Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Stress Concentration Factor for Estimating Maximum Mechanical Stress of Interior Permanent-Magnet Machines

This paper investigates the stress concentration factor (SCF) for the calculation of the maximum mechanical stress (MMS) on the rotor of interior permanent-magnet machines (IPMMs). The key factors that affect the stress concentration on the rotor iron-bridges are identified using finite element analysis (FEA). Based on the FEA results, an accurate SCF function is developed particularly for V-type and U-type notches on the IPMM rotors using the polynomial surface fitting techniques. Moreover, the equivalent ring method (ERM) is improved to calculate the normal reference stress of the bridge. Then, by combining the proposed SCF function with the improved ERM, a new analytical MMS model which is more general and accurate is obtained. In order to verify the proposed function, MMS calculations were carried out using the new ERM-SCF MMS model for different rotor configurations at various speeds, which the results are compared with the values obtained from FEA. The comparison shows that the proposed method has improved generality and accuracy as it always has a consistent trend with FEA for different rotor configurations, while the traditional ERM is only valid for some specific rotor geometries with strict constraints on choosing geometrical parameters.
Unknown Landscape Identification with CNN Transfer Learning

Unknown landscape identification is the problem of identifying an unknown landscape from a set of already provided landscape images that are considered to be known. The aim of this work is to extract the intrinsic semantic of landscape images in order to automatically generalize concepts like a stadium, roads, a parking lot etc., and use this concept to identify unknown landscapes. This problem can be easily extended to many security applications. We propose two effective semi-supervised novelty detection approaches for the unknown landscape identification problem using Convolutional Neural Network (CNN) Transfer Learning. This is based on the use of pre-trained CNNs (i.e. already trained on large datasets) already containing general image knowledge that we transfer to our domain. Our best values of AUROC and Average Precision scores for the identification problem are 0.96 and 0.94, respectively. In addition, we statistically prove that our semi-supervised methods outperform the baseline.
Breakdown of 15 kV Insulators Under Bipolar Oscillating Impulse Voltage

Lightning overvoltages depend on several parameters and may deviate significantly from the standard lightning impulse waveshape. Voltages induced on overhead power distribution lines by nearby strokes may often exhibit bipolar characteristic. This paper investigates the behavior of a typical 15 kV pin-type porcelain insulator subjected to a bipolar impulse, representative of a lightning-induced voltage. It is shown that a simple modification on the method proposed by Savadamuthu et al. to estimate the insulation strength of small (a few mm) insulation systems subjected to bipolar oscillating voltages leads to promising results in terms of predicting the occurrence of flashovers on the 15 kV insulator.
Syntax-based transfer learning for the task of biomedical relation extraction

Transfer learning aims at enhancing machine learning performance on a problem by reusing labeled data originally designed for a related, but distinct problem. In particular, domain adaptation consists for a specific task, in reusing training data developedfor the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because they usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains. In this paper, we experiment with transfer learning for the task of relation extraction from biomedical texts, using the TreeLSTM model. We empirically show the impact of TreeLSTM alone and with domain adaptation by obtaining better performances than the state of the art on two biomedical relation extraction tasks and equal performances for two others, for which little annotated data are available. Furthermore, we propose an analysis of the role that syntactic features may play in transfer learning for relation extraction. Given the difficulty to manually annotate corpora in the biomedical domain, the proposed transfer learning method offers a promising alternative to achieve good relation extraction performances for domains associated with scarce resources. Also, our analysis illustrates the importance that syntax plays in transfer learning, underlying the importance in this domain to privilege approaches that embed syntactic features.
When data permutations are pathological: the case of neural natural language inference

Consider two competitive machine learning models, one of which was considered state-of-the art, and the other a competitive baseline. Suppose that by just permuting the examples of the training set, say by reversing the original order, by shuffling, or by mini-batching, you could report substantially better/worst performance for the system of your choice, by multiple percentage points. In this paper, we illustrate this scenario for a trending NLP task: Natural Language Inference (NLI). We show that for the two central NLI corpora today, the learning process of neural systems is far too sensitive to permutations of the data. In doing so we reopen the question of how to judge a good neural architecture for NLI, given the available dataset and perhaps, further, the soundness of the NLI task itself in its current state.
Native Language Identification with User Generated Content

We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English). Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. We provide a detailed analysis of the features that sheds light on differences between native and nonnative speakers, and among nonnative speakers with different backgrounds.
Lane change maneuver for autonomous vehicles (Benchmark Proposal)

