Miracle-dz/UniFAR-FTUs huggingface dataset:
A Portable and Mobile Systems for Identifying Cardiac Arrhythmia Using Naïve Bayes

Arrhythmia is an abnormality of the heart’s rhythm. If this is not treated quickly, it can lead to serious problems, such as sudden cardiac arrest which results in death. An electrocardiogram (ECG) is a medical device used to check the activity of the heart’s electrical signals to identify arrhythmias. It is difficult for ordinary people or patients with heart disease to monitor their heart health independently because of the high cost of detecting heart problems using an ECG in a medical laboratory without supervision from a medical doctor. Therefore, a portable ECG is needed as a heart signal monitoring tool that everyone can have to detect early symptoms, where the results can be sent to a doctor for further treatment. This study uses the ADS1293 acquisition module to monitor the electrical activity of the heart with three lead points, namely lead I, lead II, and lead V1. Those three lead points are sufficient to assess cardiac status. The signals from each lead will then be classified using the Naive Bayes method. The results obtained have an accuracy of 85% and a precision of 88.23% based on 20 tests. The waveform from each lead and the classification results are sent to the user’s smartphone using Bluetooth.
Automatic Visual Inspection of a Net for Fish Farms by Means of Robotic Intelligence

Autonomous underwater vehicles (AUV) have been utilized to address the issue of net inspection in the fish farm industry. This approach can improve the accuracy, precision, and cost-effectiveness of net inspection, while also providing a safer alternative to human divers. However, creating precise inspection trajectories without GPS represent a significant challenge. In this paper, we propose a system that overcomes this challenge by controlling the distance between the net and the AUV using an optical camera and a supervised learning method. Specifically, we train a convolutional neural network to predict the distance between the net based on offline data. We then implement a feedback controller using the CNN predictions to guide the AUV’s movements. We evaluate our approach through experiments conducted in a 2x2x2m cube at CIRTESU, Castellón de la Plana, Spain. The results demonstrate the effectiveness of our proposed solution in addressing the problem of accurate net inspection without GPS, in controlled lighting conditions, and using a net that helps to run the computer vision algorithm. Further work will focus on adapting the algorithm to a more realistic scenario.
DC Microgrid for Power Sharing Model: Control Techniques Analysis

In recent years, because of the energy crisis and fossil fuels depletion, Renewable Energy Sources (RES) are more and more deployed to meet the energy demand and their strategic importance is rising more than ever. Most of them are natively sources in DC, moreover many loads, such as electric vehicles and most electronic devices, are natively loads in DC too. The latest European directives introduced the concepts of Renewable Energy Communities (REC) and collective self-consumption. These communities are increasingly attractive and, considering that the collective self-consumption (as power sharing) can be easily applied when the community is based on a DC microgrid, this network topology is more and more developed and studied. In this paper, the authors want to investigate the possible control approaches suitable for the management of a DC microgrid designed to realize collective self-consumption through a power sharing model. A possible architecture for the DC microgrid is presented, highlighting the main elements which can be actively included in control algorithms and the main tasks to be pursued. Different control approaches are analysed and compared, highlighting the main benefits and limits of specific applications and configurations.
Three-Phase AC/DC Converter fed Two Parallel Interleaved DC-DC Converters for Fast Charging Applications with Improved Power Quality

In this paper, a novel grid-interactive, off-board, three-phase EV charger and its control strategy are presented with the potential to achieve desired power factor. In the proposed configuration, the dual-stage (AC-DC and DC-DC) integrated charger is composed of a three-phase AC/DC converter and the two-phase parallel interleaved DC-DC converter sharing the same dc-link. The AC-DC converter interface between input mains and the dc-link. The parallel interleaved DC-DC converter is connected between the dc-link and the EV battery.The entire system is designed for a 3-ϕ, 10 kW rating with a broad range DC output voltage of 200–400V for EV batteries and 400V rms phase–to–phase, 50 Hz input supply. The designed converter is capability to provide enhanced low input and output current ripple with low input total current harmonics (THD), robust regulation of DC-link voltage, desired power factor and less ripple output voltage. The outcomes of the EV charger are anticipated by the MATLAB/ SIMULINK results which verify theoretical analysis. The near UPF of the system is improved by the suggested charger's compliance with the IEC 61000-3-2 standard, which also increases system reliability and efficiency. Based on the results, the system appears to be a viable fast charging solution for EVs, as it reaches 97.8% efficiency with 0.9997 PF and 2.54% THD at the source.
End-effector tracking in the micro-manipulation system with trajectory-aided particle swarm optimization

