Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Spatial-Temporal Distribution Characteristics of the Discharge Process in a 1 m Rod-Plate Gap Under Different Polarity Lightning Impulses

The observation of the air gap discharge process is the basis of revealing the physical mechanism of gas discharge and establishing the accurate numerical simulation model. In order to obtain the variation characteristics at different development stages during the air gap discharge processes under positive and negative lightning impulses in this paper, repetitive discharge tests of 1 m rod-plate air gap under standard lightning impulse were carried out in the National Engineering Laboratory (Kunming) for Ultrahigh-voltage Engineering Technology. An improved discharge observation method, using electron-multiplying intensified charge-coupled device (EMICCD) camera, was adopted in the test. A series of discharge spatial-temporal distribution images with nanosecond exposure time and interval time were captured by changing the shooting time delay of the EMICCD in repetitive discharges. The complete discharge development process was reproduced by the image stitching. The development process and the characteristics of the air gap discharge under lightning impulse were analyzed qualitatively. The characteristics of the measured discharge current under the positive lightning impulse were discussed. The testing results and the analysis show that the shape and the development process of the ionization region of the streamer are quite different under different lightning impulses. There are spherical and fan-shaped streamer regions under the positive and negative lightning impulse, respectively. After the streamer runs through the entire gap, the leader develops from the rod electrode to the plate electrode (under the positive impulse) or from both the rod and the plate electrode to the middle area (under the negative impulse). Then the final jump occurs and the gap is broken down. If the streamer still cannot penetrate the entire gap when the applied impulse voltage exceeds the peak, the gap will not be broken down finally.
Ensemble Learners of Multiple Deep CNNs for Pulmonary Nodules Classification Using CT Images

Various deep convolutional neural networks (CNNs) have been used to distinguish between benign and malignant pulmonary nodules using CT images. However, single learner usually presents unsatisfied performance due to limited hypothesis space, or falling into local minima, or wrong selection of hypothesis space. To tackle these issues, we propose to build ensemble learners through fusing multiple deep CNN learners for pulmonary nodules classification. CT image patches of 743 nodules are extracted from LIDC-IDRI database and utilized. First, eight deep CNN learners with different architectures are trained and evaluated by 10-fold cross-validation. Each nodule has eight predictions from the eight primary learners. Second, we fuse these eight predictions by the strategies of majority voting (VOT), averaging (AVE), or machine learning. Specifically, different machine learning algorithms including K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Naive Bayes (NB), Decision Trees (DT), Multi-layer Perceptron (MLP), Random Forests (RF), Gradient Boosting Regression Trees (GBRT) and Adaptive Boosting (AdaBoost) are implemented. Moreover, the correlation coefficients between the predictions of 10 ensemble learners are calculated, and the hierarchical clustering dendrogram is drawn. It is found that the ensemble learners achieve higher prediction accuracy (84.0% vs 81.7%) than single CNN learner. The overlap ratio among the 10 ensemble learners is much higher than that of the 8 primary learners (62.9% vs 33.2%). In addition, it is shown that ensemble learners are roughly divided into three categories: the first (SVM, MLP, GBRT and RF) achieves the best performance; the second (VOT and AVE) is better than the third (AdaBoost, DT, NB and KNN). VOT and AVE yield higher recall than the machine learning algorithms. These results indicate that ensemble learners based on multiple CNN learners can achieve better performances for pulmonary nodules classification using CT images and that preferred fusion strategies include SVM, MLP, GBRT and RF.
Engineering Tunable Strain Fields in Suspended Graphene by Microelectromechanical Systems

Here, we present a micro-electromechanical system (MEMS) for the investigation of the electromechanical coupling in graphene and potentially related 2D materials. Key innovations of our technique include: (1) the integration of graphene into silicon-MEMS technology; (2) full control over induced strain fields and doping levels within the graphene membrane and their characterization via spatially resolved confocal Raman spectroscopy; and (3) the ability to detect the mechanical coupling of the graphene sheet to the MEMS device with via their mechanical resonator eigenfrequencies.
Proactive Resilient Scheduling for Networked Microgrids With Extreme Events

In this paper, we propose a risk-constrained adaptive robust optimization approach to provide proactive resilient scheduling decisions for multiple networked microgrid central controllers under potential extreme events. Our objective is to minimize both risks of false judgement made by microgrid central controllers and damage done to networked microgrids by extreme events through a proactive resilient scheduling process. A risk-constrained adaptive robust optimization approach is developed to handle risks and uncertainties associated with: (i) extreme events that may occur and contingency issues linked to influential buses; (ii) renewable energy sources power generation; (iii) human reactions when faced with an extreme event; and (iv) status of combined cooling, heat and power units. “Budget of uncertainty” and risk-management parameters are utilized together to overcome both overconservative issues of conventional robust optimization and human errors that may occur when making decisions. Extensive simulation results from real-world data sets show that the risk-constrained adaptive robust optimization approach we propose can ensure the resilience of networked microgrids under extreme events.
Improved Y-source Inverter

