Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact on the concentration and chemical characterization of PM2.5 and its relation to the reduction of the content of sulfur in diesel in a traffic area of the Valle de Aburrà: 2015-2019

Taking as a reference the regional and national context on the subject of air quality and climate change, it has been financed by the United Nations UNU - International Atomic Energy Agency IAEA, Ecopetrol and Área Metropolitana del Valle de Aburrá, a project to mitigate the climate change, with the objective of evaluating the impact of the improvement of diesel delivered by Ecopetrol and strategies of the PIGECA Air Quality Management Plan. A characterization campaign was carried out at a traffic station between 11/23/2018 and 02/16/2019, locating two low-volume meters at Polytechnic traffic monitoring network station under protocols accredited by IDEAM and International Laboratories. The samples were analyzed for elemental, ionic and carbonaceous composition, applying methods (ICP-MS), chromatography and NIOSH 5040 protocol. For the comparison of the changes in the chemical composition of PM2.5, concentration and speciation historical reports for periods of diesel consumption of 25 and 50 ppm of sulfur (S) were analyzed. An analysis of the concentration distribution between march 2008 and february 2019 shows an average decrease of 4.8 (μg / m3) and 5.1 (μg / m3) for 75% of the data. Based on the results of the characterization and previous studies, a greater adjustment of the ionic balance in the composition of the samples in the 2019 campaign was evaluated, showing the effect of the reduction of sulfates of 1.4% in the PM2.5 mass as an impact of the fuel quality improvement and a 2.2% EC reduction associated with the optimization of combustion systems in furnaces and boilers, renewal of the vehicle fleet in public transport and implementation of mobility and sustainable strategies in the metropolitan area. For the first time in the region, a concentration of the potassium ion (K +) is detected, which may be associated with the increase in the consumption of biodiesel in the region. The significant increase in the OC / EC ratio in the period of consumption of lower sulfur diesel (S) and some strong correlations such as that found between SO42– and NH4 +, infer a greater atmospheric reactivity implying the formation of secondary PM2.5 and consequently, a high need to continue with strategies to control primary emissions from all combustion sources.
An Unsupervised Machine Learning Technique for Recommendation Systems

Due to the advancement of the Internet, various kinds of data are easily found online which helps users to find useful information which are of their interest. However, the exponential growth of data has caused it to be complex and huge, so it has become difficult to filter valuable information from it. Recommendation systems can help to overcome this issue and give recommendations to the users which matches the area of their interest. Most of the systems rely on a rating prediction algorithm where the items are taken as recommended for a user if the user’s predicted rating is high on those items. This research aims to increase the accuracy and reduce the processing time for recommendation using the prediction algorithm based on the unsupervised machine learning method. The proposed solution consists of Autoencoder to enhance the accuracy of prediction and reduce the processing time. Partially observed interaction matrix is used as input for the neural network model which outputs a complete rating matrix. The proposed solution achieved an improvement by 1.83%, 0.85% and 3.72% in cold start case for MSE, RMSE and MAE evaluation metrics respectively. The proposed solution performs better and will be used in cold start cases for datasets where timestamp value (user creation time) is used.
Design of a DC-DC Boost Converter of Hybrid Energy Harvester for Low-Power Biomedical Applications