This paper presents a trajectory-aided particle swarm optimization (PSO) for tracking the end-effector in the micro-manipulation system. When the micro-environment suffers from significant interference with the result that the end-effector cannot be detected by micro-vision, failure of target tracking is often caused. Hence, the proposed method is developed to solve this problem by predicting the trajectory of the end-effector. A series of end-effector paths is firstly summarized. According to the Bayesian theory, the tracking problem is then converted to the problem of calculating the probability of motion paths when the loss of end-effector occurred. A cost function is established to determine the probability by optimization, and a decision criterion is established to judge whether the loss of target occurred. After that, the predicted trajectory is used to distribute particles. The proposed method preserves the advantages of PSO, such as less sensitivity to the initial conditions and high tolerance of the target model. Experiments with real micro-operation video for tracking the needle in different scenarios show that the proposed method greatly improves the tracking performance and time efficiency compared with the traditional PSO.
Distributed state and fault estimation of complex networks with time-varying topology

This paper studies the problems of state and fault estimation for a class of discrete-time complex networks with multiplicative fault and time-varying topology. The system state and actuator faults are augmented, and a suitable estimator is designed for each node. In order to obtain a more reliable estimation effect, a distributed estimator is designed, which only relies on information from local node and adjacent nodes. Then, by using inequality technique and mathematical induction, the upper bound of the estimated error covariance is obtained, and the filter gain is calculated to minimize the upper bound. Meanwhile, the filter is designed through solving recursive matrix equation. Besides, a sufficient condition for the existence of estimator is given. Finally, the effectiveness of the proposed method is verified by a simulation example.
Auto-tuning of Non-Linear Model Predictive Controllers for a Remote Operated Vehicle

In this work, an auto-tuning algorithm has been proposed to automatically obtain the weight adjustment parameter of one variable in a Nonlinear model predictive controller (NMPC). The NMPC is part of the double loop controller implemented in conjunction with an integral action.Furthermore, disturbances corresponding to model noise, sensor noise and second order waves effects have been applied to the remotely operated vehicle (ROV) nonlinear model, including during controller tuning. In adittion, an Unscented Kalman Filter (UKF) has been implemented for model state estimation and filtering of the disturbances that affect the model output.Finally, simulations have been carried out in which by observing the system output, it can be concluded that the proposed autotuning algorithm generates an adequate tuning of the control parameter.
Output Current Control for Two-Switch Boost Buck Converters in Fuel Cell Applications for DC Microgrids

Proper integration of Fuel Cells in DC microgrids requires meticulous current control of both the input and output of the power converter. A two - boost - buck converter topology was introduced in the literature for Fuel Cell applications; however, some limitations are to be pointed out in terms of a practical and easy-to-implement current control. This paper intends to propose an output current control for the two-switch boost-buck converter based on a cascade connection of digital PI controllers. Throughout the paper, previously published works are mentioned, and the control architecture is explained. Simulation results are presented, and the main conclusions are drawn.
A Meta-EMS for Lithium-battery/Supercapacitor Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs)HEV is an important branch of new energy vehicles, providing energy savings and reduced emissions by integrating multiple energy sources. However, a reasonable energy management strategy (EMS) is a prerequisite for the promotion and application of hybrid electric vehicles. Traditional EMSs can address specific tasks, limiting their adaptability to new scenarios. In this paper, we proposed a meta-EMS based on meta reinforcement learning algorithms, which can quickly adapt to new application scenarios. Experiments have shown that in the face of new HEVs, the proposed meta-EMS can achieve control performance similar to traditional reinforcement learning-based EMSs with only a few gradient steps. This approach offers a promising solution to develop adaptable and efficient energy management strategies for HEVs.
Cooperative Damping of Lane-change-induced Disturbances via Local Density Reduction

