Miracle-dz/UniFAR-FTUs huggingface dataset:
Probability of Detection for Eddy Current Pulsed Thermography of Angular Defect Quantification

Eddy current pulsed thermography (ECPT) as one of the emerging nondestructive testing and evaluation (NDT&E) techniques has been used for the evaluation of the integrity of rail tracks, especially for rolling contact fatigue (RCF) detection and crack sizing. This paper proposes a probability of detection (POD) analysis framework for the ECPT system. Specifically, three different features, i.e., max thermal response, first-order differential imaging, and ratio mapping of the first-order differential imaging, were used to quantify the length of the angular slot by linear fitting. Based on the fitting relation between these features and the slot length, POD curves for linear-coil-based ECPT system of angular defect detection were calculated and compared. Results show that the max thermal response feature has the highest repeatability and detectability for shorter slot detection. First-order differential imaging and ratio mapping features are more convincing for longer slot detection.
A Novel Model Predictive Control Method with Discrete Space Vector Modulation for Neutral-Point Voltage Balancing of Vienna-Type Rectifier

This paper presents a novel model predictive control (MPC) method with discrete space vector modulation (DSVM) to balance neutral-point voltage of Vienna-type rectifier. With the virtual vectors, more feasible voltage vectors can be used for the MPC method to reduce input current ripple. Meanwhile, compared with the normal finite control set MPC (FCS-MPC) neutral-point balancing control method, which the predictive neutral-point voltage difference is added to cost function, the proposed DSVM-MPC method just selects the optimal redundant small vector without calculating predictive neutral-point voltage, so it is simple to realize. The proposed DSVM-MPC method only takes real and virtual feasible vectors into account after judging the current sector, which can reduce the amount of MPC calculation of three-level rectifier. The DSVM-MPC method with selected small vectors can improve the ability of balancing and the performance of input current. The proposed DSVM-MPC method is investigated through simulations and validated through experimental results with a laboratory prototype.
The Design of A 2-12GHz Scaning Tightly Coupled Dipole Array

This paper present a design for tightly coupled dipole array (TCDA) unit with microstrip tapered dipole and balun structure. Tapered dipole enhance directivity of TCDA unit. The infinite array is simulated by HFSS, which achieves 6:1 bandwidth(2-12GHz) with $\pmb{\mathrm{VSWR} < 2.8, \pm 45^{\circ}}$ scanning in all planes, and $\pmb{ < -10\mathrm{dB}}$ cross-polarization.
Assessment of Modular Multilevel Converter with Partly Integrated Battery Energy Storage System

Modular multilevel converter can be applied to battery energy storage system by integrating battery with submodules. This paper focuses on the assessment of modular multilevel converter with partly integrated battery energy storage system based on carrier phase shift pulse width modulation, namely the battery equipment ratio or battery fault-tolerant ratio of system. Firstly, power balancing restrictions are analyzed to keep capacitor voltages balanced. Then, this paper analyzes the modulation restrictions in order to avoid over-modulation. Besides, the external characteristic must remain unchanged. By combining these restrictions, the relationships among related parameters, including battery equipment ratio, modulation ratio and power factor angle of AC bus, can be obtained. At last, the correctness of relationships is verified through MATLAB/Simulink simulation.
Unconstrained Non-negative Factorization of High-dimensional and Sparse Matrices in Recommender Systems

