Miracle-dz/UniFAR-FTUs huggingface dataset:
ADASS: Anti-Drone Audio Surveillance Sentinel via Embedded Machine Learning

The proliferation of drones at a consumer level enables malicious users to equip them with recording devices to snoop sensitive information. Drones may also be used to carry dangerous objects, such as explosives. Protecting critical infrastructures from the security and safety threats imposed by drones is therefore of uttermost importance to prevent potentially hazardous attacks. Internet of Things (IoT) technology represents an enabler of drone detection solutions thanks to its deployability over large areas which might be difficult to monitor otherwise. However, state-of-the-art drone detection technology is based on deep learning, which requires intensive resources and cannot be easily deployed in embedded devices. In this paper, we propose Anti-Drone Audio Surveillance Sentinel (ADASS), the first noise-based drone detection solution that can be implemented in IoT devices. The overall model is composed of a preprocessing phase that emphasizes the frequency of noise emitted by flying drones and a successive classification via a compressed Convolutional Neural Network. To validate ADASS, we implement it on an embedded system and compare its performance to that obtained with a non-compressed model. Our results show that the detection accuracy of ADASS is the same as obtained with a full non-compressed model.
A RTC-DG Method for Electrical-Thermal-Stress Analysis of 3-D Packages of Integrated Circuits with Micro-Fluidic Cooling

In this work, a Robin transmission condition (RTC) augmented discontinuous Galerkin (DG) method (RTC-DG) is proposed for electrical-thermal-stress analysis of 3-D integrated circuits with micro-fluidic cooling. Unlike the conventional DG method, the proposed DG scheme is directly applied to discretize the high-order governing partial-differential equations (PDEs), and the resulted numerical flux is used for the information communication among neighboring subdomains. To strictly confine the new variables introduced by the numerical flux, a firstorder RTC is derived at the interface of adjacent subdomains, which serves as an auxiliary equation in helping solving the new variables. In such a way, it only needs to solve the newly introduced variables at the interface of subdomains, thus the number of degrees of freedoms (DoFs) is significantly reduced in comparison with the convential DG method. In addition, to keep the domain decomposition capability of the DG method for steady-state issues, a finite-element-tearing-and-interconnecting (FETI) like procedure is resorted to decomposed the globally-coupled matrix equation into a number of small matrix equations that can be parallely solved by a direct solver. To validate the accuracy and efficiency of proposed algorithm, the proposed RTC-DG method is applied to electrical-thermal-stress simulation for a 3-D package with micro-fluidic channel.
High Gain SEPIC-Zeta Converter for Energy Management of Wind-PMSG Based Water Pumping System

Recent studies have shown that researchers are very fascinated with variable speed Permanent Magnet Synchronous Generator (PMSG) based Wind Energy Conversion Systems (WECS). This study addresses a number of PMSG related topics, including topologies using adaptive network based fuzzy inference system maximum power point tracking (ANFIS MPPT) controlled methods. According to the simulation findings, both an artificial neural network (ANN) and ANFIS approaches work well for determining wind rapidity and for monitoring the supreme power point and related rotor speed. The suggested single ended primary inductance converter (SEPIC) zeta converter with ANN controller enhances an energy from battery to be applied to the motor at times of power shortage from WECS. The simulation and experimental findings demonstrate the steady state performance under a range of wind speeds and prove an efficiency of the developed controllers utilizing the parameters that were measured.
Estimating road friction from kinematic summaries at curved sections

We present a system for estimating the friction of the pavement surface at any curved road section, by arriving at a consensus estimate, based on data from vehicles that have recently passed through that section. This estimate can help following vehicles. To keep costs down, we depend only on standard automotive sensors, such as the IMU, and sensors for the steering angle and wheel speeds. Our system’s workflow consists of: (i) processing measurements from vehicular sensors, (ii) transmitting short kinematic summaries from vehicles to a road side unit (RSU), using V2X communication, and (iii) estimating the friction coefficients, by running a machine learning regressor at the RSU, on summaries from individual vehicles, and then combining several such estimates.We study two key questions: (i) should each individual road section have a local friction coefficient regressor, or can we use a global regressor that covers all the possible road sections? and (ii) how accurate are the resulting regressor estimates? We test the performance of design variations of our system, using simulations of normal driving scenarios at curved road sections, using the commercial package Dyna4. We consider a single vehicle type with varying levels of tyre wear, and a range of road friction coefficients. We find that: (a) only a marginal loss of accuracy is incurred in using a global regressor as compared to local regressors, (b) the consensus estimate at the RSU has a worst case error of about ten percent, if the combination is based on at least fifty recently passed vehicles, and (c) our regressors have root mean square (RMS) errors that are less than five percent. The RMS error rate of our system is half as that of a commercial friction estimation service [7]. But when tested with data from extreme driving manoeuvres that were unseen in the training data, our regressor performs an order of magnitude worse than on data from normal driving runs on curved road sections. Still our regressor’s RMS errors on such test data are no worse than the state of the art Artificial Neural Network regressors [18], [30].
A Bi-Layer Optimization Method for Electric-Heat System Based on Multi-Energy Flexibility

