paper, we consider the erasure environment, batched sparse (BATS) codes are applied to the Map and the Data Shuffling stages of distributed fog computing process to reduce both the communication and the computation loads. The communication loads of the uncoded, the coded, and the proposed BATS-based schemes over erasure channels are calculated, respectively. Numerical results show that the BATS-based scheme can reduce the communication and the computation loads simultaneously, and furthermore reduce the overall response time from fog nodes to the actuators.
A low-cost room-level indoor localization system with easy setup for medical applications

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.
Attitude and Orbit Coupled Control for Spacecraft Rendezvous and Docking Considering Safe Constraint

This paper studies attitude and orbit coupled control scheme for spacecraft rendezvous and docking. On basis of the spacecraft attitude and orbit coupled model using modified Rodrigues parameters (MPRs) on the chaser spacecraft body coordinate system, an adaptive sliding mode controller is proposed for the situation of unknown upper bound of external disturbances. A new artificial potential function is presented to approximate final approaching corridor. The use of the potential function in the controller can guarantee the chaser move in the safe area. Lyapunov theory is adopted to prove that the states of the system under the proposed controller are bounded, and the chaser can realize rendezvous and docking without collision. The numerical simulations are conducted to further demonstrate the effectiveness of the proposed controller.
Spatial Distribution of Endogenous Tissue Protease Activity in Gastric Carcinoma Mapped by MALDI Mass Spectrometry Imaging*

This sample preparation workflow enables the spatially-resolved monitoring of tissue protease activity in frozen tissue slices using MALDI MS Imaging. It measures the decrease of a general tracer substrate, substance P, and the increase in digestion products in a time-dependent manner. Attenuation of tracer degradation by protease inhibitor mix is concentration-dependent. In a mouse model, we visualized high protease activity in gastric tumor compared to surrounding tissue, and differential protease expression in these tissues was confirmed using quantitative bottom-up proteomics. Graphical Abstract Highlights New MALDI MS imaging sample preparation workflow reveals tissue protease activity. Differential time- and inhibitor concentration-dependence confirm active proteases. Mouse gastric tumor displays high protease activity compared to surrounding tissue. Proteomic data and biochemical protease activity assay support MALDI MSI results. Aberrant protease activity has been implicated in the etiology of various prevalent diseases including neurodegeneration and cancer, in particular metastasis. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has recently been established as a key technology for bioanalysis of multiple biomolecular classes such as proteins, lipids, and glycans. However, it has not yet been systematically explored for investigation of a tissue's endogenous protease activity. In this study, we demonstrate that different tissues, spray-coated with substance P as a tracer, digest this peptide with different time-course profiles. Furthermore, we reveal that distinct cleavage products originating from substance P are generated transiently and that proteolysis can be attenuated by protease inhibitors in a concentration-dependent manner. To show the translational potential of the method, we analyzed protease activity of gastric carcinoma in mice. Our MSI and quantitative proteomics results reveal differential distribution of protease activity - with strongest activity being observed in mouse tumor tissue, suggesting the general applicability of the workflow in animal pharmacology and clinical studies.
A Numerical Procedure for the Optimization of IGBT Module Packaging

A numerical solution procedure for optimizing the thermal-mechanical reliability of an IGBT module has been described in this paper. The procedure is robust and it can handle both discrete and continuous decision variables in the design of IGBT packaging. An example has been given to demonstrate its application. The objective functions in this example are the accumulated plastic work density in solder joints of a simplified IGBT module that is subject to cyclic temperature cycling and the maximum junction temperature when the model is subject to a constant thermal loading. The thickness of the chip mount-down solder, thickness of the substrate solder joint, and the location of the chip are used as continuous decision variables. The material selection for the baseplate is treated as a discrete variable. The objective function values are calculated using Finite Element Analysis method.
Extended-State Observer-Based Integral Sliding-Mode Control for a Quadrotor

This study presents the extended-state observer-based integral sliding-mode control (ESO-ISMC) for a quadrotor. A novel model of a quadrotor that employs control instruction of attitude as actual input is constructed, which is close to the actual model of a quadrotor and easy to design the controller of a quadrotor. The proposed ESO-ISMC estimates not only the multiple disturbances as the lumped disturbances form, but also the state of a quadrotor. The adaptive integral sliding-mode control (ISMC) employs the disturbance estimates from ESO to actively reject the disturbance, which can effectively reduce the chattering and enhance the anti-disturbance ability. Simulation results are given to validate the effectiveness of the proposed method.
Research on Target Tracking Algorithm Based on Variance Sampling for Fast Sparse Representation