Non-negativity is vital for latent factor models to preserve the important feature of most high-dimensional and sparse (HiDS) matrices, e.g., none of their entries is negative. With the consideration of non-negativity, the training process of a latent factor model should be specified to achieve constraints-incorporated learning schemes. However, such schemes are neither flexible nor extensible. This work investigates algorithms of inherently non-negative latent factor analysis, which separates non-negativity constraints from the training process. Based on a deep investigation into the learning objective of a non-negative latent factor model, we separate the desired latent factors from decision variables involved in the training process via a single-element-dependent mapping function that makes the output factors inherently non-negative. Then we theoretically prove that the resultant model is able to represent the original one effectively. As a result, we design a highly efficient algorithm to bring the Inherently Non-negative Latent Factor model into practice. Experimental results on three HiDS matrices from industrial recommender systems show that compared with state-of-the-art non-negative latent factor models, the proposed one is able to obtain advantage in prediction accuracy with comparable or higher computational efficiency. Moreover, such high performance is achieved through its unconstrained optimization process on the premise of fulfilling the non-negativity constraints. Hence, the proposed model is highly valuable for industrial applications required to handle HiDS matrices subject to non-negativity constraints.
Estimation of PWM - Induced Iron Loss in IPM Machines Incorporating the Impact of Flux Ripple Waveshape and Nonlinear Magnetic Characteristics

This paper presents an improved estimation model to predict the additional iron loss due to pulsewidth modulation (PWM) switching in interior permanent magnet (IPM) machines. The input current including the predicted PWM-induced current ripple is fed to 2-D finite-element analysis to calculate the PWM-induced flux ripple distribution in the analyzed machine. A special frequency decomposition method is proposed to separate the predicted flux ripple into 1x and 2x PWM frequency components. Combining these results with the modified dynamic Jiles-Atherton model, the PWM-induced iron loss is calculated incorporating the impact of the actual flux ripple waveshape and the lamination steel's nonlinear magnetic characteristics. Experiments have been conducted to evaluate the prediction accuracy and investigate the impact of several factors on PWM-induced core losses (i.e., both iron and magnet loss) in IPM machines. These results demonstrate that the model offers promising estimation accuracy over a wide range of machine and inverter operating conditions.
1/ $f$  Noise Reduction Using In-Pixel Chopping in CMOS Image Sensors

In this letter, an in-pixel chopping technique to reduce the low-frequency or 1/<inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula> noise of the source follower (SF) transistor in an active pixel sensor (APS) is presented. The SF low-frequency noise is modulated at higher frequencies through chopping, implemented inside the pixel, and in later stage eliminated using low-pass filtering. To implement the chopping, the conventional 3T APS architecture is modified, with only one additional transistor of minimum size per pixel. Reduction in the noise also enhances the dynamic range of the image sensor. The prototype sensor, consists of a <inline-formula> <tex-math notation="LaTeX">$ {128} {\times } {128}$ </tex-math></inline-formula> sized pixel array with in-pixel chopping and column-level read-out circuit, is fabricated in AMS 0.35-<inline-formula> <tex-math notation="LaTeX">$ {\mu }\text{m}$ </tex-math></inline-formula> CMOS OPTO process. The pixel is laid out at the pitch of 10.5 <inline-formula> <tex-math notation="LaTeX">$ {\mu }\text{m}$ </tex-math></inline-formula> with a fill-factor of 30%. To validate the proposed technique a <inline-formula> <tex-math notation="LaTeX">$ {2} {\times } {2}$ </tex-math></inline-formula> pixel array is characterized. The output noise integrated in the frequency band from 1 Hz to 20 kHz is measured as 210 <inline-formula> <tex-math notation="LaTeX">$ {\mu }\text{V}_{\textrm {RMS}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$191~ {\mu }\text{V}_{\textrm {RMS}}$ </tex-math></inline-formula> at chopping frequency (<inline-formula> <tex-math notation="LaTeX">$ {f}_{\textrm {ch}}$ </tex-math></inline-formula>) of 4 MHz and 8 MHz, respectively, which shows a reduction in the noise power by 13.3 and 14.2 dB, respectively.
A Method to Extract Indirect Coupling and Measure Its Complexity

