Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial Discharges Diagnostics Along Medium Voltage Cables

In the last years different partial discharge (PD) measuring techniques have been developed because PD diagnostic is the most widely tool to evaluate the insulation condition of a power cable. Recently non-conventional methods and sensors have been used in order to reach improved results in PD measurements. New sensors based on wireless technology were developed in order to simplify PD measurement especially for on-line, on-site diagnosis. The purpose of this work is to perform measurements that allow to study the variation of pulses when they travel along a medium voltage cable and characterize a new smart PD source detector and locator, named Wings sensor. For these purposes, internal PD signals have been acquired and compared with those detected by the dassical PD acquisition system. In addition, the characterization of the Wings sensor has been completed by acquiring PD pulses when the sensor is placed at different rotation angle and distance from the cable. It was found that for a better voltage magnitude signal the sensor should be placed in parallel with the cable and in contact with it.
A High-Efficiency Single-Phase T-Type BCM Microinverter

This paper proposes a high-efficiency single-phase T-type boundary conduction mode (BCM) microinverter. The conventional full-bridge BCM microinverter has achieved zero voltage switching (ZVS) and thereby improved the efficiency, but it suffers from high switching losses under light load conditions. The proposed T-type BCM microinverter reserves ZVS and uses a multilevel technique to further decrease the switching losses. The BCM operation with multilevel technique will have too low switching frequency when the grid voltage approaches half of the dc link voltage. To solve this problem, this paper adopts a third operation mode for the T-type switching leg to maintain the switching frequency above a minimum value. The corresponding mode transitions are also detailed to ensure a smooth operation. Because of the turn-off delay of the freewheeling transistor, the actual lower current boundary deviates from the programmed one, which will distort output current. To address this issue, this paper also proposes a boundary compensation method. A prototype has been built for performance verification, which can test both full-bridge and T-type topology. Compared with the full-bridge BCM microinverter, the proposed T-type BCM microinverter has a higher efficiency over the whole load range.
Playful Cognitive Training with Physical Interactive Tiles for Elderly

We propose novel cognitive training which focuses on certain specific cognitive abilities. The training is formed by playing games with physical interactive tiles (the MOTO tiles). A randomized controlled trial was conducted so as to examine the effect of the designed cognitive training program. 19 older adults (some with mild dementia) completed full trial and were analyzed. The results show that our short training (10 sessions of 30 minutes) improved reaction time of Visual Search task, which indicates beneficial effects on attention, scanning and visualization abilities
Efficacy of Hybrid Energy Beamforming With Phase Shifter Impairments and Channel Estimation Errors

Hybrid energy beamforming (HEB) can reduce the hardware cost, energy consumption, and space constraints associated with massive antenna array transmitter (TX). With a single radio frequency chain having <inline-formula> <tex-math notation="LaTeX">$N$</tex-math></inline-formula> digitally controlled phase shifter pairs, one per antenna element, theoretically achieving the same performance as a fully digital beamforming architecture with <inline-formula> <tex-math notation="LaTeX">$N$</tex-math></inline-formula> RF chains, this letter investigates the practical efficacy of the HEB. First adopting the proposed analog phase shifter impairments model and exploiting the channel reciprocity along with the available statistical information, we present a novel approach to obtain an accurate minimum mean-square error estimate for the wireless channel between TX and energy receiver (RX). Then, tight analytical approximation for the global optimal time allocation between uplink channel estimation and downlink energy transfer operations is derived to maximize the mean net harvested energy at RX. Numerical results, validating the analysis and presenting key design insights, show that with an average improvement of <inline-formula><tex-math notation="LaTeX"> $\text{58}{\%}$</tex-math></inline-formula> over the benchmark scheme, the optimized HEB can help in practically realizing the fully digital array gains.
Object Classification of Remote Sensing Images Based on Partial Randomness Supervised Discrete Hashing

Recently, object classification of remote sensing images has attracted more and more research interests due to the development of satellite and aerial vehicle technologies. Hashing learning is an efficient method to handle the huge amount of the remote sensing data. In this paper, we proposed a novel hashing learning method named partial randomness supervised discrete hashing (PRSDH), which combines data-dependent methods and data-independent methods. It jointly learns a discrete binary codes generation and partial random constraint optimization model. By random projection, the computation complexity is reduced effectively. With the weight matrix derived from the training data, the semantic similarity between the data can be well preserved while generating the hashing codes. For the discrete constraint problem, this paper adopts the discrete cyclic coordinate descent (DCC) algorithm to optimize the codes bit by bit. The experimental results show that PRSDH outperforms other comparative methods and demonstrated that PRSDH has good adaptability to the characteristic of remote sensing object.
3DMAX-Net: A Multi-Scale Spatial Contextual Network for 3D Point Cloud Semantic Segmentation

