Miracle-dz/UniFAR-FTUs huggingface dataset:
Students’ t-distribution modelling of LLS location errors

This paper investigates the statistical distribution that best describes Lightning Location System location errors. It is generally assumed that the bivariate Gaussian distribution is a good description but the presence of outliers calls for a heavier-tailed distribution. The bivariate Students’ t-distribution is proposed. Using Maximum Likelihood Estimation, Mahalanobis quality-of-fit tests and ground-truth events to the Brixton tower, South Africa and Gaisberg tower, Austria, we are able to show that the bivariate Students’ t-distribution is a better fit than the bivariate Gaussian distribution.
Performance Comparison of Stator Slot Permanent Magnet Machines with Non-Overlapping and Overlapping Windings

In this paper, an overlapping winding (OW) with armature coil pitch of 3 (A3) are applied to 6-slot stator slot permanent magnet machines (SSPM) in order to enhance the torque density and increase the efficiency. The OW layouts are applied to 6-slot SSPMs with different rotor pole numbers. Comparing with the existing SSPMs with non-overlapping winding (NOW), the SSPMs with OW has doubled its fundamental winding factor under the same stator/rotor pole number combination. Thus, the back-EMF and average torque of SSPMs with OW is effectively enhanced. The 6-slot/8-rotor pole (6s8r) SSPMs with A1 and A3 are selected from the candidates with high average torque and their electromagnetic performance is comparatively analyzed with 6s7r-A1 SSPM. It shows that the average torque in 6s8r-A1 SSPM is improved by 114.71% by employing OW at the same copper loss. Although the OW cannot be applied to 6-slot SSPMs with odd rotor poles, the 6s7r-A1 SSPM shows low total harmonic distortion (THD) in back-EMF and significant low torque ripple at on-load condition.
A Pragmatic SOH and SOC Co-Estimator for Lithium-ion Batteries in Smart Grid Applications

Reliable online co-estimation of state of health (SOH) and state of charge (SOC) of Li-ion batteries were of paramount importance for the realistic battery management system (BMS). This work aimed at bridging laboratory test and real-life battery operation data with a comprehensive analysis to provide a coherent and non-invasive approach based on probability density function (PDF), for building a pragmatic model to co-evaluate SOH and SOC of Li-ion batteries for smart grid applications. PDF results based on practical applications revealed that there was a prominent regularity of the voltage probabilities with regards to the SOH, which were exploited for setting up an online SOH evaluation scale gauge. Utilization of the load current of realistic smart grid further improve the practical generality of the proposed algorithm. The battery online SOC was determined afterwards based on the online OCV variation and the extracted SOH values. Commercial Li-ion batteries at arbitrary SOH and SOC level were tested to validate the effectiveness and robustness of the proposed algorithm, and the test results showed high accuracy and reliability of the proposed algorithm for co-evaluating SOH and SOC.
Lightning Damages in Glass Fiber-Epoxy Composite Material used for Aerospace Applications

Arcing effects, such as that due to lightning, on glass fiber reinforced epoxy composite laminate used in aerospace applications, have been studied. Lightning voltage impulses (1.2/50 micro seconds) were applied at breakdown voltage on the specimens and the arc points were inspected nondestructively using an Ultrasonic Propagation Imaging (UPI) system. The data acquired were processed using the Statistically Thresholded Anomaly Mapping (STAM) method. The outcomes depict that the size of lightning penetration damage is proportional to the breakdown voltage and significantly larger than the charring mark at the point of breakdown. Specimens subjected to surface flash-over reconfirms that whereas the arc leaves little or no visual sign on the specimen surface, there may be significant degradation of the material at microscopic scale. Inspection of 3-layer laminates reveals that the arc damage is characterized by larger charred region, exposed glass fibers due to vaporization of epoxy matrix, and delamination. The delamination could happen for the first ply, the third ply, or both at the same time. The size of the delaminations estimated from STAM image was smaller than those estimated using backlight illumination.
Series/Parallel Switching Circuits Using Power MOSFETs for Photovoltaic Modules