The process of software change requires programmers to understand the structure of the systems and the effect that is produced by the modifications they make. Overall, programmers need effective methods to trace the dependencies between components, to control the evolution of quality and to understand changes. Therefore, the extraction of coupling is a valuable input to enable developers in the analysis of changes to perform additional modifications in a successful manner. Consequently, this research proposes a method to extract indirect coupling and to measure the complexity of the call chains and their conforming entities. The extraction of coupling chains is carried out using static analysis and the Abstract Syntax Trees (AST) of languages. The method is aimed to support programmers in understanding the internals of systems and how modifications influence the complexity and quality of software. The paper explains the main concepts of direct and indirect coupling, the proposed approach and presents the results obtained, which show that implementation and testing of the method were successful.
Restriction Fragment Length Polymorphism-based Genotyping of Toxoplasma gondii from Autopsy-Proven Cases of Acquired Immunodeficiency Syndrome-associated Cerebral Toxoplasmosis

Context: Published data on genetic characterization of Toxoplasma gondii (T.gondii) from clinical cases of toxoplasmosis from India is lacking. Aims: The present study was aimed at identifying genetic types of T. gondii in fatal cases of cerebral toxoplasmosis (CT) associated with HIV, from India. Settings and Design: Archived tissues of CT were obtained postmortem from 25 acquired immunodeficiency syndrome patients between 2000 and 2014. Subjects and Methods: Direct amplification of eight different loci, namely, SAG1, 5'-3'SAG2, Alt. SAG2, SAG3, BTUB, GRA6, C22-8, and L358 followed by restriction fragment length polymorphism was used to genotype the parasite. Results: The canonical Types I, II, or III were not found in our study. More than 96% of the cases harbored atypical genotypes–likely recombinants of the canonical types; one case closely corresponded to Type II genotype. Conclusions: Thus, a majority of T. gondii causing CT in South India belonged to a noncanonical lineage. These nonarchetypal genotypes differed from the conventional Types I, II, and III and caused devastating severity in patients with CT in the background of HIV. These results are a step further to deciphering the population genetics of this important zoonotic parasitic infection in Indian patients, information that has thus far been lacking.
Complete Genome Sequences of Streptococcus sobrinus SL1 (ATCC 33478 = DSM 20742), NIDR 6715-7 (ATCC 27351), NIDR 6715-15 (ATCC 27352), and NCTC 10919 (ATCC 33402)

The bacterium Streptococcus sobrinus causes tooth decay in humans. We present complete circularized genome sequences for four strains of S. sobrinus, type strain SL1, strain NIDR 6715-7 and the related NIDR 6715-15, and strain NCTC 10919. ABSTRACT The bacterium Streptococcus sobrinus causes tooth decay in humans. We present complete circularized genome sequences for four strains of S. sobrinus, type strain SL1, strain NIDR 6715-7 and the related NIDR 6715-15, and strain NCTC 10919. The finished genomes will enable genomic comparisons between S. sobrinus and other cariogenic microbes.
R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks

Terahertz human body security images have low resolution and a low signal-to-noise ratio. In the traditional method, image segmentation, positioning, and identification are applied to detect objects carried by humans in the THz images. However, it is difficult to satisfy the requirements of detection accuracy and speed with this approach. The current research presents a faster detection framework (R-PCNN) combining the preprocessing and structure optimization of Faster R-CNN. The experiment results show that this method can effectively improve the accuracy and speed of object detection in human body THz images. A detection accuracy of 84.5% can be achieved in dense flow scenes, with an average detection time of less than 20 milliseconds for each image.
Effects of Thermo-Mechanical Fatigue and Low Cycle Fatigue Interaction on Performance of Solder Joints

