Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of Electromagnetic Launch System Driven by a Repetitive Inductive Pulsed Power Supply

This paper discusses the feasibility of applying a repetitive inductive pulsed power supply (PPS) in electro-magnetic launch (EML) system. The repetitive inductive PPS was constructed based on a high temperature superconducting pulsed power transformer (HTSPPT). A 3-m long simple railgun model was used for the verification simulation of the PPS. The influence of circuit parameters on the launching performance was studied by using Matlab/Simulink software platform. The simulation results show that the repetitive inductive PPS is suitable for driving the EML system. The load current pulse can be cut off rapidly and the residual energy can be driven back to the primary inductor, which is beneficial to eliminate the muzzle arc of railgun system and improve the launching efficiency. Reasonable selection of circuit parameters can improve the launching performance of the system.
A System Design for Virtual Reality Visualization of Medical Image

Based on medical image data, VR technology can create a visualized three-dimensional environment of human organs in accordance with human operating habits, so that doctors can accurately determine spatial location, size, geometry, and spatial relationship with the surrounding tissue structure. This new approach will overcome the uncertainties in traditional medical visualization methods, and let doctors easily make preoperative planning and virtual surgery. This paper designed and implemented a VR visualization system for medical images, and allowing doctors to conduct disease analysis, surgical training or surgical education in a virtual environment. In particular, patients can also use virtual reality devices to communicate their illnesses with doctors, and to ease the contradiction between doctors and patients. The entire system has been basically implemented and trial run. We suggest it might has certain value for the specific application of virtual reality technology in precision medicine.
Real-World Railway Traffic Detection Based on Faster Better Network

Detection of railway shape and dangerous obstacles plays a critical role in the auxiliary driving of the train. Speed and accuracy are both of great significance to real-world railway traffic detection, which demands a higher efficiency and effectiveness. The goal of this paper is to design an architecture that achieves the right speed (for effectiveness)/accuracy (for effectiveness) balance for actual railway detection. Driven by this motivation and based on the advantages of some current algorithms, we propose FB-Net (faster better network), a robust end-to-end convolutional neural network. Detectors based on deep learning method are composed of feature extraction, candidate region generation,and classification. Specifically, our framework is focusing on with three embedded modules: 1) To improve efficiency, we replace standard convolutions with depthwise-pointwise convolutions in the feature extraction stage, aiming to red reduce model parameters; 2) To address the effectiveness, a priori module is added for candidate boxes to provide a coarse location for subsequent regressor and to reduce the searching space of objects significantly; 3) Meanwhile, we design a feature fusion module to enhance the semantic context interaction of adjacent feature maps for better detection of small objects. Experiments for railway traffic datasets on both computer device and mobile device demonstrate that FB-Net achieves good results when the input size is $320\,\,\text {pixels} \times 320$ pixels.
1 Gb/s Underwater Optical Wireless On-Off-Keying Communication with 167 MHz Receiver Bandwidth

We demonstrate bandwidth-limited 420 nm, 1.1 Gb/s on-off keying (OOK) based underwater optical wireless communication (UOWC) with only 167 MHz bandwidth by using maximum likelihood sequence estimator (MLSE), with a record spectral efficiency of 6.58 b/s/Hz for OOK based UOWC communication. The MLSE is adopted here to compensate the bandwidth limitation mainly caused by the photodiode (3 dB bandwidth is 150 MHz) at the receiver. By using 7 and 10 taps MLSE detection and assuming BER threshold of 3.8×10−3, the bitrate that can be achieved after 1 meter underwater transmission is 0.9 and 1.1 Gb/s, which is more than two times of data rate using conventional symbol-by-symbol detection.
Land Cover Classification of Very High Resolution Image Using Graph-Cuts

Land cover classification from very high resolution (VHR) image (e.g. QuickBird, IKONOS) is an extremely challenging task due to high spatial heterogeneity and low spectral separability. Markov random field (MRF) based classification methods have proven to be effective for solving the above problem by integrating contextual information into classification process. Existing energy minimization approaches for the MRF-based image classification model cannot balance classification accuracy and computational efficiency. Focusing on efficient energy minimization of the MRF-based image classification model, we apply two graph cuts algorithms, called the swap move algorithm and the expansion move algorithm to classify land cover using VHR image. The proposed methods turn out to be fast, simple, and robust. A set of experimental results of QuickBird images demonstrate that the proposed methods can obtain the land cover classification results similar with manual interpretation with homogeneity interior and smooth boundaries.
Detection Mechanism of FDI Attack Feature Based on Deep Learning