This work discusses the feasibility of the series/parallel switching circuits using power MOSFETs in order to minimize the output power reduction of the photovoltaic power system due to the partial shading by connecting the shaded panels to the adjacent panel in parallel. From the experimental results: 1) When one of the photovoltaic panels is shaded at least, 50% of the reduction ratio of the solar radiation intensity becomes the lower limit of the threshold values for the series/parallel switching control. 2) The effect of the series/parallel switching control can be more expected when more than two of the photovoltaic panels are shaded. However, the sum of the solar radiation intensity applied to the panels in the parallel circuit should be less than 150% of the maximum intensity of the photovoltaic power system. 3) The shaded panel which should be connected to the adjacent panel in parallel can be detected by the bypass current measurements. The authors developed a switching circuit module prototype and carried out the operation tests using three photovoltaic panels. From the results, the switching circuit module successfully detects the partial shading conditions and switches from the series operation mode to the parallel operation mode.
Efficient static checking of library updates

Software engineering practices have evolved to the point where a developer writing a new application today doesn’t start from scratch, but reuses a number of open source libraries and components. These third-party libraries evolve independently of the applications in which they are used, and may not maintain stable interfaces as bugs and vulnerabilities in them are fixed. This in turn causes API incompatibilities in downstream applications which must be manually resolved. Oversight here may manifest in many ways, from test failures to crashes at runtime. To address this problem, we present a static analysis for automatically and efficiently checking if a library upgrade introduces an API incompatibility. Our analysis does not rely on reported version information from library developers, and instead computes the actual differences between methods in libraries across different versions. The analysis is scalable, enabling real-time diff queries involving arbitrary pairs of library versions. It supports a vulnerability remediation product which suggests library upgrades automatically and is lightweight enough to be part of a continuous integration/delivery (CI/CD) pipeline. To evaluate the effectiveness of our approach, we determine semantic versioning adherence of a corpus of open source libraries taken from Maven Central, PyPI, and RubyGems. We find that on average, 26% of library versions are in violation of semantic versioning. We also analyze a collection of popular open source projects from GitHub to determine if we can automatically update libraries in them without causing API incompatibilities. Our results indicate that we can suggest upgrades automatically for 10% of the libraries.
Fast Grayscale-Thermal Foreground Detection With Collaborative Low-Rank Decomposition

This paper investigates how to perform efficient and robust foreground detection in challenging scenarios by leveraging multiple source data. We propose a novel approach, called collaborative low-rank decomposition (CLoD), for grayscale-thermal foreground detection. Given two data matrices by accumulating sequential frames from the grayscale and the thermal videos, CLoD detects the foreground objects as sparse noises against the backgrounds with collaborative low rank structure, and also incorporates modality weights to achieve adaptive fusion of different source data. For the optimization, CLoD seeks a sub-optimal solution by making the background matrix rank explicitly determined. In particular, the background matrix with the fixed rank can be decomposed into two sub-matrices of low rank, and then, we iteratively optimize them and the modality weights with closed-form solutions. For improving the efficiency, we design a block-based accelerated algorithm to speed up CLoD while employing the edge-preserving algorithm to keep the accuracy. Extensive experiments on the recently public benchmark grayscale-thermal foreground detection suggest that our approach achieves comparable performance in terms of both accuracy and efficiency against other state-of-the-art methods.
Joint Learning for Emotion Classification and Emotion Cause Detection

We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.
${L}_{{g}} = {87}$  nm InAlAs/InGaAs High-Electron- Mobility Transistors With a g m_max of 3 S/mm and  $f_{{T}}$   of 559 GHz

We present our latest results on <inline-formula> <tex-math notation="LaTeX">${L} _{g} = {87}$ </tex-math> </inline-formula> nm InAlAs/InGaAs high-electron-mobility transistors with an excellent combination of dc and high-frequency characteristics. We obtained a maximum transconductance of 3 S/mm at <inline-formula> <tex-math notation="LaTeX">${V}_{\text{DS}} = {0.9}$ </tex-math> </inline-formula> V, an ON-resistance of <inline-formula> <tex-math notation="LaTeX">$273~\Omega $ </tex-math> </inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math> </inline-formula>, a current gain cutoff frequency of 559 GHz, and a maximum oscillation frequency of 671 GHz. According to small-signal modeling, these outstanding device metrics are a consequence of the indium-rich InGaAs channel with very high Hall mobility, compact device design with <inline-formula> <tex-math notation="LaTeX">${L} _ {\text{SD}} = {1}\,\,\mu \text{m}$ </tex-math> </inline-formula>, and an optimized gate process that yields well-tempered behavior.
Fusion of Resampled 3D MR Images for Geometric Modeling of Blood Vessels