The Sparse Representation tracking algorithm under particle filter framework has many problems, such as large number of particles and complexity of $l_{1}$ norm minimization calculation. To refine the issues mentioned above, a fast sparse representation target tracking algorithm based on variance sampling is proposed. The algorithm first uses the variance estimation to optimize the distribution of sampling particles in the motion estimation stage; Then, the discriminant objective function is solved by using $l_{2}$ norm instead of $l_{1}$ norm, furthermore, the improvement of the measurement form of the reconstruction error is made to enhance the sparsity of the $l_{2}$ norm; Finally, online dictionary learning (ODL) algorithm to update the template dictionary online. The experimental results show that the proposed algorithm can greatly improve the tracking efficiency under the premise of ensuring stable tracking and accuracy.
A Transmission Metasurface for Generating OAM Beams

This letter presents a novel transmission metasurface to achieve orbital angular momentum (OAM) waves at the X-band. The unit cell of the proposed metasurface consists of three dielectric layers and four metallic layers, where the metallic layer is composed of an outer metallic ring and a center quasi-snow patch. A good transmission coefficient of the unit cell is obtained at the operating frequency. Meanwhile, a full 360° phase shift can be achieved by changing the length of the quasi-snow patch. The method of generating OAM waves is studied and analyzed. Based on this method, a metasurface producing a +1-mode OAM wave is designed and simulated. To verify this concept, one prototype is fabricated, assembled, and measured. The measured results agree well with the simulated ones, showing that the +1-mode OAM wave can be generated successfully with a high gain of 14.5 dBi. Moreover, a narrow divergence angle of +/−9° is achieved in the produced OAM wave.
Lactic acid production from sugarcane bagasse hydrolysates by Lactobacillus pentosus: Integrating xylose and glucose fermentation

Lactic acid, traditionally obtained through fermentation process, presents numerous applications in different industrial segments, including production of biodegradable polylactic acid (PLA). Development of low cost substrate fermentations could improve economic viability of lactic acid production, through the use of agricultural residues as lignocellulosic biomass. Studies regarding the use of sugarcane bagasse hydrolysates for lactic acid production by Lactobacillus spp. are reported. First, five strains of Lactobacillus spp. were investigated for one that had the ability to consume xylose efficiently. Subsequently, biomass fractionation was performed by dilute acid and alkaline pretreatments, and the hemicellulose hydrolysate (HH) fermentability by the selected strain was carried out in bioreactor. Maximum lactic acid concentration and productivity achieved in HH batch were 42.5 g/L and 1.02 g/L h, respectively. Hydrolyses of partially delignified cellulignin (PDCL) by two different enzymatic cocktails were compared. Finally, fermentation of HH and PDCL hydrolysate together was carried out in bioreactor in a hybrid process: saccharification and co‐fermentation with an initial enzymatic hydrolysis. The high fermentability of these process herein developed was demonstrated by the total consumption of xylose and glucose by Lactobacillus pentosus, reaching at 65.0 g/L of lactic acid, 0.93 g/g of yield, and 1.01 g/L h of productivity. © 2018 American Institute of Chemical Engineers Biotechnol. Prog. , 35: e2718, 2019
Combining Multiple Views from a Distance Based Feature Extraction for Text Classification

Text Mining is a challenging task due to the lack of a naturally structured representation and the high dimensionality induced by the feature extraction techniques commonly used. Different feature extractions can lead to multiple views that can capture different aspects of the text documents being analyzed. The combination of these features can lead to a better accuracy in classification tasks but, also, an undesirable increase in the number of features. In this work, we investigate the use of a feature extraction technique called DCDistance used as a multiple feature extraction for text documents combined with a Genetic Algorithm based feature selection, hereby called MVDCD. The results show that the main advantage of MVDCD is that the dimensionality is reduced by more than 90% while significantly increasing the classification accuracy when compared to vanilla DCDistance and other feature selections techniques. A side effect of the use of DCDistance and MVDCD is the possibility of model interpretability, as the extracted features are explicit.
Block Size Optimization of CBFM for Scattering Problems

The characteristic basis function method (CBFM) was developed as an algorithm to accelerate the analysis based on direct matrix solver. In CBFM, the optimum block size greatly influences the overall analysis time. In this paper, we propose a guideline for setting the optimal block size that can be applied to every algorithm based on CBFM. Rough estimation of the optimal block size is $L_{B}^{rough} \approx 0.05 N^{6/{29}}$ , and the exact optimal block size can be determined through preprocessing based on rough estimation. Numerical results of electromagnetic scattering problems are shown to demonstrate the performance of the proposed method.
Real-time Detection of Wearable Camera Motion Using Optical Flow