Semantic segmentation of 3D scenes is a fundamental problem in 3D computer vision. In this paper, we propose a deep neural network for 3D semantic segmentation of raw point clouds. A multi-scale feature learning block is first introduced to obtain informative contextual features in 3D point clouds. A global and local feature aggregation block is then extended to improve the feature learning ability of the network. Based on these strategies, a powerful architecture named 3DMAX-Net is finally provided for semantic segmentation in raw 3D point clouds. Experiments have been conducted on the Stanford large-scale 3D Indoor Spaces Dataset using only geometry information. Experimental results have clearly shown the superiority of the proposed network.
Robust Optimal Energy and Reserve Management of Multiple-Microgrids via Cooperative Bidding

This work proposes a cooperative trading scheme for the robust optimal energy and reserve management in a multiple-microgrid (MMG) system comprising four microgrids (MGs). This scheme includes a robust optimization (RO) model which accounts for the uncertainties in the renewable energy source (RES) generation, the load demand and the energy market prices using a scenario based worst-case exploration technique. A coordinating entity in the MMG system called a power sharing operator manages the day-ahead energy and reserve trading between the MGs and also with the main grid, thereby enabling market clearing within the MMG system. The proposed scheme is based on the iterative sub-gradient method. The results showcase the convergence characteristics of the proposed scheme. Moreover, the tradeoff between the energy and reserve costs in each MG are also analysed. Finally, the economic benefits gained by the MMG system under the proposed RO based cooperative trading scheme are evaluated.
Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction

In this article, we propose an efficient method for solving analysis-l1-TV regularization problems with a multi-step alternating direction method of multipliers (ADMM) approach as the fast solver. Additionally, we apply it to a real-data magnetoen-cephalography (MEG) brain imaging problem as well as to signal reconstruction. In our approach, the inverse problem arising in MEG or signal reconstruction is formulated as an optimization problem which we regularize using a combination of analysis-l1 prior together with a total variation (TV) regularization term. We then formulate an optimization algorithm based on ADMM which can effectively be used to solve the optimization problems. The performance of the algorithm is illustrated in practical scenarios.
An Emerging Application Centric RFID Framework Based on New Web Technology

In the context of emerging applications such as IoT, an RFID framework that can dynamically incorporate, identify, and seamlessly regulate the RFID tags is considered exciting. Earlier RFID frameworks developed using the older web technologies were limited in their ability to provide complete information about the RFID tags and their respective locations. However, the new and emerging web technologies have transformed this scenario and now framework can be developed to include all the required flexibility and security for seamless applications such as monitoring of RFID tags. This paper revisits and proposes a generic scenario of an RFID framework built using latest web technology and demonstrates its ability to customize using an application for tracking of personal user objects. This has been shown that the framework based on newer web technologies can be indeed robust, uniform, unified, and integrated.
Nuclear Segmentation and its Quantification in H&E Stained Images of Oral Precancer to Detect its Malignant Potentiality

Diagnosis of oral cancer using pathology is becoming more dependent on digital imaging. Since precancerous conditions like Oral submucous fibrosis originate in the basal layer of the tissue, it is very important to investigate the cell nuclei of the basal layer in Haematoxylin and Eosin stained tissue as it contains diagnostically important information. For that, accurate identification and segmentation of the nuclei is imperative. Our algorithm uses differential contrast enhancement and distance map transformations to segment out the cell nuclei in ImageJ Software. The algorithm performed successfully on high magnification images with high speed and relative simplicity thus proving its credibility. The nuclear attributes like entropy, polarity, and compactness are calculated and the values obtained are then statistically analyzed using Mann-Whitney U Test using SPSS Software to differentiate between normal and OSF(with severe dysplasia and without dysplasia). The results showed that in case of entropy, statistical significant difference $(\mathbf{p} < \pmb{0.0001})$ is present between all the above mentioned three classes but in cases of compactness and polarity, statistical significant differences are present between normal and diseased classes, but not between OSF (without dysplasia) and OSF (with severe dysplasia) cases for both attributes $(\mathbf{p}=\pmb{0.1527}$ for compactness and $\mathbf{p}\pmb{=0.6965}$ for polarity).
Investigation on the Thermo-Optic Coefficient of Silica Fiber Within a Wide Temperature Range

