Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Multimode Battery Charger with Dynamic Voltage Tracking Control

This paper presents a multimode battery that is composed of a dc-dc buck converter and a charging circuit. The charging circuit can provide a ripple-free charging current and automatically switches among trickle current (TC), constant-current (CC), and constant-voltage (CV) stages depended on the battery voltage. The buck converter with a dynamic voltage tracking (DVT) control produces a dynamic supply voltage to closely follow the battery voltage, resulting in considerable efficiency improvement of the charging circuit. To reduce power loss of the buck converter, non-switching control and zero current detection are utilized to increase the efficiency of the battery charger at CV and TC stages. The battery charger was designed with a 0.35-μm CMOS technology. The simulation results are shown to verify the theoretical analysis of the proposed charger.
Evaluation of the Detectability of Bearing Faults at Different Load Levels Through the Analysis of Stator Currents

Bearing faults are among the most relevant electrical machines failures. Their early and reliable detection can imply significant economic savings for the industry. This paper discusses the effects of bearing faults in the stator current, performing steady state analysis, under different load levels. A motor-generator test bench has been built in order to simulate real working conditions. Motor current signature analysis (MCSA) is used to study steady-state regime. It is a well-known and widespread technique for detecting rotor-related faults but not so commonly used in bearing diagnosis. The objective is to study the evolution of the harmonic components in different cases of use, taking into account several types of progressive faults. Tests were carried out with healthy and damaged induction motors under different fault severities and confirm the potential of the technique for detecting such faults. Moreover, the results show the variation of the harmonics amplitudes with the load; the paper specifies the most suitable load levels for the application of the technique.
Learning Word Representations with Cross-Sentence Dependency for End-to-End Co-reference Resolution

In this work, we present a word embedding model that learns cross-sentence dependency for improving end-to-end co-reference resolution (E2E-CR). While the traditional E2E-CR model generates word representations by running long short-term memory (LSTM) recurrent neural networks on each sentence of an input article or conversation separately, we propose linear sentence linking and attentional sentence linking models to learn cross-sentence dependency. Both sentence linking strategies enable the LSTMs to make use of valuable information from context sentences while calculating the representation of the current input word. With this approach, the LSTMs learn word embeddings considering knowledge not only from the current sentence but also from the entire input document. Experiments show that learning cross-sentence dependency enriches information contained by the word representations, and improves the performance of the co-reference resolution model compared with our baseline.
Is this Sentence Difficult? Do you Agree?

In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity. We collect a large corpus of sentences rated with judgments of complexity for two typologically-different languages, Italian and English. We test our approach in two experimental scenarios aimed to investigate the contribution of a wide set of lexical, morpho-syntactic and syntactic phenomena in predicting i) the degree of agreement among annotators independently from the assigned judgment and ii) the perception of sentence complexity.
Analysis and Compensation of Dead-Time Effect in SiC-Device-based High-Switching-Frequency Inverters

The ultra-fast-switching silicon carbide (SiC) devices enable power inverters to achieve extremely high switching frequencies (e.g. 100kHz). However, as a limiting factor at high switching frequencies, the dead-time of the conventional pulse width modulation (PWM) can cause serious low-frequency current/voltage harmonics and reduce the dc-link voltage utilization. It is timely to investigate the aggravated dead-time effect on the performance of high-switching-frequency inverters with the increasing application of wide-bandgap devices. In this paper, the dead-time effect and its compensation at high switching frequencies are investigated. The voltage loss and the maximum linear modulation index are derived considering both the dead-time and the switching frequency. The increased amplitude of modulation wave by the dead-time compensation is illustrated, indicating a significantly decreased linear modulation region in high-switching-frequency conditions. A dead-time elimination PWM scheme is therefore adopted, which is equivalent to the conventional PWM with dead-time compensation while has a larger linear modulation region but a slight lower efficiency. The efficiency difference can be very small or even negligible at high switching frequencies and high operating powers. The analyses are finally validated by the experimental results at 100kHz on a three-phase inverter with SiC MOSFETs and SiC Schottky diodes.
More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing

We present an extension of an annotated corpus of short argumentative texts that had originally been built in a controlled text production experiment. Our extension more than doubles the size of the corpus by means of crowdsourcing. We report on the setup of this experiment and on the consequences that crowdsourcing had for assembling the data, and in particular for annotation. We labeled the argumentative structure by marking claims, premises, and relations between them, following the scheme used in the original corpus, but had to make a few modifications in response to interesting phenomena in the data. Finally, we report on an experiment with the automatic prediction of this argumentation structure: We first replicated the approach of an earlier study on the original corpus, and compare the performance to various settings involving the extension.
An Advanced Mobility-Aware Algorithm for Joint Beamforming and Clustering in Heterogeneous Cloud Radio Access Network

Heterogeneous Cloud Radio Access Networks (H-CRANs) are a promising cost-effective architecture for 5G system which incorporates the cloud computing into Heterogeneous Networks (HetNets). We consider in this work the joint beamforming and clustering (user-to-Remote Radio Head (RRH) association) issue for downlink H-CRAN to solve the sum-rate maximization problem under fronthaul link capacity and per-RRH power constraints. The main objective is to address the beamforming and user association process over time by taking into account the user mobility as a key factor to tune the solution's parameters. More precisely, based on the mobility profile of users (mainly velocity), we propose an advanced Mobility-Aware Beamforming and User Clustering (MABUC) algorithm which selects the best Channel State Information (CSI) feedback strategy and periodicity to achieve the targeted sum-rate performance while ensuring the minimum possible cost (complexity and CSI signaling). MABUC inherits the behavior of our previously proposed Hybrid algorithm which periodically activates dynamic and static clustering strategies to manage the allocation process over time. MABUC algorithm, however, takes into account the user mobility by using a CSI estimation model which can improve the algorithm performance compared to reference schemes. Our proposed algorithm has the benefit to meet the targeted sum-rate performance while being aware and adaptive to practical system constraints such as mobility, complexity and signaling costs.
A New ASIC Structure With Self-Repair Capability Using Field-Programmable Nanowire Interconnect Architecture

Total ionizing dose tolerance is one of the essential features of the electronic devices used to control and monitor the vital operations in extreme environments. Most of today’s commercial off-the-shelf systems do not have this capability unless they are shielded with at least 10 cm of tungsten. Hence, application-specific integrated circuits (ASICs) are developed to achieve the desired performance in extreme environments. In this paper, a new ASIC structure with self-repair capability has been proposed using the field-programmable nanowire interconnect architecture. Our proposed structure includes a nonprogrammable part as circuit functionality and a reconfigurable part to improve the circuit reliability. The performance of the proposed structure is compared with some static and dynamic fault-tolerant approaches. According to the simulation results, the proposed structure can tolerate several permanent faults during the run time with minimum delay, power consumption, and area overhead.
Early Fault Detection of Aircraft Components Using Flight Sensor Data

In this paper, a classification-based anomaly detection model is proposed to detect the aircraft component fault by exploring the historical flight sensor data. Detection of the aircraft component fault is formulated as a classification problem. Firstly, several sensors relevant to the fault are selected using statistical analysis. Secondly, flight phase-based statistical features are extracted using the selected sensors. Thirdly, several important features are selected using correlation analysis with the flight label. Finally, the random forest algorithm is applied to build the fault classification model based on the selected features. Experimental results show the proposed method can detect the component fault earlier than or as early as the current aircraft alarming system.
Recovering Missing Characters in Old Hawaiian Writing

In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach, which approximately composes an FST with a recurrent neural network language model.
Training Model For Localization of Interfering and Illegal Transmission for Military Forces in uhf Bands

Technology is an important factor in aspect related with electronic warfare because it aims for secure communications and interfering signals could be detected. In Colombia this technology aids to counter back narcotraffic and guerrilla forces with detection and localization equipment. This work depicts a training model for military forces to understand detecting interfering and enemy communications through the usage of HTZ Warfare software and IDA2 devices. It is focused in UHF bands between 400 and 800MHz. With this design, military forces in Colombia could train and detect illicit communications in diverse rural regions of the country.
Generating Syntactic Paraphrases

We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, text-to-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.
Rotor Design of a Line-Start Synchronous Reluctance Machine with Respect to Induction Machine for Industrial Applications