Accurate quantitative information about blood vasculature depicted in 3D MR images has a great importance in diagnosis of many vascular diseases. In this work, the accuracy of vessel lumen walls reconstruction is studied. A 3D printed model of two MRI time-of-flight arterial branches is manufactured. Next, it is submerged in water and T2-weighted MR images are acquired in coronal, transversal and sagittal thick slice orientation. Then, MR anisotropic and fused (resampled and averaged) images are segmented with the use of Level-Set (LS) and centerline-radius (CR) algorithms. Surfaces of 3D branch models are constructed, and errors of their estimated radii are evaluated. We have demonstrated that LS approach may not be capable of segmenting thin vascular branches in anisotropic-voxel images. After image resampling, the LS segmentation restores the object surface, but with significant staircase distortion. Thanks to the knowledge about the assumed vessel shape, incorporated with the CR method, it produces accurate estimates of the branch radius and smooth model surface, even in the case of anisotropic voxel images.
A Consensus-Based Distributed MPC Approach for Batteries Sharing in Group of Buildings

This paper addresses the problem of sharing a private Electrical energy Storage System (ESS) for thermoelectrical energy management in a group of buildings. Each building is equipped with an ESS which can be used partly or completely by other buildings in the same group. This sharing strategy creates coupling constraints among the controllers of each individual building. In this context, an increment proximal minimization method has been employed to manage the energy flows among the various ESS's through a distributed approach. The local controller of each building employs a Model Predictive Control (MPC) which focuses on balancing economic optimization and occupant comfort while fulfilling various local technical constraints. The proposed technique allows local controllers to operate with maximum autonomy and privacy since, at each iteration, a little amount of information is exchanged between each local controller to a centralized coordination unit. Indeed, only the information related to the coupling constraints is required to exchange with the centralized unit. The most significant advantage of the method is to allow the individual building to exploit effectively not only its own ESS but also any excess power and energy capacity of the ESSs of other buildings. The simulation results show the accuracy and efficiency of the proposed method.
A Control Strategy for Flying-Start of Shaft Sensorless Permanent Magnet Synchronous Machine Drive

Starting a spinning shaft sensorless controlled permanent magnet synchronous machine drive is difficult due to the lack of rotor position and speed information. This information is needed for the current and speed controllers when they are activated initially. Particularly, current controller requires back-EMF to calculate the decoupling voltage to prevent drive overcurrent. In this paper, a restarting strategy for back-EMF based shaft sensorless drive is proposed. The existing position and speed estimator for shaft sensorless control is used and no additional algorithm is required. The current controller is set to a particular condition so the back-EMF can be estimated and decoupled. With the proposed strategy the regeneration current is suppressed, and the motor can start from various spinning conditions with either positive or negative speed.
Optimization of a Spoke-Type Permanent Magnet Motor by Combination of Genetic Algorithm and Finite Element Method

The spoke-type permanent magnet motors with ferrite magnets provide relatively high torque density and high efficiency at a low cost compared to conventional structures and are widely applied in different drive systems. In this paper, a parametric finite element model of a spoke-type permanent magnet motor for washing machines is built up. Operating points at low speed and high speed are calculated respectively. An optimization process by using genetic method is then conducted, with the goal to find an optimal design that satisfies a list of constraints and has lowest cost. A fitness function is specially defined to deal with the object together with the constraints. The results show the feasibility of the proposed approach combining genetic method with finite element method.
Efficient Computation of G-Skyline Groups (Extended Abstract)

The skyline of a data point set consists of the best points in the set, and is very important for multi-criteria decision making. One recent and important variant of the traditional skyline is group-based skyline, which aims to find the best groups of points in a given set. This paper brings forward an efficient approach, called minimum dominance search (MDS), to solve the g-skyline problem, a latest group-based skyline problem. MDS consists of two steps: In the first step, a novel g-skyline support structure, i.e., minimum dominance graph (MDG), is constructed to store all the points which may occur in g-skyline groups. In the second step, two searching algorithms are proposed to find g-skyline groups based on the MDG through two searching algorithms, and a skyline-combination based optimization strategy is employed to improve these two algorithms. The support for dynamic group sizes, i.e., a practical extension of the origin g-skyline problem, is provided through slightly modifying MDS. Comprehensive experiments are conducted on both synthetic and real-world data sets, and the results show that our algorithms are orders of magnitude faster than the state-of-the-art.
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach

Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that human perform post-editing using discrete edit operations, wich we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.
Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