This paper demonstrates the feasibility of low cycle fatigue effects on the thermo-mechanical fatigue evolution in the solder joints of a power module. To achieve this goal, finite-element method (FEM) simulation and experimental studies have been carried out. One of the prepared samples was exposed to the sole thermal cycling while another one underwent frequent drop impacts, as an indicator of low cycle fatigue event, during the thermal cycling. The FEM results indicate that the thermal cycling leads to the accumulated creep strain in the solder joints. The amount of creep strain considerably increases when the drop impacts are coupled to the thermal cycling. This phenomenon is due to the additional peeling stress applied by drop impacts to the solder joints. It is also revealed that with an increase in the number of drop impacts, the rate of strain accumulation declines which may be due to the restriction of changes led by stress triaxiality. The fractography also validates that the sample exposed to both drop and thermal loadings shows an entire brittle fracture mode. Moreover, the EDS map shows that the elemental distribution heterogeneity and intermetallic growth were created under mutual effects of drop impacts and thermal cycle fatigue.
Wind Energy Conversion System Based on DFIG with Three-Phase Series Active Filter Operating with Floating Capacitors

In this paper a Wind Energy Conversion System based on Doubly-Fed Induction Generator (DFIG) with a three-phase series active filter without additional transformer is proposed. The series active filter acts to eliminate the distortions of the grid voltages in the stator windings of DFIG and It operates with floating capacitors. The control system regulates the floating capacitor voltage with no need for external sources. Simulation and experimental results are presented to evaluate the proposed system with series active filter.
A Highly Sensitive Flexible Piezoresistive Sensor Based on Wrinkled CNT-PDMS

This paper presents a highly sensitive flexible piezoresistive sensor based on wrinkled structure. By adding carbon nanotube(CNT) into the mixing of polydimethylsiloxane(PDMS), conductive PDMS can be fabricated. Patterned with monolayer wrinkle structure, this stretchable CNT PDMS can be used as a thin and flexible pressure sensor. The wrinkle structure enables the sensor with high sensitivity (8.3 kPa-1) in low pressure region(0.01kPa). In addition, the result of force image test shows that the sensor array can be used into detecting pressure distribution and measuring force images.
Indoor Micro-Activity Recognition Method Using Ubiquitous WiFi Devices

This paper presents a wireless device-free WiFi-based micro-activity recognition system by leveraging the Channel State Information (CSI) of WiFi signals. Unlike previous CSI-based micro-activity recognition methods, the proposed method extracts both amplitude and phase information of CSI, which provides more information and increased the detection accuracy. The proposed approach presents an efficient signal processing method to expose unique patterns of each activity. Moreover, a machine learning algorithm is applied to classify the proposed micro-activities. The proposed prototype has been evaluated in different indoor environments, and the empirical results show the efficiency of the proposed prototype with several volunteer users.
Draft Genome Sequence of Marine Bacillus sp. Strain ISO11, a Candidate Finfish and Shellfish Probiotic

Bacillus sp. strain ISO11, a Bacillus cereus clade member isolated from the intestinal tract of Fundulus heteroclitus, possesses potential probiotic and antibacterial activity against Vibrio sp. ABSTRACT Bacillus sp. strain ISO11, a Bacillus cereus clade member isolated from the intestinal tract of Fundulus heteroclitus, possesses potential probiotic and antibacterial activity against Vibrio sp. pathogens. Antibacterial activity is likely due to production of microcin and a zwittermicin A-like aminopolyol. The genome sequence will assist in identifying additional related processes.
Effects of thermal aging on moisture equilibrium in oil-paper insulation

Recent studies have reported that thermal aging of insulation paper affects its moisture absorption property, which can affect moisture equilibrium in oil-paper insulation. In engineering practice, Fessler's model is one of the most widely used methods to estimate the moisture content of paper through measurement of moisture content of oil. However, this model lacks consideration of the thermal aging effect on equilibrium moisture. This study comprehensively investigated the effect of thermal aging on moisture equilibrium in oil-paper insulation. Based on the accelerated ageing and moisture measurements, the equilibrium moisture content of insulation paper with different aging and temperatures were obtained. The effects of aging condition and the temperature on equilibrium moisture are discussed and Fessler's model was modified accordingly. The results show that: 1) the decreasing of specific surface area is the dominant factor that affects the moisture equilibrium in oil-paper insulation, which is caused by thermal aging; 2) the modified Fessler model is reasonable and suitable to estimate the equilibrium moisture content of insulation paper with different aging.
Thermal Modeling and Simulations for Fast Electric Fuses