This paper introduces a unique rotor design for line-start synchronous reluctance machines. Efficiency and synchronization capability are determined and a comparison between line-start synchronous reluctance machines and induction machines is drawn. For the design and simulation, FEM calculations were used and the results were experimentally verified. Within the frame of this paper a 4 kW rated induction machine with an efficiency class rating of IE3 was chosen as a benchmark motor. The new rotor-cage design of the line start reluctance machine decreases the motor losses at steady state by about 28%. This promising approach could be used to upgrade induction machines in industry environments instead of replacing them. Nevertheless, a challenge in the design is a good balance between the steady state performance and synchronization capability, whereby the range of industrial applications is limited for high efficient line-start synchronous reluctance machines.
Automatically Detecting the Position and Type of Psychiatric Evaluation Report Sections

Psychiatric evaluation reports represent a rich and still mostly-untapped source of information for developing systems for automatic diagnosis and treatment of mental health problems. These reports contain free-text structured within sections using a convention of headings. We present a model for automatically detecting the position and type of different psychiatric evaluation report sections. We developed this model using a corpus of 150 sample reports that we gathered from the Web, and used sentences as a processing unit while section headings were used as labels of section type. From these labels we generated a unified hierarchy of labels of section types, and then learned n-gram models of the language found in each section. To model conventions for section order, we integrated these n-gram models with a Hierarchical Hidden Markov Model (HHMM) representing the probabilities of observed section orders found in the corpus, and then used this HHMM n-gram model in a decoding framework to infer the most likely section boundaries and section types for documents with their section labels removed. We evaluated our model over two tasks, namely, identifying section boundaries and identifying section types and orders. Our model significantly outperformed baselines for each task with an F1 of 0.88 for identifying section types, and a 0.26 WindowDiff (Wd) and 0.20 and (Pk) scores, respectively, for identifying section boundaries.
Using machine learning to balance energy cost and emissions in optical networks

The goal of energy cost-aware routing and wavelength assignment (RWA) is to minimize the total electricity expenditure in an optical network. While effective, simply aiming to reduce the electricity consumed does not necessarily mitigate the environmental impact. A new approach is required to simultaneously reduce both the electricity cost and emissions produced as a by-product of RWA. We present a method for doing so through the use of mixed-integer linear programming (MILP), which can find the optimal solution that minimizes the electricity cost of RWA for a static set of requests while keeping emissions under a specified cap. This objective is quantitatively compared to alternative goals, including directly minimizing the emissions produced, reducing the length of established paths, and balancing reductions in both emissions and electricity cost simultaneously. As MILP computation is costly and the results are required in near real time to react to changing prices, we present a solution that employs a well-known supervised machine learning algorithm, logistic regression, that predicts the cost saving paths in real time. Using a dataset of 808,024 records (70% for training and 30% for testing) output from the MILP, we find that this logistic regression model predicts the most cost-efficient path with 92.5% accuracy.
Modelling of Cross Saturation Effect in Interior Permanent Magnet Synchronous Machines Using Magnetic Equivalent Circuits

This paper presents a method for calculation of flux linkages in the direct and quadrature axes of interior permanent magnet synchronous machines with a single U shaped barrier. The method is based on the magnetic equivalent circuit. For obtaining the correct results, it is essential to model the cross-saturation effect, especially for q axis current excitation due to magnetomotive force of the permanent magnets. The proposed method is fast and accurate. The paper describes modeling techniques and provides results as well as the comparisons with finite element method.
Novel Ethanol- and 5-Hydroxymethyl Furfural-Stimulated β-Glucosidase Retrieved From a Brazilian Secondary Atlantic Forest Soil Metagenome