We propose a V2V connectivity-based algorithm to dissipate oscillations caused by lane changes, which we refer to as Cooperative Damping of Lane-change-induced Disturbances (CDLD) system. In CDLD, a connected automated vehicle (CAV) performing the lane change (the ego vehicle) informs the upstream CAV(s) in the target lane of the lane change. In turn these cooperating CAV (s) perform lane changes, which reduce the density in the target lane and damp the oscillations caused by the ego vehicle’s lane change. We evaluate CDLD in simulation and show that a single cooperating CAV can effectively improve traffic performance under certain traffic conditions. This strategy works both in fully connected and in mixed traffic, where unconnected human-driven vehicles share the road with CAVs.
H∞ Consensus Design and Optimization for Multi-Agent Systems with Time-Varying Delay

This paper takes multi-agent systems (MASs) under switching communication topologies as the research object, and studies the multi-agent systems consensus problem in view of communication delay in complex environment. Based on the average dwell time (ADT) method and Lyapunov function theory, sufficient conditions for the stability and robustness of the systems are given. The model uncertainty is estimated and compensated online in real time based on deep reinforcement learning (DRL) algorithms, which improves the robustness of the system against strong uncertainties and disturbances while ensuring the stability. Through comparative simulations, it is proved that the optimized control system has better control accuracy and dynamic performance, which can enhance the reliability and intelligence of multi-flight formation.
Preliminary study on the impact of acoustic emissions on fish

Anthropogenic underwater acoustic noise is known to affect marine life from mammals to sea birds. Due to the intense use of acoustics for communication and navigation by mammals (especially whales), there is a significant body of research devoted to the impact of underwater noise in this type of animals. On the contrary, studies on the impact on fish are much less common. This work presents a preliminary study on the impact of acoustic emissions in fish. Results obtained in a tank are presented both for biological parameters and speed estimation through camera. Differences in stress levels have been found as well as in speed that may suggest physical deficiencies. However, future work is needed to corroborate these results.
Robust Cooperative Output Regulation of Heterogeneous Multi-Agent Systems under Markovian Switching Topologies

This paper considers the robust cooperative output regulation of multi-agent systems (MASs) under Markovian switching topologies. Two novel distributed controllers are designed. It is shown that the resulting MASs can achieve robust cooperative output regulation in the sense of mean square. Finally, a simulation is given to show the effectiveness of the designed controller.
Distortion Correction of AUV-acquired Side-Scan Sonar Data

Side scan sonars (SSS) are types of sonars that produce representations of the sea-bed by emitting and receiving acoustic fan-shaped pulses perpendicularly to the motion of the sensor in the water. Generally, SSS are deployed from a towed vehicle pulled from a boat at high speed to avoid oscillatory motions and thus achieve a clean and consistent sea-bed scan. Our approach targets the use of SSSs on small AUVs with limited computing power, and travelling at slow speeds, where the waterfall is directly used for real-time classfication. Due to the increased oscillatory motion, the SSS waterfall tends to have significant distortion, that will preclude the classifier from obtaining correct results. The algorithm described in this paper performs a geometric correction of the sonar scanlines by taking into account the AUV’s change in height, yaw and pitch to calculate correctly their positions on the seabed. The data is mapped into a 2D grid using the scanline intensities to form a mosaic, and the missing mosaic pixels are interpolated using a Gaussian-based kernel. The waterfall is recreated by considering a modified AUV trajectory with smooth changes in heading and a null pitch. The results show a significant improvement in the waterfall output, removing unwanted distortions and displaying the seabed structures with higher geometric consistency. In addition, the concentration of the ping data on the waterfall is constant all around, compared to the varying contribution density in the original one.
Synthesis of Effects in Four Arctic Subregions