The growing need for alternative sources for powering Sensors and wearable devices has led to many energy harvesting solutions by which we can consequently replace batteries. The typically small amount of energy provided needs to be efficiently managed to be able to supply biomedical applications. In biomedical sector, continuous power requirement is crucial. Available human energies can be utilized for this purpose. But as only small and limited energies can be extracted from human body, the main challenge is to enhance this low energy output with the design of proper electronic circuits to capture and increase the energy output. In a lot of cases one harvester is not sufficient. As a solution for this issue we found combinations of converters in hybrid structures an efficient technique for boosting the amount of converted energy. However, energy converters need to be designed in a proper manner fitting to the properties of ambient sources. This work reports about the design of a boost converter for an hybrid energy harvesting solution using three ambient sources, which are Radio Frequency, thermal energy and vibration for powering low power biomedical applications. In this work the use of power extracted from human body is investigated for both thermal and vibration source. The proposed boost converter is used to boost the limited input voltage from thermal, radio frequency, and vibration. The inductance in the circuit is varied from 0.3 to $\boldsymbol{1.4} \mu \mathrm{H}$ for thermal source and from 0.10 to $\boldsymbol{0.21}\mu \mathrm{H}$ for the hybrid inputs in order to achieve the desired output range. For the hybrid structure the optimum inductance value is set to $\boldsymbol{0.21} \mu \mathrm{H}$ to achieve the required output voltage of 3.0 to 5.5V. The converter consumes 0.28 mW of power, delivers 3.3 mW of output power, and is 91.6 % efficient for a 500 mV input. All circuits are designed and simulated using LTSPICE software.
Full-Speed Control of A Novel Variable Speed Drive Using Four-level Hybrid Clamped Converter

The four-level hybrid clamped converter (4L-HCC) is emerging as a new multilevel topology for medium/high-voltage motor variable speed drives. However, the internal capacitor voltage has unstable issues at extra low frequencies, limiting the 4L-HCC’s application. This paper presents an integrated control scheme of the 4L-HCC and an electrical machine. Compared with the traditional PWM method, the proposed method can effectively suppress the excessive voltage ripples at low speed. The proposed control has features of easy implementation, accurate/fast current and torque response, and strong capacitor voltage balancing capability. Four-quadrant operation with high constant torque current is realized. Simulated and experimental results are used to validate the effectiveness of the proposed integrated method.
Virtual Impedance Regulator for the Three-Phase Inverter Stand-Alone Distributed Generation System

In the distributed power generation system, the stand-alone inverter as the main power interface unit, its stability is directly related to the reliable operation of the system. Impedance-based analysis methods can effectively study the interaction stability between stand-alone inverter and EMI filter, so impedance modeling of stand-alone inverter is an important prerequisite for impedance stability analysis. The virtual impedance regulator is proposed in this paper to stabilize the cascaded system. According to the input impedance of the standalone inverter, the characteristic of the negative impedance is modified only in a small range of frequency. Therefore, both of the dynamic performance and the stability of the system can be ensured.
Joint User-Subcarrier Pairing and Power Allocation for Uplink ACO-OFDM-NOMA Underwater Visible Light Communication Systems

Underwater visible light communication (UVLC) is a promising technique to provide high-speed data transmission in the water. Since it is quite difficult to supply power to the underwater devices in the complex and severe marine environment, energy consumption would be a main consideration in the practical deployment of UVLC system. In this article, we investigate an asymmetric clipped optical orthogonal frequency division multiplexing and non-orthogonal multiple access (ACO-OFDM-NOMA) scheme for the uplink UVLC system to realize energy saving and support massive device connectivity. A mixed integer nonlinear programming problem of jointly optimizing the user-subcarrier pairing and power allocation is formulated to maximize the system utility. To tackle this problem, we propose a low-complexity iterative algorithm to obtain an optimal solution. Simulation results verify that the proposed algorithm converges quickly, and the uplink ACO-OFDM-NOMA system using the proposed algorithm has better utility compared to the uplink ACO-OFDM-NOMA system using the greedy algorithm and the traditional uplink ACO-OFDM access (ACO-OFDMA) system.
Threats Classification Method for the Transport Infrastructure of a Smart City

The concept of a Smart City is the forefront in the development of our near future. One of the most important parts of such cities is the transport infrastructure. Threats implementation for such infrastructure can have critical consequences for a city. To prevent such threats in the future we should investigate and counteraction them in the present. The first step in this way can be the creation of a threats classification for the transport infrastructure of a Smart City. The paper describes a set of methods that can be used for this. As a result, a scheme for categorical and cluster classifications creation is proposed. The categorical classification relies on the division of categories for elements grouping. The cluster one uses machine learning methods for elements grouping. Examples of classifications applying for the top-10 threats from the official state database are given.
Impacts of Voltage Feedforward Control on Impedance Characteristics and Stability of MMC-based STATCOM