At present, with the continuous optimization of grid standards in power system, the degree of convergence of information and communication technologies has become increasingly closer. However, it has also led to the exposure of important infrastructures in the smart grid, especially to the advanced measurement systems in smart metering communication systems. And the most typical type of attacks is False data injection (FDI). Therefore, consumer-centric communication devices, such as Phaser Measurement Units (PMUs) and smart meters, provide powerful support for data acquisition and transmission. As these devices facilitate the acquisition, transmission and consumption of power system data through the integration of communications, some malicious users initiate attacks against measured data in the meter for their own or other economic intention. This kind of attack behavior, on the one hand, affects the availability of meter data. On the other hand, it seriously undermines the authenticity and confidentiality of the measurement data itself. Therefore, in this paper, we proposed attack recognition mechanism based on Deep Belief Network to extract attack features. Our aim is to study the FDI attack behavior and accurately extract the relevant features of the behavior, and provide an effective criterion for the accurate identification of attack behavior in the smart grid. At the same time, through the optimization of the number of nerve cells and the number of layers in each layer of the deep neural network to ensure the real-time detection, the security defense system of the power grid is further enhanced.
Comprehensive Sensitivity Analysis and Multiobjective Optimization Research of Permanent Magnet Flux-Intensifying Motors

In this paper, two permanent magnet flux-intensifying motors are designed and optimized to realize the characteristic of Ld > Lq and the flux-intensifying effect. Compared with the conventional interior permanent magnet motor with the characteristic of Ld < L q, the advantages of a low irreversible demagnetization risk and a wide speed range can be obtained in the motors. To meet multiple design requirements effectively, a comprehensive sensitivity analysis method is first implemented to evaluate the influence of each design variable on the selected optimized objectives of output torque, reverse saliency ratio, and torque ripple. Second, a sequential nonlinear programming algorithm is used for realizing the multiobjective optimizations of strong-sensitive design variables. Then, the performances of the two optimal motors are analyzed and compared with the initial permanent magnet motor in detail. Finally, two prototype motors are manufactured and tested. Both the simulation and experimental results verify the validity of the new flux-intensifying motors and the optimization method.
An Active Learning Approach to Transcript Coding for Education Research

Educational research relies on a labor intensive analysis of qualitative data. Many times, data takes the form of transcribed conversations between students and teachers. These transcripts are analyzed, annotated, and coded to help uncover patterns and ideas. An education researcher will comb through the documents, line by line, classifying each line or paragraph into a pre-described category. This paper presents the framework of a new tool that has the potential to greatly aid education researchers as they analyze qualitative data. By utilizing statistical machine intelligence tech- niques for Natural Language Processing (NLP), and leveraging existing Deep Learning language models, the tool completes much of the grunt work of transcript coding, letting the researcher focus his or her attention on the areas that are important.
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox

This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.
Fecal Metagenome Sequences from Lactating Dairy Cows Shedding Escherichia coli O157:H7

Cattle are primary reservoirs of Escherichia coli O157:H7, a causative agent of severe human infections. To facilitate analyses of the communities in which this pathogen is found, we sequenced the fecal metagenomes of 10 dairy cows shedding E. coli O157:H7 and added them to the public domain. ABSTRACT Cattle are primary reservoirs of Escherichia coli O157:H7, a causative agent of severe human infections. To facilitate analyses of the communities in which this pathogen is found, we sequenced the fecal metagenomes of 10 dairy cows shedding E. coli O157:H7 and added them to the public domain.
Physical Layer Encryption for Industrial Ethernet in Gigabit Optical Links

Industrial Ethernet is a technology widely spread in factory floors and critical infrastructures where a high amount of data need to be collected and transported. Fiber optic networks at gigabit rates fit well with that type of environment, where speed, system performance, and reliability are critical. In this paper, a new encryption method for high-speed optical communications suitable for such kinds of networks is proposed. This new encryption method consists of a symmetric streaming encryption of the 8b/10b data flow at physical coding sublayer level. It is carried out thanks to a format preserving encryption block cipher working in CTR (counter) mode. The overall system has been simulated and implemented in a field programmable gate array. Thanks to experimental results, it can be concluded that it is possible to cipher traffic at this physical level in a secure way. In addition, no overhead is introduced during encryption, getting minimum latency and maximum throughput.
Decoupled parallel recursive Newton-Euler algorithm for inverse dynamics