Beta-glucosidases are key enzymes involved in lignocellulosic biomass degradation for bioethanol production, which complete the final step during cellulose hydrolysis by converting cellobiose into glucose. Currently, industry requires enzymes with improved catalytic performance or tolerance to process-specific parameters. In this sense, metagenomics has become a powerful tool for accessing and exploring the biochemical biodiversity present in different natural environments. Here, we report the identification of a novel β-glucosidase from metagenomic DNA isolated from soil samples enriched with decaying plant matter from a Secondary Atlantic Forest region. For this, we employed a functional screening approach using an optimized and synthetic broad host-range vector for library production. The novel β-glucosidase – named Lfa2 – displays three GH3-family conserved domains and conserved catalytic amino acids D283 and E487. The purified enzyme was most active in pH 5.5 and at 50°C, and showed hydrolytic activity toward several pNP synthetic substrates containing β-glucose, β-galactose, β-xylose, β-fucose, and α-arabinopyranose, as well as toward cellobiose. Lfa2 showed considerable glucose tolerance, exhibiting an IC50 of 300 mM glucose and 30% of remaining activity in 600 mM glucose. In addition, Lfa2 retained full or slightly enhanced activity in the presence of several metal ions. Further, β-glucosidase activity was increased by 1.7-fold in the presence of 10% (v/v) ethanol, a concentration that can be reached in conventional fermentation processes. Similarly, Lfa2 showed 1.7-fold enhanced activity at high concentrations of 5-hydroxymethyl furfural, one of the most important cellulase inhibitors in pretreated sugarcane bagasse hydrolysates. Moreover, the synergistic effect of Lfa2 on Bacillus subtilis GH5-CBM3 endoglucanase activity was demonstrated by the increased production of glucose (1.6-fold). Together, these results indicate that β-glucosidase Lfa2 is a promissory enzyme candidate for utilization in diverse industrial applications, such as cellulosic biomass degradation or flavor enhancement in winemaking and grape processing.
Resilience and the Coevolution of Interdependent Multiplex Networks

We propose a new model for the study of resilience of coevolving multiplex scale-free networks. Our network model, called preferential interdependent networks, is a novel continuum over scale-free networks parameterized by their correlation p, 0 ≤ $p$ ≤1. Our failure and recovery model ties the propensity of a node, both to fail and to assist in recovery, to its importance. We show, analytically, that our network model can achieve any γ, 2 ≤ γ ≤ 3 for the exponent of the power law of the degree distribution; this is superior to existing multiplex models and allows us better fidelity in representing real-world networks. Our failure and recovery model is also a departure from the much studied cascading error model based on the giant component; it allows for surviving important nodes to send assistance to the damaged nodes to enable their recovery. This better reflects the reality of recovery in man-made networks such as social networks and infrastructure networks. Our main finding, based on simulations, is that resilient preferential interdependent networks are those in which the layers are neither completely correlated (p = 1) nor completely uncorrelated (p= 0) but instead semi-correlated (p ≈ 0.1 – 0.3). This finding is consistent with the real-world experience where complex man-made networks typically bounce back quickly from stress. In an attempt to explain our intriguing empirical discovery we present an argument for why semi-correlated multiplex networks can be the most resilient. Our argument can be seen as an explanation of plausibility or as an incomplete mathematical proof subject to certain technical conjectures that we make explicit.
Efficient and Scalable Integrity Verification of Data and Query Results for Graph Databases

Graphs are used for representing and understanding objects and their relationships for numerous applications such as social networks, semantic webs, and biological networks. Integrity assurance of data and query results for graph databases is an essential security requirement. In this paper, we propose two efficient integrity verification schemes – HMACs for graphs (gHMAC) for two-party data sharing, and redactable HMACs for graphs (rgHMAC) for third-party data sharing, such as a cloud-based graph database service. The proposed schemes have linear complexity in terms of the number of vertices and edges in the graphs, which is shown to be optimal. Our experimental results corroborate that the proposed HMAC-based schemes for graphs are highly efficient as compared to the digital signature-based schemes – computation of HMAC tags is about 10 times faster than the computation of digital signatures.
ZVS Interleaved Totem-pole Bridgeless PFC Converter with Phase-shifting Control

This paper proposes an interleaved totem-pole bridgeless boost power factor correction (PFC) converter employing an additional inductor which has small switching loss. The main problem of conventional interleaved totem-pole bridgeless boost PFC converter is large switching loss due to the hard switching operation. And that problem becomes more serious in high switching frequency operation. In proposed converter, by utilizing the energy of additional inductor, zero-voltage-switching (ZVS) operation of the switches is achieved. Also, by using phase-shifting control between two interleaved PFC units, the flowing current on additional inductor can be controlled and only proper magnitude of current is generated. As a result, the proposed converter has higher efficiency than conventional converter. The feasibility of the proposed converter is confirmed with 50Hz, 230Vrms input and 1.6kW (400V / 4A) output prototype.