Recently, the voltage feedforward control is widely used in the MMC-based static synchronous compensator (STATCOM) to improve its performance of starting, disturbance rejection, suppression of background harmonics and unbalance in the grid voltage. However, this control may lead to a challenge to the system stability, due to its large impact on the frequency characteristics of STATCOM. Hence, sequence impedance modeling and analysis method are used in this paper to understand the effect of the voltage feedforward control on the system stability for providing a basis of impedance shaping design. The frequency-domain small-signal impedance models of STATCOM with a typical voltage feedforward control in the dq-frame and αβ-frame are developed by double-harmonic linearization, respectively. The effects of the voltage feedforward control in the two frames on the impedance characteristics and system stability are analyzed and compared. A cased study based on a wind farm in Western China is presented to confirm the analysis by simulations.
A Single-Phase Five-Level Transformer-less PV Inverter

Single-phase five-level transformer-less inverter has been widely used in photovoltaic (PV) applications because of low total harmonic distortion (THD) and excellent output quality. However, a serious problem for transformer-less inverters is the leakage current issue which will lead to safety problems. This paper proposes a single-phase five-level transformer-less inverter to eliminate the leakage current. The circuit consists of eleven active switches and can keep the common mode voltage half of the DC bus voltage. The voltages of flying capacitors are balanced within one switching cycle through selecting the proper switching states. The mechanism of suppressing the leakage current is analyzed in detail with operational modes and modulation strategy. A 1 kW single-phase five-level inverter prototype is built to verify the validity and flexibility of the proposed topology and modulation.
Graph Connectivity Control of a Mobile Robot Network With Mixed Dynamic Multi-Tasks

Given a team composed of groups of robots with different abilities and different tasks, we propose a control solution that allows maintaining the connectivity between the agents whilst securing the execution of the different tasks assigned to the team. The specific notion of connectivity adopted here is that the different agents have to mutually remain in line-of-sight, and this condition is guaranteed in presence of obstacles. In the computation of the graph topology, based on Minimum Spanning Tree (MST), each agent takes into account the surrounding environment, its own task and the tasks of its neighbours. Our solution permits the execution of heterogeneous tasks and the task dynamic reallocation between the robots when required by the situation. The approach relies on a sound theoretical framework, which is discussed in this letter, while its practical feasibility is shown through simulations and experimental data using real robotic platforms.
Investigation of Axial Flux Near-Wheel motor for Electric Vehicle

Distributed drives increase the flexibility of electric vehicles, but puts forward higher requirements on the size and dynamic performance of the motor. An axial flux near-wheel motor suitable for distributed driving is proposed, which adopts a double-stator single-rotor structure. Firstly, the electromagnetic design of the near-wheel motor is carried out to reduce the total harmonic distortion (THD) of back electromotive force (EMF), reduce torque ripple and rotor eddy current loss. The Maxwell software was used to analyze the stator slot shape, pole slot fit, permanent magnet structure and winding structure of the near-wheel motor. Then, the structural design was carried out. A spiral cooling water channel is designed for the chassis to improve the overload capacity of the motor. At the same time, the front and rear cover of the motor, the casing and the rotating shaft have been designed to reduce weight. The strength analysis and modal analysis of the motor were performed through Ansys software. Finally, the efficiency characteristics test, field weaking performance test and temperature rise test were carried out on the near-wheel motor.
Validação do uso de n-alcanos como indicadores para estudos nutricionais em veados - catingueiro (Mazama gouazoubira).