An efficient parallel implementation of the robot inverse dynamics based on the recursive Newton-Euler formulation is considered. The algorithm basically partitions the computations related to a manipulator link among separate processors, resulting in a parallel architecture in which the number of processors equals the number of degrees of freedom of the manipulator. This has considerably low inherent parallelism if the forward propagation of velocities and accelerations and the backward propagation of forces and torques are synchronized. Therefore, in order to maximize the parallelism, the synchronization is completely removed, leaving each processor on the most recent values of the propagated variables. Since the mathematical approach for error analysis is too complex and the simulation approach is incomplete and unreliable, the algorithm is investigated on a real, multiprocessor machine, KUMARAN. A series of experiments with the model of the PUMA 560 manipulator and randomly generated inputs has shown surprisingly small errors. Some improvements of the algorithm which do not impair the time efficiency but provide further reduction of the approximation error are presented. <<ETX>>
First Identification and Genomic Characterization of a Porcine Sapelovirus from Corsica, France, 2017

We report the isolation and genomic characterization of a Sapelovirus A strain, or porcine sapelovirus (PSV), from a diarrheic Corsican piglet in France. It shares 87% nucleotide identity with a 2014 German isolate. ABSTRACT We report the isolation and genomic characterization of a Sapelovirus A strain, or porcine sapelovirus (PSV), from a diarrheic Corsican piglet in France. It shares 87% nucleotide identity with a 2014 German isolate.
Performance Modeling towards Interrupt System of Virtualized Cryptography Device

For the problem that the large number of interrupts leads to decrease of the performance of the single-root I/O virtualization cryptography device, we establish an M/M/1 queueing model with preemption priority based on the queuing theory towards interrupt system of virtualized cryptography device. Specifically, we simplify the model taking the average value of interrupt request interval. We find that the long interrupt transmission time and the large interrupt request frequency are the main factors which lower the performance of interrupt system of virtualized cryptography device. In particular, we verify the effectiveness of the model and the correctness of the conclusion by MATLAB simulation and experimental testing. The results show that we can get the system performance index through analyzing the model quantitatively. What’s more, this model helps guide the optimization of interrupt system of virtualized cryptography device.
Airgap Search Coil-Based Detection of Damper Bar Failures in Salient Pole Synchronous Motors

Damper bars are often used in high output salient pole synchronous motor (SM) rotors for motor starting and stabilization of performance. Several cases of broken damper bars in the rotor of SMs that lead to motor starting failure and/or forced outage have recently been reported. However, detection of damper bar failures is difficult, since the damper bars are active only under the starting or load transients. Damper bar testing in the field currently relies on off-line visual inspection, and there is no means available for detecting failures on-line. In this paper, a new method for detecting damper bar failures based on the analysis of airgap search coil measurements under the starting transient is proposed. Since airgap search coils are being increasingly installed in large SMs for detecting field winding short circuits, the proposed method can be implemented without additional hardware. The effectiveness of the proposed method is verified with 2 dimensional finite element analysis and experimental testing on a custom-built 30 kW salient pole SM under controlled fault conditions. It is shown that the proposed method can provide reliable and sensitive detection of broken damper bars whenever the motor is started at low additional cost.
Effect of morphological deformation on barriers to charge injection at the interface of copper and polyethylene

Barriers to charge injection at polymer/metal interface is key to understanding high field conduction in cables and capacitors. The present paper investigates barriers to charge injection at the interface of copper and polyethylene (PE), one of the most dominant material combination in the power industry, with a focus on morphological deformation. Using computational quantum mechanics in the framework of density functional theory (DFT) barriers to charge injection where calculated showing similar results to previous DFT studies on the interface of Pt, Au, and Ag in terms of the absolute barriers to hole and electron injection. However, states appearing between the Fermi level of Cu and the conduction band of PE where identified leading to the lowering of the barrier to electron injection by about 0.9 eV. These states were due to the morphological deformation that takes place for both the metal and the polymer at the interface. No similar states were identified in the case of Pt, Au and Ag in similar studies. The paper stresses on the importance of considering morphological deformation in lowering the barriers to charge injection in soft metal which is less considered as compared to chemical impurities.
Hierarchical-Dynamic Embedding for Zero-Shot Object Recognition