Lane changes are known to be risky maneuvers both for autonomous vehicles and human drivers since they require changes in longitudinal and lateral velocities in the presence of other moving vehicles. In this paper, we propose a benchmark modeling a cooperative lane change maneuver that involves four fully autonomous vehicles; three in the left lane and one in the right. The vehicle driving in the right lane aims to move to the left lane while avoiding a collision with the other vehicles. Each vehicle is equipped with sensors and can also communicate with its neighboring vehicles. The vehicle dynamics are described by a dynamic bicycle model and each vehicle is equipped with a linear low-level controller that regulates its own longitudinal and lateral behavior. To guarantee that the maneuver is safe and the traffic rules are enforced, we employ a cooperative driving control scheme (in the spirit of supervisory logic) that decides the actions of each vehicle.
Effect of Additional Copper Losses on the High-speed Induction Machine Performance

Accurate loss calculation is an important part of the machine design, which can have significant influence on the predicted operating temperature, especially in the high-speed applications. In this paper, the calculation method of additional copper losses is demonstrated with an example of high-speed induction machine. The additional copper losses are studied using finite element analysis for twisted and non-twisted stator windings. In the proposed study, only one coil is modeled with strands to optimize computational resources. Influence of an accurate determination of the end winding leakage inductance on additional loss is presented. Proposed method of the analysis can be implemented for loss estimation of various high-speed electrical machines.
Encoding Gated Translation Memory into Neural Machine Translation

Translation memories (TM) facilitate human translators to reuse existing repetitive translation fragments. In this paper, we propose a novel method to combine the strengths of both TM and neural machine translation (NMT) for high-quality translation. We treat the target translation of a TM match as an additional reference input and encode it into NMT with an extra encoder. A gating mechanism is further used to balance the impact of the TM match on the NMT decoder. Experiment results on the UN corpus demonstrate that when fuzzy matches are higher than 50%, the quality of NMT translation can be significantly improved by over 10 BLEU points.
A 112GB/S PAM4 Wireline Receiver Using a 64-Way Time-Interleaved SAR ADC in 16NM FinFET

A 112Gb/s PAM4 wireline receiver testchip is implemented in 16nm FinFET. The receiver consists of continuous-time linear equalizers, a peaking capacitance buffer, and a 56GSa/s 64-way time-interleaved SAR ADC. The receiver achieves 2e-5 BER over a 20dB loss channel at 28GHz Nyquist while consuming 590mW power, excluding DSP.
A 2-Layered Graph Based Diffusion Approach for Altmetric Analysis

The research shared on a digital social media has enabled us to measure the impact of academic entities beyond the conventional bibliometric community. We explored a diffusion-based metrics to measure the influence of academic entities in social media using 2-layered graph where the first layer is the graph between academic and social media entities and a second layer is the graph between social media entities. We employed the heat diffusion algorithms to measure the social impact of academic entities and evaluate them by (i) predicting links between academic entities and social media and (ii) suggesting memes for the academic entities. Our analysis on predicting links between scientist and social media entities showed the AUC-ROC score of 0.73 and the AUC-PR score of 0.30. Similarly, predicting links between scientific publications and social media entities showed the AUC-ROC score of 0.80 and the AUC-PR score of 0.19. Our approach also provides decent social media entities (memes) suggestion for scientific publications.
Current Controller Design in Discrete-Time Domain for Pmsm/G in Flywheel Energy Storage Systems

Wide speed range operation of the Permanent-Magnet Synchronous Motor/Generator (PMSM/G) in a Flywheel Energy Storage System (FESS) leads to lower sampling-to-fundamental frequency ratio than other PMSM applications. Oscillatory or unstable response can occur if the current controller design does not properly incorporate the discrete nature of PWM modulation method. A current controller directly designed in discrete-time domain is presented in this paper based on an accurate discrete model expressed with complex-vector, and some modifications and approximations are made to extend its application to salient machines. The digital delays are taken into consideration in the accurate discrete model and the coupling problem between d-q axis current loops is well solved. At last, the designed controller is validated by simulations and experiments on a PMSM/G test bench.
High-voltage Bi-directional Half-bridge Three-level Series Resonant Converter with Frequency Modulation Control