Feed intake and digestibility estimates were determined by the total fecal collection method of 8 deer (Mazama gouazoubira) receiving a pelleted diet of 63.9% alfalfa hay, 16.9% corn starch, 14.7% yeast (Saccharomyces cerevisiae), 2.5% mollasses, 1.5% mineral-vitamin mix, and 0.5% NaCl on DM basis. n-alkane tracer method was used in parallel with total fecal collection to validate its application on wild small ruminants. The mean DM feed intake and water intake during the collection period were 24g ± 2.5 and 67 mL ± 11.5 per kg BW, and the animals did not present weight losses during the experimental period. When these values were expressed on kg of metabolic live weight, the animals consumed 48g ± 5.2 of DM and 136.4 mL ± 23.8 of water. Digestible energy intake was estimated at 1,089 kcal/day. The mean coefficients of apparent digestibility observed by total fecal collection for dry matter was 61.6% ± 1.7; organic matter 63.9% ± 1.6, nitrogen 69.7 % ± 3.4, neutral detergent fiber 31.7% ±
Land cover classification of GeoEye image based on Convolutional Neural Networks

Land cover classification is an important topic in remote sensing. It provides useful information for environmental projects and ecosystem management. To succeed the mapping task, interest should be given to select a suitable classification algorithm. The recent advances in machine learning have shown the very great performances of the deep neural networks for many applications including the land cover mapping. The purpose of this work is to exploit the performance of Convolutional Neural Networks CNN to classify a GeoEye satellite image. Two CNN models with three and four convolution layers are tested to show the impact of the number of layers on the accuracy result. The two architectures outperform, with an overall accuracy more than 81%, the pixel based classification using Random Forest classifier.
Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids

For a robot with redundant sensors and actuators distributed throughout its body, it is difficult to construct a controller or a neural network using all of them due to computational cost and complexity. Therefore, it is effective to extract functionally related sensors and actuators, group them, and construct a controller or a network for each of these groups. In this study, the functional and spatial connections among sensors and actuators are embedded into a graph structure and a method for automatic grouping is developed. Taking a musculoskeletal humanoid with a large number of redundant muscles as an example, this method automatically divides all the muscles into regions such as the forearm, upper arm, scapula, neck, etc., which has been done by humans based on a geometric model. The functional relationship among the muscles and the spatial relationship of the neural connections are calculated without a geometric model. This study is applied to muscle grouping of musculoskeletal humanoids Musashi and Kengoro, and its effectiveness is verified.
Thermal Modeling of Power Semiconductor Devices with Heat Sink Considering Ambient Temperature Dynamics

Thermal characteristics of semiconductor devices play important roles in reliability evaluation. There is a common problem that existing thermal models ignore ambient temperature dynamics while predicting the junction temperature of semiconductor devices, especially when the heat sink is connected to a semiconductor device. In this paper, the frequency domain analysis method is used to analyze the influence of the ambient temperature dynamics on the junction temperature of the power semiconductor device with the heat sink. The results show that the slow ambient temperature dynamics are not negligible for power semiconductor devices with heat sink which has large time constants when junction temperature prediction is carried out. A universal method for obtaining first-order Cauer parameters of a heat sink is given in this paper. Furthermore, an improved frequency domain thermal model is proposed, which simultaneously considers self-heating and ambient temperature variation. It can provide authentic temperature estimates. The validity of the proposed model is verified by simulation in MATLAB/Simulink with PLECS.
Numerical Investigation of orbital angular momentum mode purity in Inverse Parabolic Graded index fiber

Orbital angular momentum is seriously considered as an additional degree of freedom that enhances the capacity transmission in optical fiber communications. One of the major issues in OAM-fibers is the OAM-purity degradation provoking intrinsic crosstalk. Both issues are together considered as a manifestation of spin-orbit coupling. In this work, we numerically assess the OAM mode quality in recently proposed OAM-fiber referred as inverse parabolic graded index fiber (IPGIF). Our analysis addresses the purity of OAM modes/channels and the equivalent induced intrinsic crosstalk in IPGIF. This helps to identify the key parameters that have direct impact on the purity performance.
A Study on Resonance Suppression Control Based on Virtual Resistance Concept for Parallel Inverters in Islanded Microgrid