The efficient use of image sensors has been one of the top challenges for computer vision researchers for several years. Detecting and tracking objects, video surveillance, navigation, and many other real-time applications depend on motion estimation for moving camera. In this paper, a real-time method for the detection and classification of the motion of a wearable, moving monocular camera is proposed. This approach was adopted to be used with smart glasses to assist people with visual field defects. Five main motion classes (corresponding to the five primary degrees of freedom) were detected using optical flow and motion velocity vectors calculation. These classes cover different degrees of freedom including rotation and translation. The proposed method classifies the type of camera motion as static, translation/rotation left, right, up or down. This classification is important for object detection and tracking that can alert the user to the potential hazards outside their field of view. The proposed approach has been tested on a real first-person perspective video captured by a wearable camera. The experimental results demonstrate that the proposed method classifies the type of motion successfully in real-time and can be used as part of low-cost wearable solutions for various forms of vision loss assistive technologies. Promising performance results of 84% correct states for camera motion detection were obtained.
A High Performance and Low Cost Half Bridge IGBT Planar Power Module

This study presents the development and performance of a half bride insulated-gate bipolar transistor planar power module integrated with a turbulent water cooler for double side direct substrate cooling. A holistic approach which simultaneously overcomes the mechanical, electrical and thermal limitations as well as the inefficient manufacturing of conventional power modules has been employed. Work includes the design consideration in terms of packaging structure, materials and bonding technologies; three-dimensional finite element static electric simulation to spot locations with the highest electrical potential gradient, electromagnetic simulation to extract the parasitic inductances of commutation loops, thermal and computational fluid dynamics co-simulation to predict the cooling performance of the integrated turbulent water cooler; assembling processes and technologies to prototype the design module; and static electrical and double pulse tests to verify the electrical functions, and thermal test to validate the cooling performance. All the results obtained can be used to demonstrate cost effective manufacturing, desirable mechanical, electrical and thermal performance and potential improvement of the designed and prototyped planar power module.
Automatic Discovery of Behavioral Models From Software Execution Data

During the execution of a software system, tremendous amounts of data are recorded, and such data provide valuable information on software runtime behavior analysis. This paper presents an approach on how to utilize process mining as an enabler to discover software behavioral models. To achieve this, we formally define the software event log and its transformation from original software execution data. Essentially, a software event log consists of a set of cases that each is a manifestation of an independent software run. A case is represented as an ordered sequence of events that each refers to a method call. Given the observation that software usually has a hierarchical structure, we first propose an approach to construct a hierarchical software event log from the original flat one by the recursively applying method calling relation detection. Next, using extended process discovery techniques, we discover a software behavioral model, which is represented as a kind of hierarchical Petri net with components, from the hierarchical software event log. We have implemented the proposed approach in the open source process mining toolkit ProM. By using two synthetic software event logs, we show that our approach can deal with infrequent behavior. Moreover, a validation with one real-life software event log shows that our approach can help visualize actual software runtime behavior in an easy-to-understand manner. Note to Practitioners—Software data analysis techniques play an increasingly important role for understanding the real behavior of software systems. This paper addresses the issue of discovering behavioral models from real-life software execution data. A kind of hierarchical Petri nets is defined to represent the discovered software behavior. All proposed concepts and algorithms are supported by open source tools, and experiments over both synthetic and real-life software execution data have shown their applicability. The proposed methodology is readily applicable to industrial-size software behavior discovery problems.
Maximum Power Point Tracking in Photovoltaic Farms Using DE and PSO Algorithms: A Comparative Study

Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are two commonly employed techniques in designing maximum power point tracking systems in photovoltaic (PV) farms. A mathematical formulation of the objective function is derived by implementing the maximum power theorem for load matching using the relationship between input and output impedances. This paper also proposes a novel Center-based Latin Hypercube (CLHS) initialization scheme for population-based algorithms; it is shown that for population initialization, the newly proposed technique of CLHS gives better results with a small population size. A comprehensive comparative study is conducted on DE and PSO algorithms in terms of control parameters, search components, and population initialization methods to determine the best algorithm with its corresponding optimal parameters settings and population initialization to solve a family of maximum power point tracking problems. The work shows that both algorithms are capable of tracking the maximum power point although the PSO is more effective over a small population size. In this study, in overall, 15,876 and 96,228 settings possibilities for DE and PSO respectively are investigated.
Towards Self-management of Chronic Diseases in Smart Homes : Physical Exercise Monitoring for Chronic Obstruction Pulmonary Disease Patients

We aim to design a modular system that provides self-management of chronic diseases in a Smart Home. The modularity is defined on a per-disease basis with distinctive requirements for each disease model: a set of specifications to describe a disease and a combination of technologies that support them in the Smart Home. In this paper we focus on a use-case related to a patient with Chronic Obstructive Pulmonary Disease (COPD) living in a smart environment. The proposed solution will provide monitoring of physical exercise through sensors and video, as well as deliver timely feedback about their execution, performance and also environmental conditions that could have an impact on user’s health.