In this paper an improved Y-source inverter (IYSI) topology is proposed based on traditional Y-source inverter. In order to solve the limitations of the traditional Y-source inverter (YSI). The proposed circuit topology not only contains the advantages of the traditional YSI, such as excellent boost voltage gain and reduce cost, but also makes the input current continuous and reduces the capacitor voltage stress, which can reduce the damage of the inrush current to the DC power supply through soft start. By analyzing the topology and working principle of the IYSI, the mathematical formula of the voltage gain is derived in detail. The simulation based on the prototype are performed and the corresponding results verify the rationality and reliability.
An Artificial Intelligence-based Error Correction for Optical Camera Communication

Optical Camera Communication (OCC) is promising to be the candidate for vehicular wireless communication due to its low cost, unlicensed spectrum and safe for human. Our most recent approach is to add region-of-interest (RoI) signaling functionality for cars via either headlight or taillight using hybrid of a low-rate waveform and high-rate waveform, which is already standardized in IEEE 802.15. 7 standard. However, commercialize OCC for vehicular communication still be challenging work. In this paper, we proposed a novel error correction method based on the most trending technology - artificial intelligence (AI) to deal with the various existing issues in the communication channel. The simulation for analyzing the performance of a new method in enhancing the performance of the communication system also be provided
XMM-Newton observations of the merger-remnant galaxies NGC 3921 and 7252

Using the high sensitivity of XMM‐Newton ,w e h ave studied the X-ray emission of the two prototypical late-stage merger remnants, NGC 3921 and 7252. In the case of NGC 7252, this is complemented by archival Chandra data. We investigate the nature of the discrete X-ray point source populations and the hot diffuse gas components in these two galaxies, and compare them in the light of their different merger ages and histories. We detect three candidate ultraluminous X-ray point sources (ULXs) in NGC 3921 and at least six in NGC 7252, for which we have high spatial resolution Chandra data. These have luminosities ranging from ∼1.4 × 10 39 ‐10 40 erg s −1 (for H 0 = 75 km s −1 Mpc −1 ). We expect these ULXs to be high-mass X-ray binaries, associated with the recent star formation in these two galaxies. Extended hot gas is observed in both galaxies. We have sufficient counts in the XMM‐ Newton data to fit two-component hot plasma models to their X-ray spectra, and estimate the X-ray luminosities of the hot diffuse gas components to be 2.75 × 10 40 erg s −1 and 2.09 × 10 40 erg s −1 in NGC 3921 and 7252, respectively. These luminosities are low compared with the luminosities observed in typical mature elliptical galaxies (L X ∼ 10 41 ‐10 42 erg s −1 ), into which these merger remnants are expected to evolve. We do not see evidence that the X-ray haloes of these galaxies are currently being regenerated to the masses and luminosities seen in typical elliptical galaxies. The mass of atomic gas available to fall back into the main bodies of these galaxies and shock-heat to X-ray temperatures is insufficient for this to be the sole halo regeneration mechanism. We conclude that halo regeneration is most likely a long-term (>10 Gyr) process, occurring predominantly via mass loss from evolving stars, in a subsonic outflow stage commencing ∼2 Gyr after the merging event. Ke yw ords: galaxies: evolution ‐ galaxies: individual: NGC 3921 ‐ galaxies: individual: NGC 7252 ‐ X-rays: binaries ‐ X-rays: galaxies ‐ X-rays: ISM.
A supervised classification approach for note tracking in polyphonic piano transcription

Abstract In the field of Automatic Music Transcription, note tracking systems constitute a key process in the overall success of the task as they compute the expected note-level abstraction out of a frame-based pitch activation representation. Despite its relevance, note tracking is most commonly performed using a set of hand-crafted rules adjusted in a manual fashion for the data at issue. In this regard, the present work introduces an approach based on machine learning, and more precisely supervised classification, that aims at automatically inferring such policies for the case of piano music. The idea is to segment each pitch band of a frame-based pitch activation into single instances which are subsequently classified as active or non-active note events. Results using a comprehensive set of supervised classification strategies on the MAPS piano data-set report its competitiveness against other commonly considered strategies for note tracking as well as an improvement of more than in terms of F-measure when compared to the baseline considered for both frame-level and note-level evaluations.
Urban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series