This paper aims to develop a bi-directional half-bridge three-level series resonant converter for high-voltage DC micro-grid. The topology of three-level circuit can reduce the device voltage stresses and electromagnetic interference. The clamped diode and capacitor unit can provide an appropriate current path to achieve zero-voltage switching on power switches. By conventional frequency modulation control, the resonant tank can be operated in SRC region and achieve bi-directional power conversion. Since the proposed control method is developed in this paper, the circuit has a characteristic of transformer decoupling called LLC-SRC region. It achieves quasi-zero-current switching to reduce the switching loss. Synchronous rectifier is also implemented to reduce conduction loss on the secondary side. A laboratory prototype of bi-directional half-bridge three-level series resonant converter is designed and verified in high-voltage applications. The circuit specifications are 3-kW rated power, 1-kV input voltage, and 3 A output current. A digital signal processor (DSP) chip is used to realize the digital controller of this converter. The measured efficiency can be achieved 96% under different load conditions.
Large-Signal Metal-Insulator-Graphene Diode Model on a Flexible Substrate for Microwave Application

In this paper, a metal-insulator-graphene (MIG) diode is designed, fabricated, and modelled. The MIG diode is realized on a 50 μm- Kapton substrate based on graphene material grown by chemical vapor deposition (CVD) and TiO2 as a barrier material. The flexible MIG diode has several superior properties compared to a diode using flexible Si, carbon-nanotube (CNT) or ZnO especially for microwave application. Therefore, it is critical to establish a model of the MIG diode for circuit design. A SPICE-compatible model of the MIG diode is demonstrated for the first time to the best of our knowledge. The Lambert Wfunction is used to extract the intrinsic parameters of the diode. The equivalent circuit including extrinsic elements is verified up to 20 GHz. The main contribution of this paper is to show that the SPICE model is able to predict the behaviour of MIG diodes.
Discussion on the Control of Wound-Rotor Synchronous Motors for Variable Speed Drives

For wound-rotor synchronous machines, one has four degrees of freedom to set a particular torque-speed operation point: Excitation current, phase current, phase current frequency and the current angle. It is desirable to set these parameters according to a particular strategy (e.g. Maximum Torque per Ampere). When the motor design is changed, the optimal combination of the four parameters at an operation point might change as well. To ensure a fair comparison of variants in the design process, it is desirable to assess them using their respective adapted control. This paper presents a workflow to calculate the best control parameters numerically and compute results for performance assessment. The calculation of the torque-speed envelope curve is used as exemplary means of performance evaluation. The suggested procedure is based on Finite-Element models without, however, becoming prohibitively computational expensive. Consideration of eddy-currents and core saturation is possible. Finally, torque-optimal control of wound-rotor synchronous machines is discussed in more detail.
Characterizing Interactions and Relationships between People

This paper presents a set of dimensions to characterize the association between two people. We distinguish between interactions (when somebody refers to somebody in a conversation) and relationships (a sequence of interactions). We work with dialogue scripts from the TV show Friends, and do not impose any restrictions on the interactions and relationships. We introduce and analyze a new corpus, and present experimental results showing that the task can be automated.
Return Map Approach for Simulations of Electronic Circuits with Memristors

In this work, we discuss numerical methods for simulations of circuits containing memristors. In several models, the internal state of a memristor is limited by physical dimensions of the device. This introduces discontinuities in right hand sides of differential equations describing dynamics of circuits with memristors and makes it difficult to integrate such systems using standard numerical integration methods. An approach based on the concept of a return map is proposed to solve this problem. A simple example is discussed to show the usefulness of the proposed technique.
Learned optimizers that outperform SGD on wall-clock and test loss

Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned optimizers may similarly outperform current hand-designed optimizers, especially for specific problems. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process. The resulting gradients are either strongly biased (for short truncations) or have exploding norm (for long truncations). In this work we propose a training scheme which overcomes both of these difficulties, by dynamically weighting two unbiased gradient estimators for a variational loss on optimizer performance. This allows us to train neural networks to perform optimization of a specific task faster than well tuned first-order methods. Moreover, by training the optimizer against validation loss (as opposed to training loss), we are able to learn optimizers that train networks to better generalization than first order methods. We demonstrate these results on problems where our learned optimizer trains convolutional networks in a fifth of the wall-clock time compared to tuned first-order methods, and with an improvement in test loss.
Electronic Architecture for a Mobile Manipulator