This paper studies a resonance suppression control based on virtual resistance concept for parallel inverters in islanded microgrids. At the inverter systems with LC output filters, resonances often occur due to various reasons. To suppress the resonances, additional resistances are often used to dampen the system (passive damping). However, additional resistance inevitably causes additional losses. On the other hand, many active damping approaches have also been studied. In this paper, we study one of the active damping methods, the resonance suppression control based on virtual resistance concept. In particular, the resonance suppression control implemented at the current-controlled inverter and that implemented at the voltage-controlled inverter in islanded microgrids are comparatively studied. The effectiveness is evaluated by analyses of frequency characteristics, simulation studies, and experimental results in the case of two parallel inverters in islanded microgrids.
Auxiliary Power Supply with SiC MOSFET for Wide-Range High Voltage Input Applications

For high power and high voltage (HV) applications, low power subsystems must have an auxiliary power supply that can take wide-range high voltage input and can generate low DC voltage at the output. In order to improve the efficiency, silicon carbide (SiC) MOSFET with smaller on resistance are considered to replace silicon (Si) MOSFET, and the static and dynamic characteristics of two devices are compared and analyzed. Besides, the Quasi-Resonant (QR) topology is selected to reduce losses and Electromagnetic Interference (EMI) level. In this paper, a SiC-based QR flyback auxiliary power supply for high voltage applications is proposed, and the design scheme for auxiliary power supply is validated.
Design and Implementation of Bidirectional Full-Bridge Soft-Switching DC/DC Converter with Natural Clamping

The main purpose of this paper is to design and implement a bidirectional full-bridge phase-shift (BPSFB) converter with snubber-less natural clamping for application such as EVs. In the charging stage, the phase shift control is used to achieve zero-voltage-switching and two additional clamping diodes and a small resonating inductance are used for natural clamping. In the discharging stage, the current fed technique is used for transferring energy between the battery and dc bus. By applying the dc bus voltage to the leakage inductance of the transformer for an appropriate time duration, during the inductor energy storing period in battery side, the voltage spike on main power switches can be reduced without snubber circuits. Finally, the digital signal processor, TMS320F28035, is used as the controller to realize the laboratory prototype with bus voltage 400 V, battery voltage 350-400 V and rated power 4kW. During the charging stage, the maximum efficiency is 98.2% at 75% load and during discharge stage, the efficiency is 96.8% at 50% load.
Delay Compensation Amongst Aggregated Storage Assets Providing Fast Frequency Regulation

This paper investigates a delay compensation control for coordinating aggregated energy storage systems (ESSs) of dissimilar characteristics to provide fast frequency regulation, particularly the UK National Grid enhanced frequency response (EFR) service. The dynamic model of a generic micro-grid system comprising synchronous generator, load, and EFR-providing ESS is established. Theoretical analyses of the compensation effects are conducted using describing function techniques to model the EFR nonlinearities. The theoretical study indicates the compensation control’s beneficial effects through an increase in the micro-grid system’s equivalent regulating energy (the inverse of the frequency droop coefficient). The analytical results are validated by RTDS simulations of a representative micro-grid system, which is based on the measured characteristics of a real aggregated ESS fleet, including a 180kWh/240kW commercial battery ESS asset and a 1MWh/2MW battery asset located 100 miles apart. Simulations show that the frequency deviation of the micro-grid system in response to a 10% sinusoidal power imbalance at the network’s natural angular frequency could be reduced by 74% through the inclusion of two BESSs providing EFR with a total capacity of 10% of the micro-grid; a further reduction of 42% can be obtained through the implementation of the proposed delay compensation method.