This paper aims at detecting several urban morpho-type classes out of complementary SPOT-6/7 imagery and Sentinel-2 time series and using existing Urban Atlas database. A bottom-up strategy is proposed. It first detects basic urban objects (buildings, roads, vegetation), that are used to calculate multi-scale morphological features. These features are then fed to a Random Forest classifier trained from samples out of Urban Atlas urban classes. Obtained results are optionally merged with a Random Forest classification based on Sentinel-2 time series. Obtained results are promising.
A New Technique for Detecting Android App Clones Using Implicit Intent and Method Information

Detecting repackaged apps is one of the important issues in the Android ecosystem. Many attackers usually reverse engineer a legitimate app, modify or embed malicious codes into the app, repackage and distribute it in the online markets. They also employ code obfuscation techniques to hide app cloning or repackaging. In this paper, we propose a new technique for detecting repackaged Android apps, which is robust to code obfuscation. The technique analyzes the similarity of Android apps based on the method call information of component classes that receive implicit intents. We developed a tool Calldroid that implemented the proposed technique, and evaluated it on apps transformed using well-known obfuscators. The evaluation results showed that the proposed technique can effectively detect repackaged apps.
Deeply Exploiting Long-Term View Dependency for 3D Shape Recognition

Recognition of 3D shapes is a fundamental task in computer vision. In recent years, view-based deep learning has emerged as an effective approach for 3D shape recognition. Most existing view-based methods treat the views of an object as an unordered set, which ignores the dynamic relations among the views, e.g. sequential semantic dependencies. In this paper, modeling the views of an object by a sequence, we aim at exploiting the long-term dependencies among different views for shape recognition, which is done by constructing a sequence-aware view aggregation module based on the bi-directional Long Short-Term Memory network. It is shown that our view aggregation module not only captures the bi-directional dependencies in view sequences, but also enjoys the robustness to circular shifts of input sequences. Incorporating the aggregation module into a standard convolutional network architecture, we develop an effective method for 3D shape classification and retrieval. Our method was evaluated on the ModelNet40/10 and ShapeNetCore55 datasets. The results show the encouraging performance gain from exploiting long-term dependencies in view sequences, as well as the superior performance of our method compared to the existing ones.
Trends in deep convolutional neural Networks architectures: a review

Deep convolutional Neural networks(CNN) has recognized much advances in recent years. Many CNN models have been proposed in few years ago which focused by first on improving accuracy, next minimize number of parameters using squeeze architecture, then CNN model adapted for embedded and mobile systems. But face the huge applications of CNN in computer vision, few papers discuss what is the theory behind building CNN models combining some components. In this paper, we present a survey of recent advances in CNN architecture design taking into account the three periods listed above.
Academic Supervision and Risk Assessment Based on Moodle LMS Data

With the application of Learning Management Systems(LMS) in online learning platforms, there is already a large amount of data to record student operations. Many researchers have done some research based on this type of data and obtained some valid predictors. First, we analyze the theoretical feasibility of this research and combine other established research results to determine our research objectives. Then our data comes from a learning platform based on Moodle LMS in Chongqing. The main research objects include 8 blended courses and 638 students. After some experiments, we established a general multiple linear regression model with interaction effects to analyze the relationship between predictor and final grade. After some experiments, we establish a ordinary multiple linear regression model with interaction effects to analyze the relationship between predictors and final grades. Our model has a good track record in predicting student achievement. Therefore, we hope to establish a system of academic supervision and reminders on the basis of this model to help students better manage their own learning and strengthen the supervision of teachers. Since the data may vary greatly at different levels of education, we need to improve the model in the future to achieve greater portability and support more data information.
Smart Grids and Software Testing Process Models

A Smart Grid (SG) is a Cyber–Physical System (CPS) idealized with the ultimate goal to reach optimal energy efficiency, a key enabler for the Smart City concept. SG reliability needs to be ensured by an in-depth testing process, involving software/ hardware components, network communication, together with quality concerns such as security of the overall infrastructure. While there are many models for modelling/designing SGs (e.g., SGAM), there is no specific testing process model customized for the SGs needs. As such, the aim of this paper is to identify a set of software testing process models developed over the years and investigate their adaptability to SG testing requirements. To reach the goal, we categorize SG requirements from related works with the help of a concept map. Afterwards, the most popular testing process models from literature are discussed in the context of the identified requirements. From a list of 17 main testing models, six testing models (TPI Next, TMMi, Test SPICE, TIM, ISO/IEC/IEEE 29119, and MTPF) are considered as potentially adaptable to the SG domain.
Fast Beam Tracking Discontinuous Reception for D2D-Based UAV MmWave Communication