Zero-shot object recognition is aiming to attach unseen category labels to images which are out of the training set. The key challenge in Zero-shot learning is building the map between visual domain and semantic domain. However, previous Visual-Semantic Embedding ignores the essential difference between the vectors of category names and the vectors of the entities. Hybrid model, moreover, computes the middle vector with a fixed size candidate set which limits the generalization on different images. So we propose a novel framework named Hierarchical-Dynamic Embedding. First, Hierarchical Network Embedding (HNE) takes advantage of the internal hierarchical taxonomy of the category names. We then provide Dynamic Hybrid Model (DHM) to map unseen images from visual vectors to entity vectors. Furthermore, we conduct the experiments on 1,000 seen categories and 1,548 unseen categories to show the state-of-the-art performance of our proposed framework.
Magnetometer Bias Finite-Time Estimation Using Gyroscope Data

This paper deals with the problem of estimating the hard iron bias affecting the measurements provided by a triaxial magnetometer. The estimation is carried out using measurements from a magnetometer and a gyroscope mounted on the same inertial measurement unit. The described solutions ensure finite-time estimation performance and robustness against measurements noise. The effectiveness of the proposed solutions is proved by experimental tests carried out using real inertial sensors.
Triple-Phase-Shift Control Strategy for Full-Bridge Three-Level (FBTL) DC/DC Converter

This paper proposes a triple-phase-shift (TPS) control strategy for the full-bridge three-level (FBTL) DC/DC converter to reduce the voltage stress and harmonics on the transformer. The proposed control strategy is composed of three phase-shift delays to keep the maximum voltage changes $(\mathbf{\Delta} \mathbf{V})$ on the transformer's primary side at only half of the input voltage $(\boldsymbol{V}_{in}/2)$, which is different from the conventional control strategies causing the higher voltage changes with value of ${V}_{{i}\boldsymbol{n}}$. Therefore, the proposed control strategy can effectively reduce the voltage change rate $(\boldsymbol{d}\boldsymbol{v}/\boldsymbol{d}\boldsymbol{t})$ and voltage harmonics on the transformer due to the benefits of multi-level voltage produced by the three phase-shift delays. Additionally, the proposed TPS control strategy has two working modes, which can thus have wide voltage gain. Finally, the experimental results are presented to verify the proposed control strategy.
Price-Sensitivity Aware Load Balancing for Geographically Distributed Internet Data Centers in Smart Grid Environment

In this paper, we investigate the problem of developing a geographical load balancing (GLB) scheme for distributed Internet data centers (IDCs) when they are price-makers in the deregulated electricity markets, i.e., GLB may impose impact on electricity prices in IDC locations due to the large power consumption of IDCs. Taking into account the dynamic characteristics (e.g., time-varying power demands and generation outputs) and actual physical constraints (e.g., active/reactive power flow balancing, transmission congestion, and network loss) of smart grids, it is difficult to obtain price impact models with analytical expressions accurately. As a result, it is challenging to design an efficient GLB scheme without requiring price impact models. To overcome the above challenge, we propose a price-sensitivity aware GLB scheme. The key idea of the proposed scheme is to impose proper limits on the workloads allocated to the IDC locations with high price-sensitivity coefficients by exploiting a number of interactive information between IDCs and main grids, so that the sudden increase in the total cost could be avoided. Here, the price-sensitivity coefficient is defined as the ratio of the percentage change in price to the percentage change in IDC power demand. Extensive simulation results show the effectiveness of the proposed GLB scheme.
A Very High Efficiency Circuit Topology for a Few kW Inverter Based on Partial Power Conversion Principle

One trend of the fundamental enabling technologies into the direction of the eco-society with more electric power consumption is realization of ultra-high efficiency electric power conversion technologies. In this paper, a very high efficiency circuit topology for a few kW inverter is proposed based on a principle of partial power conversion. The new topology is made of (1) HEECS chopper for full rectified waveform generation and (2) unfolding full bridge inverter for full sinusoidal wave synthesis. The theoretical efficiency is derived, and the preliminary experimental verification confirmed the efficiency expectation. Tentatively, 99.2% efficiency was measured for the DC-to-full-rectified-waveform conversion at 3.6 kW output.
Reinforcement Learning Strategies for Energy Management in Low Power IoT

Energy management in low power IoT is a difficult problem. Modeling the consumption of a sensor node is complicated, they operate in a stochastic environment. They harvest energy in their environment but energy sources present time-varying behavior. It becomes hazardous to predict in advance the energy behavior of our system. In this paper we propose a new approach using both neural networks to estimate the harvesting energy and reinforcement learning algorithms to find the operating parameters to maximize the node’s performance while preserving its energy resources.