A deterministic privacy metric using non-stochastic information theory is developed. Particularly, maximin information is used to construct a measure of information leakage, which is inversely proportional to the measure of privacy. Anyone can submit a query to a trusted agent with access to a non-stochastic uncertain private dataset. Optimal deterministic privacy-preserving policies for responding to the submitted query are computed by maximizing the measure of privacy subject to a constraint on the worst-case quality of the response (i.e., the worst-case difference between the response by the agent and the output of the query computed on the private dataset). The optimal privacy-preserving policy is proved to be a piecewise constant function in the form of a quantization operator applied on the output of the submitted query. The measure of privacy is also used to analyze $k$ -anonymity (a popular deterministic mechanism for privacy-preserving release of datasets using suppression and generalization techniques), proving that it is in fact not privacy preserving.
sFog: Seamless Fog Computing Environment for Mobile IoT Applications

Fog computing is a promising solution to provide low-latency and ubiquitously available computation offloading services to widely distributed Internet of Things (IoT) devices with limited computing capabilities. One obstacle, however, is how to seamlessly hand over mobile IoT devices among different fog nodes to avoid service interruption. In this paper, we propose seamless fog (sFog), a new framework supporting efficient congestion control and seamless handover schemes. Intrinsically, sFog improves system performance during handovers (achieved by the handover scheme), and guarantees the performance does not degrade when handovers do not occur (achieved by the congestion control scheme). Through the congestion control scheme, jobs are efficiently offloaded without causing unnecessary system idling; through the handover scheme, jobs are pre-migrated to the target fog node when a handover is about to occur, in order to reduce migration delay. In order to evaluate the performance of sFog, we propose a theoretical framework and establish a real-world prototype. Both the theoretical and experimental results show that sFog achieves substantial delay reductions compared with traditional benchmark handover schemes.
On Message Exchange Motifs Emerging During Human/Bot Interactions in Multilayer Networks: The Case of Two Riot Events

In this paper, we analyze the message exchange patterns that emerge when social bots and human users communicate via Twitter. In particular, we use a multilayer network to analyze the emergence of the corresponding representative and statistically significant sub-graphs (so called motifs). Our analysis is based on two recent riot events, namely the Philadelphia Super-bowl 2018 riots and the 2017 G20 riots in Hamburg (Germany). We found that in these two events message exchanges between humans form characteristic and re-occurring communication patterns. In contrast, message exchanges including bots occur rather sporadically and do not follow a particular statistically significant pattern.
Complementing global and local contexts in representing API descriptions to improve API retrieval tasks

When being trained on API documentation and tutorials, Word2vec produces vector representations to estimate the relevance between texts and API elements. However, existing Word2vec-based approaches to measure document similarities aggregate Word2vec vectors of individual words or APIs to build the representation of a document as if the words are independent. Thus, the semantics of API descriptions or code fragments are not well represented. In this work, we introduce D2Vec, a new model that fits with API documentation better than Word2vec. D2Vec is a neural network model that considers two complementary contexts to better capture the semantics of API documentation. We first connect the global context of the current API topic under description to all the text phrases within the description of that API. Second, the local orders of words and API elements in the text phrases are maintained in computing the vector representations for the APIs. We conducted an experiment to verify two intrinsic properties of D2Vec's vectors: 1) similar words and relevant API elements are projected into nearby locations; and 2) some vector operations carry semantics. We demonstrate the usefulness and good performance of D2Vec in three applications: API code search (text-to-code retrieval), API tutorial fragment search (code-to-text retrieval), and mining API mappings between software libraries (code-to-code retrieval). Finally, we provide actionable insights and implications for researchers in using our model in other applications with other types of documents.
Design and Analysis for Torque Ripple Reduction in Synchronous Reluctance Machine.

The rotor of transversally laminated synchronous reluctance motor is constructed with multi-flux barriers by stamping the laminated steel sheets and the machine is short for TLR-SynRM. It has obvious advantages of smooth rotor surface, and the lower copper loss, et al. [1]. Nevertheless, TLR-SynRM has the drawback of high torque ripple, and one of the main reasons is the non-sinusoidal distribution of the air-gap flux density caused by the spatial harmonics of stator magnetic potential and flux-barrier rotor configuration. In [2], it has been carried out the genetic algorithm (GA) optimizations to improve the angles of the flux-barrier ends to reduce the torque ripple. In this paper, it proposes the step-shaped gradient flux barriers in TLR-SynRM for torque ripple reduction.A method is presented to compute the reluctances of each layer of flux barriers through conformal mapping method. Based on this calculation, it is shown how the air-gap flux density distribution of TLR-SynRM can be analytically computed through magnetic equivalent circuit (MEC) method. Combining with the two methods, the flux-barrier parameters for torque ripple reduction can be improved and the final improved flux-barrier parameters are obtained.