The device-to-device (D2D) communication integrated with the millimeter-wave (mmWave) air interface will play a significant role in the future mobile communication in terms of high transmission rate and deployment flexibility. Meanwhile, unmanned aerial vehicles (UAVs) are promoted as D2D nodes to support D2D-enabled networks for broad coverage. In this paper, we provide a new D2D-based UAV mmWave communication framework, in which the UAVs are mobile nodes and have stringent energy constraints. Therefore, the energy-efficient technology which can turn on the UAVs’ radio only when necessary and overcome the resulting easily-occurred beam misalignments due to the radio OFF periods and the aircrafts’ agile mobility is required urgently. To tackle this issue, we propose a novel fast beam tracking discontinuous reception (FBT-DRX) mechanism. A new Semi-Markov model is designed to analytically investigate the statistical property of the proposed scheme. Different from many published works on DRX with beamforming, we take the impact of UAV movements into account when deriving the exact closed-form expressions of sleep ratio and average packet delay. Finally, the numerical results are supplied to evaluate the effects of different key parameters on the system performance, and demonstrate that the FBT-DRX mechanism can perform well both in power saving and packet delay in the considered framework.
Optimal design of a multichannel fiber Bragg grating by using the DC sampling method

A novel and flexible method for producing a multichannel filter from a single-channel fiber Bragg grating (FBG) is proposed and numerically demonstrated. The proposed method enables us to produce a multichannel filter from a single channel FBG just by using the direct UV exposure without the need of the expense and the complex phase mask. Moreover, the proposed method provides much more flexibilities to design any other new kinds of fiber gratings than ever before.
Utilization and Comparision of Convolutional Neural Networks in Malware Recognition

Advances in Industry 4.0, IoT and mobile systems have led to an increase in the number of malware threats that target these systems. The research shows that classification via the use of computer vision and machine learning methods over byte-level images extracted from malware files could be an effective static solution. In this study, in order to detect malware, we have employed various contemporary convolutional neural networks (Resnet, Inception, DenseNet, VGG, AlexNet) that have proven success in image classification problem and compared their predictive performance along with duration of model production and inference. In addition, a novel malware data set involving 8750 training and 3644 test instances over 25 different classes was proposed and used. As a result of the experiments carried out with 3-channel (RGB) images obtained, the highest success in terms of accuracy was determined as 97.48% by using DenseNet networks.
A Scalable 3-D Printed Biological Assembly Technology

This paper presents a scalable biomolecular assembly on three-dimensional (3-D) electrodes enabled by electro-bioprinting (EBP). A highly uniform assembly of proteins - cysteine-modified Tobacco mosaic virus (TMV1cys) and glucose oxidase (GOx) - on micropillar array (μPA) electrodes is achieved, enabled by selective and sequential EBP of biological inks (biomolecule-containing aqueous media). The printing and assembly of the biomolecules (termed "print-assembly") on the 3-D surfaces is confirmed via SEM showing nanoscale "rod-like" structures corresponding to TMV1cys/GOx coating on EBP-processed surfaces. Colorimetric assays confirmed biological/enzymatic activity, where TMV1cys/GOx functionalization on μPAs significantly increase enzymatic reaction density compared to functionalized planar surfaces. Specifically, the reaction density per device foot-print has been enhanced by factors of 4 and 5.5, closely corresponding to the surface area enhancement factors derived from respective μPA geometries. This work demonstrates a highly scalable, efficient, and customizable biomolecular assembly method, providing numerous opportunities in developing biomaterial-integrated devices including biochemical sensors, bioenergy harvesters, and drug-delivery devices.
SIMT: A holistic framework for embedding simulation into the health care systems.

Simulation and Modelling (S&M) have been proven as very valuable tools in the health care sector. In recent years, the sector has experienced a rapid increase in applications. However, it appears that health care organisations have failed to sustain the use of these powerful techniques. In this research, an extensive literature review is carried out to identify the main challenges of the use of health care simulation and the underlying barriers of implementing S&M in the sector. In order to address these issues, it identifies the need to fully embed S&M into the sector through a systematic approach. However, the literature in this subject area has not provided such a holistic approach to the use of simulation.With the view to embed these techniques in health care decision making processes, this research develops a new framework, known as SIMulation Thinking (SIMT), to overcome the identified challenges and barriers. SIMT includes five key components: infrastructure, management, culture change, methodology and modelling. Whilst the SIMT framework presents the important elements that need to be considered to make S&M mainstream tools, this research also presents an implementation framework which transforms SIMT into a practical and applicable approach to embed S&M in health care organisations. The implementation framework includes two main stages: planning stage and action stage. Questionnaire and case study approach are conducted to validate the usefulness and importance of the SIMT components and the proposed implementation framework. The questionnaire is used to understand how the selected group of experts consider the SIMT components and the planning stage of the implementation framework as a valuable guideline. To validate the action stage of the implementation framework, this research uses the case study approach which introduces the proposed methodologies and modelling best practices into a local hospital. The feedback received from the hospital is used to evaluate the usefulness and practicable of the proposed approach.