This article presents a thermal modeling and simulation of fast electric fuses used for power electronic protection equipment. These devices still have low overload abilities and need fast protection, justifying the researches in this domain area. For a fast fuse it was realized a 3D model with the Pro-Engineer software. Temperature distribution through the fuse and through all fuselink elements simulated and analyzed. Thus, with the 3D simulation, the design of the fuse can be improved and can be estimated better solutions to protect the power semiconductor equipment.
An Efficient On-Demand Latency Guaranteed Interactive Model for Sensor-Cloud

Motivated by the Internet-of-Things (IoT) cloudification as the trend to implement IoT applications, an efficient design for interactions between sensors and cloud is necessary. In this paper, we propose an efficient interactive model that is designed for the sensor-cloud integration to enable the sensor-cloud to simultaneously provide sensing services on-demand to multiple applications with various latency requirements. In the proposed model, complicated functions are offloaded to the cloud, and only the light-weight processes are executed at resource constrained sensor nodes. We design an aggregation mechanism for the sensor-cloud to aggregate the application requests so that the workloads that are required for sensors are minimized, thereby saving energy. The latency of sensing packets from sensor-to-cloud is controlled by the sensor-cloud based feedback control theory. Based on the feedback from the sensor-cloud, physical sensor nodes optimize their scheduling accordingly to save energy while maintaining the latency of all sensing flows routed through them satisfied the requirements of applications. Extensive experimental and analysis results show that the proposed model effectively controls the latency of sensing flows with a low signaling overhead and a high energy efficiency compared with the state-of-the-art scheme.
KNN-Based Representation of Superpixels for Hyperspectral Image Classification

Superpixel segmentation has been demonstrated to be a powerful tool in hyperspectral image (HSI) classification. Each superpixel region can be regarded as a homogeneous region, which is composed of a series of spatial neighboring pixels. However, a superpixel region may contain the pixels from different classes. To further explore the optimal representations of superpixels, a new framework based on two <italic>k</italic> selection rules is proposed to find the most representative training and test samples. The proposed method consists of the following four steps: first, a superpixel segmentation algorithm is performed on the HSI to cluster the pixels with similar spectral features into the same superpixel. Then, a domain transform recursive filtering is used to extract the spectral–spatial features of the HSI. Next, the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbor (KNN) method is utilized to select <italic>k</italic><inline-formula><tex-math notation="LaTeX">$_1$ </tex-math></inline-formula> representative training samples and <italic>k</italic><inline-formula> <tex-math notation="LaTeX">$_2$</tex-math></inline-formula> test pixels for each superpixel, which can effectively overcome the within-class variations and between-class interference, respectively. Finally, the class label of superpixels can be determined by measuring the averaged distances among the selected training and test samples. Experiments conducted on four real hyperspectral datasets show that the proposed method provides competitive classification performances with respect to several recently proposed spectral–spatial classification methods.
Dynamic Representation Learning for Video Action Recognition Using Temporal Residual Networks

Recent progresses in video action recognition are driven by the great success of convolutional neural networks (ConvNets). Many researches have investigated modeling spatiotemporal architecture with ConvNets to take full advantage of video information. However, the quality of video representations that serve as the network inputs, is often insufficient due to significant background motion. In this paper, we propose an Improved Dynamic Image (IDI) to describe videos by applying salient object detection and rank pooling on sequence of still images. Saliency detection aims to segment objects of interest which can be viewed as image-level background motion removal, while rank pooling is used to construct dynamic images as input to ConvNets. We also present a temporal Residual Network (ResNet) architecture which directly operates on multiple IDIs for long-term video representations learning. Experiments on two standard action recognition benchmarks demonstrate that our method achieves state-of-the-art performance without combining video-level representations (e.g. dense optical flow) as well as hand-designed features (e.g. improved dense trajectories).