Abstract An assessment of impacts on Arctic terrestrial ecosystems has emphasized geographical variability in responses of species and ecosystems to environmental change. This variability is usually associated with north-south gradients in climate, biodiversity, vegetation zones, and ecosystem structure and function. It is clear, however, that significant east-west variability in environment, ecosystem structure and function, environmental history, and recent climate variability is also important. Some areas have cooled while others have become warmer. Also, east-west differences between geographical barriers of oceans, archipelagos and mountains have contributed significantly in the past to the ability of species and vegetation zones to relocate in response to climate changes, and they have created the isolation necessary for genetic differentiation of populations and biodiversity hot-spots to occur. These barriers will also affect the ability of species to relocate during projected future warming. To include this east-west variability and also to strike a balance between overgeneralization and overspecialization, the ACIA identified four major sub regions based on large-scale differences in weather and climate-shaping factors. Drawing on information, mostly model output that can be related to the four ACIA subregions, it is evident that geographical barriers to species re-location, particularly the distribution of landmasses and separation by seas, will affect the northwards shift in vegetation zones. The geographical constraints—or facilitation —of northward movement of vegetation zones will affect the future storage and release of carbon, and the exchange of energy and water between biosphere and atmosphere. In addition, differences in the ability of vegetation zones to re-locate will affect the biodiversity associated with each zone while the number of species threatened by climate change varies greatly between subregions with a significant hot-spot in Beringia. Overall, the subregional synthesis demonstrates the difficulty of generalizing projections of responses of ecosystem structure and function, species loss, and biospheric feedbacks to the climate system for the whole Arctic region and implies a need for a far greater understanding of the spatial variability in the responses of terrestrial arctic ecosystems to climate change.
Int&Int: A Two-Pathway Network for Skeleton-Based Action Recognition

Human action recognition has recently attracted a lot of researchers, while most of them mainly focus on the local dynamics or global information alone and cannot focus on both the intensity and the integrity of the action. In this work, we propose a two-pathway Int&Int network(Intensity&Integrity) for skeleton-based action recognition to satisfy both aspects, where the great complementarity between the two pathways further enhances the performance. Besides, for Integrity pathway, we apply the uniform sampling strategy. For Intensity pathway, we introduce the intensity-dependent sampling, where a clip composed of consecutive frames around the frame with the largest motion intensity is sampled. Moreover, we explain various definitions of the motion intensity containing different semantic information based on the extracted 2D human poses. For each pathway, the poses are represented by a 3D heatmap volume and 3D-CNNs of both pathways have the same architecture. Late fusion is used to ensemble them. The model is evaluated on two action recognition datasets, FineGYM-99 and HMDB-51, and it achieves superior performance on both of them. The code has been shown at https://github.com/SarahQi666/Int-and-Int.
Data and Knowledge Visualization with Virtual Reality Spaces, Neural Networks and Rough Sets: Application to Geophysical Prospecting

Visual data mining with virtual reality spaces are used for the representation of data and symbolic knowledge. The approach is illustrated with data from a geophysical prospecting case in which partially defined fuzzy classes are present. In order to understand the structure of both the data and knowledge extracted in the form of production rules, structure-preserving and maximally discriminative virtual spaces are constructed. High quality visual representations can be obtained using Samann and nonlinear discriminant neural networks. Rough set techniques are used for demonstrating the irreducibility of the set of original attributes and for learning the symbolic knowledge. Grid computing techniques are used for constructing sets of virtual reality spaces and for assessing the behavior of some of the neural network parameters controlling the quality of the virtual worlds. The general properties of the symbolic knowledge can be found with greater ease in the virtual reality space whereas both the prediction of unknown objects to the target class, as well as a derivation of a fuzzy membership function from the virtual reality space and the neural network results are obtained.
Estudo dos impactos de um sistema fotovoltaico conectado à rede elétrica utilizando análises QSTS