As renewable energy becomes increasingly integrated in the grid, various uncertainties bring serious challenges to the operation of regional electric-heat systems. In order to expand the capacity of renewable energy, considering the time coupling characteristics of multi-energy flexibility, a bi-layer optimization method of the electric-heat integration system was proposed. Firstly, the energy conversion model of multi-energy device and energy work are established considering the thermal inertia of heat network. Then, considering the wind uncertainty, we established the bi-layer flexible model of electric-heat operation using analytical target cascading (ATC) theory. Finally, an IEEE 33-bus distribution network and a 6-node district heat network are taken as examples to verify the importance of considering multi-energy flexibility for system operation.
A Hybrid Approach for Leaf Classification Using Machine Learning and Deep Learning

Leaf classification is a crucial task for plant identification and disease diagnosis. This study suggests a unique method that combines deep learning and machine learning algorithms for leaf classification. The proposed method involves a novel combination that employs Residual Network (ResNet50) for extraction of features, Principal Component Analysis (PCA) for reducing the dimensions of feature vector and Light gradient boosting machine (LightGBM) to perform classification. The proposed method is evaluated on a 40-class dataset of leaves from the UCI repository and achieve an impressive accuracy of 95.70%. Furthermore, we compare the time complexity of this method with other boosting models such as XGBoost and CatBoost and investigated the role of component selection in the classification task. The proposed approach has the capability to address various classification problems within the domains of computer - vision and machine learning. Moreover, it holds promise for enhancing the precision of leaf identification in plant recognition tasks.
Cloud Classification via Satellite Images using Residual Network

Identifying cloud shapes and predicting the weather based on these shapes is an important phenomenon for many people and businesses. Making a suitable definition for the clouds obtained from satellite images and revealing the cloud shapes will make things easier in weather forecasting. Although the prediction of cloud shapes using artificial intelligence has become widespread recently, the problem has still not been fully resolved due to complex clouds structure. This study addresses a problem that involves multi-label features in a training dataset of 5546 samples using a deep learning method. Clouds have four different types: fish, flowers, sugar, and gravel. In this multi-label classification pattern Resnet50 model, we studied images with 256x256 pixels, using and comparing two different loss functions, namely Binary Cross-Entropy and Weighted Binary Cross-Entropy. With the applied method, a success score of 69.1% was achieved and better results were obtained than the methods at the state-of-the-art level.
Image Annotation Optimization Method of Civil Engineering Supervision Based on Association Rule Mining Algorithm

The rapid development of civil engineering, leading to the vigorous development of civil engineering supervision industry, and civil engineering supervision, as an important mechanism to ensure that civil engineering to meet the relevant laws and regulations, its supervision data plays a vital role. Therefore, it is necessary to strengthen the importance of civil engineering supervision, and on this basis to improve the quality of the project. Because of its intuitive image and easy to interpret, efficient management and retrieval methods of civil engineering supervision image information have gradually become a research hotspot. This paper mainly discusses the problems existing in the image processing of civil engineering supervision, and puts forward the corresponding solutions, so as to provide some reference for the subsequent engineering application. At present, the requirements of high-level semantic expression of images are constantly improving, but the traditional solution is to manually annotate images, which is not only time-consuming, laborious and easy to cause ambiguity. Therefore, this paper studies the automatic annotation of civil engineering supervision images. In order to obtain the regional features of the image, the Mean Shift segmentation (MS) algorithm is proposed to segment the image. In order to improve the accuracy of automatic image annotation, the automatic annotation of civil engineering supervision image based on association rules is proposed, and the semantic extraction of civil engineering supervision image is carried out by Apriori algorithm. The experimental results show that the recall rate and precision rate of the proposed method are 85% and 91% respectively, and the precision rate is increased by 116% and the recall rate is increased by 117% compared with the CBA method. Compared with PLSA algorithm, the precision is increased by 15%, and the recall is increased by 13%. It also has a good effect on the image retrieval of civil engineering supervision.
Fast Analysis of Array Structure Using Reduced Basis Method