In this paper, the thermo-optic coefficient of silica fiber is analyzed and measured within a wide temperature range of 0–1200 °C for the first time. To obtain the thermo-optic coefficient, experiments were carried out by using fiber optic extrinsic Fabry–Perot interferometers (EFPIs) embedded in silica fiber. The optical cavity lengths of the EFPIs were measured with white-light interferometry. The experimental results show that the first and second orders of the thermo-optic coefficient are 1.090 × 10<sup>−5</sup>/°C and 1.611 × 10<sup>−9</sup>/°C<sup>2</sup>, respectively. The quadratic model for the temperature dependence of the basic optical parameter, the refractive index, is rigorous within a wide temperature range. Furthermore, this paper also provides a temperature calibration method for extrinsic Fabry–Perot interferometric temperature sensors. The temperatures measured by the sensors are in excellent agreement with those measured by the reference thermocouple. Hence, this calibration method is promising for temperature sensors with a wide working temperature range.
Analog Carrier Recovery for Broadband Wireless Communication Links

In this paper we demonstrate the feasibility of a novel carrier recovery approach for broadband wireless communication systems with direct conversion topology through measurements. In a direct conversion IQ mixer, a part of the local oscillator (LO) signal is leaked to the radio frequency (RF) port. Consequently the RF signal traveling over the air towards receiver contains a part of the LO signal. This work introduces a method to extract the LO content from the received signal in a modulation type and baud rate independent manner with a single hardware setup.
Human Routine Change Detection using Bayesian Modelling

Automatic discovery of changes in a human's routine is one of the requirements for the future of smart home living, and its contribution to the E-health of the community. In this paper, a Bayesian modelling approach is used which models routine change discovery as a pairwise model selection problem. The method is evaluated on a collected office kitchen dataset that captures snapshots of the routine of the same person over multiple years (2014–2017). The results show that our method is able to detect not only the presence of routine changes, but also which activity patterns have been changed, fully automatically, and in a fully unsupervised manner. Moreover, changes within the same activity pattern can be discovered. Interestingly, discovered changes demonstrate subtle variations that are missed by the visual inspection of a human observer.
Estimating Secret Parameters of a Random Number Generator from Time Series by Auto-synchronization

A novel estimate system is proposed to discover the security weaknesses of a chaos based random number generator (RNG). Convergence of the estimate system is proved using auto-synchronization. Secret parameters of the target RNG are recovered where the available information are the structure of the RNG and a scalar time series observed from the target chaotic system. Simulation and numerical results verifying the feasibility of the estimate system are given such that, next bit can be predicted while the same output bit sequence of the RNG can be reGenerated.
Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning

Pitch systems are among the wind turbine components with most frequent failures. This article presents a multicomponent fault detection for induction motors and planetary gearboxes of the electric pitch drives using only the three-phase motor line currents. A deep autoencoder is used to extract features from the extended Park's vector modulus of the motor three-phase currents and a support vector machine to classify faults. The methodology is validated in a laboratory setup of a scaled pitch drive, with four commonly occurring faults, namely, the motor stator turns fault, broken rotor bars fault, planetary gearbox bearing fault and planet gear faults, under varying load and speed conditions.
Operational Rate-Constrained Beamforming in Binaural Hearing Aids

Modern binaural hearing aids (HAs) can collaborate wirelessly with each other as well as with other assistive (wireless) devices. This enables multi-microphone noise reduction over small wireless acoustic sensor networks (WASNs) to increase the intelligibility under adverse conditions. In this work, we assume one of the HAs to serve as the fusion center (FC). The optimal beamforming strategy for processing the received data at the FC depends on the acoustic scene and physical constraints (e.g., the bit-rate for transmission to the FC), and might be frequency dependent. Selection of the optimal beamforming strategy, while satisfying rate constraints on the communication between the different devices is an important challenge in such setups. In this paper, we propose an operational rate-constrained beamforming system for optimal rate allocation and strategy selection across frequency. We show an example of the proposed framework, where both the algorithm selection as well as the required rates to transmit the necessary microphone signals are optimized using uniform quantizers, while minimizing the mean-square error (MSE) distortion measure. In contrast to a well-known (theoretically optimal) reference method based on remote source coding for two devices, the presented algorithm is practically implementable and only requires knowledge of joint signal statistics at the FC. Evaluations (based on simulation experiments) show clear improvement over other practically imnlementable strateuies.
Multiscale Frequency Technique-A Robust Short-Time Series Biomedical Signal Analysis Tool for Wearable and Smart Devices