A mobile manipulator is a robotic system consisting of a mobile platform on which a manipulator arm is mounted, allowing the robotic system to perform locomotion and manipulation tasks simultaneously. A mobile manipulator has several advantages over a robot manipulator which is fixed, the main advantage is a larger workspace. The robots manipulators are oriented to work collaboratively with the human being in tasks that simultaneously require mobility and ability to interact with the environment through the manipulation of objects. This article will present the electronic design for a mobile robot manipulator with five degrees of freedom and a 6-wheel traction with four of these directional.
Cross-lingual Decompositional Semantic Parsing

We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score.
SiC: Technology Enabler for MV DC/DC Galvanically Insulated Modular Converters

In this paper, the use of 3.3 kV SiC MOSFETs, Fast Si IGBTs and the mix of both in the so called Cross-Hybrid Switch (XS) and Bimode Cross-Hybrid Switch (BXS) are considered for their application on Galvanically Insulated Modular Converters (GIMC) cells. The semiconductors typically operate under Soft Switching conditions to allow high frequency operation resulting in size and weight minimization while keeping a high efficiency. The high frequency (10 kHz) and Soft Switching operation in the selected semiconductors have been first evaluated by applying 2D semiconductor device simulations. The simulation results allow to identify the impact of device parameters such as the Si IGBTs Lifetime adjustment and the Si IGBT – SiC MOSFET chip surface ratio on the BXS devices, over the on-state and switching losses. Accordingly, different 3.3kV dual HiPak testing samples have been implemented and experimentally evaluated on a 200 kW DC/DC GIMC cell test bench. The comparison between the 3.3 kV Fast Si IGBTs, SiC MOSFETs and BXS shows clear functional benefits of using MV SiC MOSFETs in resonant mode operation, allowing an increase of the GIMC cell power capability by 30 - 35 % for the same module current rating compared to the use of Fast IGBTs. In a similar way, the BXS devices can provide and intermediate power capability increase of around 20-25%, minimizing the installed Si and SiC chip area that can be used as an enabler for improvement of the GIMC technology impacting the cost and performance of the semiconductor devices applied on the GIMC cells
Manual Dexterity and Aging: A Pilot Study Disentangling Sensorimotor From Cognitive Decline

Manual dexterity measures can be useful for early detection of age-related functional decline and for prediction of cognitive decline. However, what aspects of sensorimotor function to assess remains unclear. Manual dexterity markers should be able to separate impairments related to cognitive decline from those related to healthy aging. In this pilot study, we aimed to compare manual dexterity components in patients diagnosed with cognitive decline (mean age: 84 years, N = 11) and in age comparable cognitively intact elderly subjects (mean age: 78 years, N = 11). In order to separate impairments due to healthy aging from deficits due to cognitive decline we also included two groups of healthy young adults (mean age: 26 years, N = 10) and middle-aged adults (mean age: 41 years, N = 8). A comprehensive quantitative evaluation of manual dexterity was performed using three tasks: (i) visuomotor force tracking, (ii) isochronous single finger tapping with auditory cues, and (iii) visuomotor multi-finger tapping. Results showed a highly significant increase in force tracking error with increasing age. Subjects with cognitive decline had increased finger tapping variability and reduced ability to select the correct tapping fingers in the multi-finger tapping task compared to cognitively intact elderly subjects. Cognitively intact elderly subjects and those with cognitive decline had prolonged force release and reduced independence of finger movements compared to young adults and middle-aged adults. The findings suggest two different patterns of impaired manual dexterity: one related to cognitive decline and another related to healthy aging. Manual dexterity tasks requiring updating of performance, in accordance with (temporal or spatial) task rules maintained in short-term memory, are particularly affected in cognitive decline. Conversely, tasks requiring online matching of motor output to sensory cues were affected by age, not by cognitive status. Remarkably, no motor impairments were detected in patients with cognitive decline using clinical scales of hand function. The findings may have consequences for the development of manual dexterity markers of cognitive decline.
Detection and Delineation of Events and Sub-Events in Social Networks

We define and solve the problem of event detection and delineation as a task of identifying events and decomposing them to their major sub-events, with a description and a timeline. We propose DeLi, an algorithm that focuses on providing such an understanding of events and sub-events. DeLi, to the best of our knowledge, is the first method that addresses the problem in a generic stream of text, and in an online fashion. Extensive evaluation on social streaming data demonstrates that, by combining the structure of a social network with content attributes, our method outperforms the state-of-the-art techniques.