In order to quickly and accurately calculate the electromagnetic wideband characteristics of the array structure, this paper combines the reduced basis method (RBM) with the tangential equivalent principle algorithm (T-EPA). T -EPA is applied to solve the integral equation of the array structure, which transfers the problem on the target to the equivalence surface, thus reducing the total number of unknowns of the array and calculating the RCS of the array structure quickly, but it cannot perform the wideband processing. The RBM can accelerate the frequency sweep, including offline and online processes. The offline process constructs the reduced basis set by solving the solution vector corresponding to the parameter set, reduces the dimensionality of the impedance matrix, and speeds up the iterative solution of the linear system of equations in the online process. The numerical result shows that the computation time and memory are greatly reduced.
Game Statistics Forecast Based on Sports Using Machine Learning

Cricket is a popular sport worldwide, with millions of fans and players. One of the critical aspects of cricket is analyzing player statistics to forecast future performances. In order to examine player performance data and forecast future results, machine learning (ML) techniques have been used in a variety of fields, including sports. In this study, we present a machine-learning model that predicts cricket player performance using data from actual cricket matches. We use a dataset collected from international cricket matches and analyze various statistical features, including batting and bowling averages, strike rates, and fielding performance. We use this data to train a machine-learning model that predicts a player's future performance. We evaluate our model's performance by comparing its predictions to actual gameplay data and show that our model achieves high accuracy in predicting player performance. Our proposed model can be used to provide insights into a player's performance and help players improve their skills. It can also be used by cricket coaches and selectors to evaluate player skills and select teams. Overall, our work demonstrates the potential of machine learning techniques in predicting player performance in cricket and can have significant implications for the game's future.
A Modified Three-Phase Synchronous Isothermal Relaxation Current Detection System Based on Independent Measurement Loop Method

As a nondestructive testing technique, the isothermal relaxation current (IRC) method has been applied to assess the condition of power cable insulation. In order to shorten the detection time in the field and improve the measurement accuracy, a modified measurement circuit based on an independent measurement loop was proposed to discharge the interference from the high-voltage insulated wires. Based on this circuit, the three-phase synchronous IRC measurement system was established. Furthermore, the independence and consistency of the three-phase current measurement loop were verified, which could realize the synchronous acquisition of the three-phase relaxation current. Finally, the three-phase IRC test was carried out to evaluate the aging state of the sample in service. The results showed that the aging state of each phase for the same cable might be quite different after long service.
Attribution of a Record-Breaking Heatwave Event in Summer 2017 over the Yangtze River Delta

© 2018 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy. INTRODUCTION. During 11 to 28 July 2017, the Yangtze River Delta experienced a record-breaking heatwave. In particular, the Xujiahui weather station in Shanghai endured the worst heatwave in at least 145 years, with a new record of 40.9°C on 21 July 2017. The extreme heatwave left four people dead and many elderly people and children suffering from heatstroke. Generally, the western Pacific subtropical high (WPSH) is regarded as the key anticyclone system for the occurrence of extreme heatwave events in eastern China (Yang and Li 2005; Ding et al. 2010; Wang et al. 2014; Luo and Lau 2017; Wang et al. 2016; Li and Sun 2018). The WPSH variability is modulated by tropical forcing from sea surface temperature anomalies over the equatorial Pacific Ocean (e.g., ENSO) (Tao and Xu 1962; Wang et al. 2000; He et al. 2015a; Chen and Zhou 2017) and over the tropical Indian Ocean (Yang et al. 2007; Du et al. 2009; Xie et al. 2009). During the decaying El Niño, the lasting warming over the tropical Indian Ocean can trigger an anomalous WPSH by exciting Kelvin waves eastward (Wu et al. 2009; Wu et al. 2010), resulting in a westward extension of the WPSH (Huang and Wu 1989; Zhou et al. 2009). The west-extending WPSH induces an anomalous southwesterly f low to transport more moisture to the northern regions and then dry the atmosphere over the Yangtze River Delta (Chen and Lu 2015). This process results in decreased rainfall and increased surface incident solar radiance (Hu et al. 2012, 2013; Liu et al. 2015; Chen et al. 2016; Freychet et al. 2017), which introduce extreme heatwaves in the Yangtze River Delta, such as the one in 2013 (Li et al. 2015). Other known factors that increase the risk of heatwaves over the Yangtze River Delta include anthropogenic global warming (Yang and Li 2005; Sun et al. 2014; Zhou et al. 2014), which meanwhile influences the variations of the WPSH by tropical oceanic warming (Zhou et al. 2009; He and Zhou 2015; He et al. 2015b), and the urban heat island effect due to local urbanization around the weather stations, which can exacerbate the heatwave by enhancing sensible heat f lux and longwave radiation from the surface during the daytime (Tan et al. 2008; Sun et al. 2016; J. Wang et al. 2017). Using the heatwave magnitude index (HWMI) and July–August mean temperature to quantify the 2017 heatwave, this study therefore tries to answer two questions: 1) How extreme is the heatwave over the Yangtze River Delta in a historical context? 2) What are the relative impacts of the WPSH, global warming, and the urban heat island on the heatwave?
Gait Recognition Using EigenfeetNet