Esta dissertacao apresenta um estudo da operacao de uma rede teste trifasica de media tensao com a interligacao de um sistema fotovoltaico de 1,0 MWp. Dois metodos de analise sao utilizados para avaliar os impactos deste sistema fotovoltaico, sendo estes metodos as analises estaticas convencionais eas analises conhecidas como Quasi-Static Time-Series Analysis. Apesar de cada rede eletrica apresentar caracteristicas unicas, e importante a utilizacao de sistemas testes, que simulam as caracteristicas de sistemas reais, para analisar que tipos problemas podem surgir e entao buscar alternativas, se necessario. Os impactos avaliados se referem as perdas no sistema, minimizadas com a correta alocacao da geracao, perfil de tensao e curva de posicao do tap, no caso de transformador com comutacao automatica de tap. Contata-se que o ponto de conexao do sistema fotovoltaico e o mais influenciado pela sua conexao a rede. As analises QSTS possibilitam avaliar corretamente a iteracao entre carga e geracao, efetuando o fluxo de potencia consecutivo atraves de dados estimados para as curvas de carga e de irradiância solar ao longo de 168 horas. Ja as analises convencionais consideram apenas condicoes criticas de operacao, como por exemplo, carga leve ou nominal e geracao nula ou maxima, nao avaliando entao diferentes cenarios de operacao que ocorrem na pratica. Os sistemas fotovoltaicos podem trazer muitos beneficios aos sistemas eletricos, como melhoria do perfil de tensao de atendimento ao consumidor, reducao de perdas nas linhas, alem da reducao nos impactos ambientais. Entretanto, com o aumento de geracao fotovoltaica distribuida na rede, e necessario estar atento aos impactos que isto pode causar atraves de estudos de interconexao.
Empirical tests of a simple pricing model for sugar futures

In this paper we test the empirical implications of a simple pricing model for commodity futures for the marginal process of prices of Sugar futures. According to the prícing model, the futures price bias depends linearly on the conditional variance. We find significant coefficients, from monthly as well as daily data, if the conditional variance is modelled using the GARCH-M model put forward by Engle, Lillien and Robins (1987). These estimates imply contango in the futures market and a net hedging demand on the long side of it.
Initial segments of the Σ0 2 enumeration degrees

Using properties of K -pairs of sets, we show that every nonzero enumeration degree a bounds a nontrivial initial segment of enumeration degrees whose nonzero elements have all the same jump as a . Some consequences of this fact are derived, that hold in the local structure of the enumeration degrees, including: There is an initial segment of enumeration degrees, whose nonzero elements are all high; there is a nonsplitting high enumeration degree; every noncappable enumeration degree is high; every nonzero low enumeration degree can be capped by degrees of any possible local jump (i
Ethnobotanical Study of Medicinal Plants in the Province of Settat (Morocco)

trapezoidale, est situee au centre du Maroc dans la region de Chaouia-Ourdigha, entre l’Atlas et l’Atlantique, sur 33°00’22.38’’ de Latitude Nord, 7°37’08.77’’ de Longitude West et 290 m d’Altitude. S’etendant sur la plaine Atlantique, elle couvre une superficie d’environ 10 000 Km² (soit 23.5% du territoire de la region du centre) (DPA, 2006). Elle est limitee au Nord par la region du Grand Casablanca, a l’Est par les provinces de Benslimane, Khemisset et Khouribgha, au Sud par les provinces de Beni Mellal et Kalaâ d’Sraghna et a l’Ouest par la province d’El Jadida et l’Ocean Atlantique (Figure 1). Les principaux cours d’eau sont Oued Oum Er Rabia qui traverse la province sur environ 100 km et d’autres oueds a debits variables dont le plus important est Oued Tamadroust