Use of wearable and smart devices for real-time telemedicine and ambulatory care is gaining importance which needs reliable, accurate and robust biomedical signal analysis by specifically exploring their signatures characteristic of their corresponding physiological events. However, such analysis faces challenges due to the complex nature of these biomedical signals which often are recorded as short time series data, presence of various noises that challenges existing complexity classification algorithms such as the commonly used Shannon Entropy (SE) and other techniques which are limited by their requirement for long time series data. The purpose of this study was to demonstrate the robustness of multi scale frequency (MSF) approach for short time series that can be non-linear and/or non-stationary for various applications. Efficacy and robustness of MSF approach was tested with respect to various noises such as white, pink and brown noise using simulated sine wave and ECG data. MSF robustly estimated complexity compared to SE with various noises thereby demonstrating its efficacy for analyzing short time series signals. The results indicate huge promise for wide range application of MSF technique on a variety of time series data including biomedical signals that can be used for advanced prognosis and diagnosis of variety of diseases for wearable and smart devices suitable for telemedicine and ambulatory care.
Semen cassiae Extract Inhibits Contraction of Airway Smooth Muscle

β2-adrenoceptor agonists are commonly used as bronchodilators to treat obstructive lung diseases such as asthma and chronic obstructive pulmonary disease (COPD), however, they induce severe side effects. Therefore, developing new bronchodilators is essential. Herbal plants were extracted and the extracts’ effect on airway smooth muscle (ASM) precontraction was assessed. The ethyl alcohol extract of semen cassiae (EESC) was extracted from Semen cassia. The effects of EESC on the ACh- and 80 mM K+-induced sustained precontraction in mouse and human ASM were evaluated. Ca2+ permeant ion channel currents and intracellular Ca2+ concentration were measured. HPLC analysis was employed to determine which compound was responsible for the EESC-induced relaxation. The EESC reversibly inhibited the ACh- and 80 mM K+-induced precontraction. The sustained precontraction depends on Ca2+ influx, and it was mediated by voltage-dependent L-type Ca2+ channels (LVDCCs), store-operated channels (SOCs), TRPC3/STIM/Orai channels. These channels were inhibited by aurantio-obtusin, one component of EESC. When aurantio-obtusin removed, EESC’s action disappeared. In addition, aurantio-obtusin inhibited the precontraction of mouse and human ASM and intracellular Ca2+ increases. These results indicate that Semen cassia-contained aurantio-obtusin inhibits sustained precontraction of ASM via inhibiting Ca2+-permeant ion channels, thereby, which could be used to develop new bronchodilators.
Beauty Premium versus Beauty Discount on Crowdfunding Platforms

By using a combination of archival data analysis and controlled experiments, this study examines the effect of physical attractiveness of recipients on donation decisions in a prosocial microlending market. We find that the physical attractiveness of female recipients matters to donors. Attractive recipients tend to have a large share of male donors, implying that male (female) donors have a more (less) favorable attitude toward those recipients. By contrast, the physical attractiveness of male recipients did not change the composition significantly. Our experiment results show that this is partly due to the fact that male donors give more to attractive female recipients because of their intuitive preference, simply liking the attractive females more. These findings have implications for the beauty effect in crowd-driven platforms and provide guidance for recipients seeking donation from crowds.
Data Mining Application in Prediction of Potential Customers of POS Machine Users in Fund Transaction

The main idea of the paper is predicting potential users of POS machine in bank system by Application of k-means and J48, classification algorithms. The card holders customer data along with book information have been collected, cleaned, integrated and transformed for testing using the clustering and classification algorithms. K-means clustering applied and used as an input for classification. Algorithm are tested certain cross validation and splitting the dataset into some percent for training and remaining for testing, techniques by setting the cluster file formed by the cluster models as dependent variables and the remaining variable as independent variables. Lastly, a comparison of the J 48 and Naïve Bayes Classification models in terms of the overall classification accuracy in classifying high level customers and accuracy in classifying low level/value customers have been undertaken. Therefore, clustering and Classification models have been the best in these evaluation parameters and thus one of algorithms selected as a better classifier in an electronic fund transaction in POS machine.
Numerical simulation of nonlinear wave propagation in symmetric cross-ply laminates with hyperelastic material behavior

Due to microstructural damages in composite structures, for example caused by cyclic loads, material degradation may occur, changing material properties. Over time these damages are likely to accumulate and can lead to an abrupt structural failure. Therefore, it is necessary to investigate or detect material degradation as a result of cyclic loading at an early stage. For this purpose, the generation of so-called cumulative second harmonic modes [1] is employed. In this work the authors will deal with numerical simulation of Lamb-wave propagation in symmetric cross-ply laminates (CPL). Based on existing work in this area for isotropic and unidirectional materials [2], the numerical investigation is extended to CPL. The aspects of material stability as well as of conditions of existence regarding cumulative second harmonic generation in CPL will be discussed.