Due to a growing emphasis on personal privacy and non-intrusive methods of person authentication, researchers have sought to evaluate new and emerging biometric technologies. One promising approach is gait recognition using pressure-sensitive flooring (or footstep recognition) which strives to verify a person's identity using the patterns of pressures exerted on the floor while they walk. In this study, we describe the development of a solution for person verification based on a fused feature selection process inspired by the popular PCA-based eigenfaces approach and a deep learning framework. Dynamic three-dimensional (3D) foot pressure patterns recording during walking were first reduced to ten different 2D pre-feature images. Using the eigenfeet extracted from the peak pressure, a nearest neighbour balanced accuracy (BACC) of 91.1% was obtained based on a single footstep when verifying subjects. Selecting discriminatory eigenfeet, using a minimum-redundancy-maximum-relevance (mRMR), further improved the performance (93.4% BACC), and when fused with a convolutional neural network (CNN) architecture into a stacking PCA network (PCANet+), the maximum verification performance of 96.2% BACC was found. These results show that the proposed selective EigenfeetNet method (i.e., peak pressure, PCANet+, and mRMR) provides a promising platform for the further development of floor sensor-based gait recognition for person verification.
Communication Networks with Multiple SDN Controllers

Software-defined networks are slowly but surely coming into production. Currently, there are several different SDN architectures. As SDN solutions continue to evolve, there is room to improve current solutions in networks with multiple SDN controllers. One of the researched aspects of the SDN network is the deployment of multiple controllers to improve reliability, load balancing, and efficiency. Each controller will manage its SDN domain. In this paper, we proposed an efficient SDN architecture based on a flat multicontroller multidomain network that provides efficient load balancing in the network. We demonstrated and verified its performance via simulations in Mininet environment with Ryu controllers.
A Neural Network Model for N-Type Metal-Ferroelectric-Insulator-Semiconductor Ferroelectric Tunnel Junction

In this paper, we develop an artificial neural network (ANN) model to predict the tunneling current of ferroelectric tunnel junction (FTJ) based on the metal-ferroelectric-insulator-semiconductor (MFIS) stacks using training data obtained from numerical simulation, which calculates tunneling current by the Wentzel-Kramers-Brillouin (WKB) method with the band profile obtained by self-consistently solving Poisson's equations and the drift-diffusion transport equations with a tunneling-induced carrier generation rate. The proposed ANN model can greatly reduce simulation cost while maintaining high accuracy, showing good agreement with both numerical simulation results and experiment results.
A Novel Model for Sea Surface Loss Transport in Complex Environments

The current prediction model for sea surface signal transmission is relatively simple to establish and has significant errors compared to the actual situation. This article establishes a novel sea transmission model based on the dual-path model and the modified Longley-Rice model, and applies the two models to different transmission distances. The relationship between the received signal power of ocean buoy antenna and distance was simulated by taking into account the absorption of water vapor on the sea surface, the influence of waves, and sea winds. The simulation results show that this model can be used to analyze the effects of complex sea surface environments and has some significance for sea surface communication.
A Few-shot learning SAR image generative model for automatic target recognition

A new Synthetic aperture radar (SAR) image generative model is proposed for efficiently solving the recognition tasks in few-shot learning. In order to further improve SAR image quality and variety, the advantages of the two types of traditional generative models are combined, parts of their structure are used to stitch together into a novel generative model. An intuitive representation is the series connection between Variational Auto-encoder (VAE) and discriminator (Dis), the former is used to generate images and the latter is used to judge the generated images and the real images. Numerical results show that the image quality and variety can be improved significantly by the proposed VAE-Dis method with encouraging accuracy in the identification tasks.
Allometric biomass estimators for aspen-dominated ecosystems in the upper Great Lakes. Forest Service research paper

The authors recently described the climate, geology, soils, and the biotic structure and dynamics of four contrasting ecosystems dominated by quaking and bigtooth aspen (Populus tremuloides and P. grandidentata). Other papers describe how those ecosystems responded to perturbation. Common to most of those papers were biomass estimates for the tree and shrub layers. The authors derived the estimators from weight and dimensional analysis of a subsample of stems measured on sample plots. They found much variability among sites that could not be adequately explained by stand or soils data. These equations should be useful in estimating woody plant components of similar forests on upland soils in the Upper Great Lakes region.