Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Demand Forecasting Approach Based on Generic Cloud Workload Model

Ubiquitous computing environment generates massive amounts of data exploiting any kind of device, in any location and in any format. IaaS resources which are distributed around the world and can be rented on demand become popular to store and process those data. Therefore, it becomes imperative that an effective methodology is developed to learn what IaaS resources are required, how many resources and for how long are needed. However, the heterogeneity and hybridity of data and applications, and the variation of workload make resource forecasting be a big challenge. In this paper, we purpose a unified resource demand forecasting approach based on the generic application model and generic workload variation model. The approach suits for different hybrid applications, various resources and diverse time-varying workload patterns. Taking input from parameterized cloud applications, workload variation model and resource profile, the corresponding resources demands during any time interval can be deduced as output. Experiments are conducted taking MapReduce application WordCount as the example. Our approach is evaluated by contrasting the logged data against forecasting data, and our results show that the average forecast accuracy rate can reach 90%.
Disturbance Predictive Control of the LC Coupled Voltage Source Inverter

The LC coupled voltage source inverters (LC-VSIs) are used to compensate harmonic distorted load currents as the hybrid active harmonic filters. Due to the series capacitor in the coupling impedance, the inverter rating is reduced when compared to traditional topologies. However, the plant has resonant characteristics and excessive currents may flow through the arrangement when the point of common coupling voltage is distorted. The main contribution of this paper is to present a predictive observer with feedforward of the estimated disturbance in order to mitigate this problem. Current tracking is achieved using a linear quadratic regulator with state-feedback and input integrator. The modelling of the plant and the control loop design in the discrete-time domain are presented. Simulations and experimental measurements on a prototype show good agreement thus validating the proposed control strategy; evidencing good disturbance rejection and reference tracking.
Electrospinning Application on Fabrication of PMMA Nanofibers Membranes for Electrochemical Sensors

The present paper focus on research carried out to optimize manufacturing of the electrochemical sensors electrodes based on nanostructured hybrid composite interface at nano and micro-scale. To obtain the nanofibers the electrospinning method was considered and several experimental research were carried out to extract the effect of solution parameters and electrospinning parameters on the characteristics of nanofibers membranes to be used later as support for ceramic materials that are employed for the construction of ceramic nanotubes. The characteristics of the obtained nanofibers membranes were analyzed using 1000x microscope. Regarding the experimental study different PMMA solution characterized by wt=5% and wt= 10% concentrations, were used for different high voltage values applied between needle and collector drum and different distances between the needle and metallic support surface. Different experimental setups were carried out followed by analysis and comparisons between the nanofibers obtained by electrospinning and also about ZnO deposition on the nanofibers level.
Development of a new recline mechanism in order to reduce the “whiplash” effect using a virtual model

The purpose of this paper is to design a mechanism mounted on the occupant’s seat to control the recline of the backseat in the case of rear-end collisions to reduce the effects of whiplash upon the occupant’s neck and head using a virtual model of an occupant and seat. The design of the system is modeled in SolidWorks and simulated in this software by using the Adams physics model included in SolidWorks. The system will function similarly to a real sled; it is composed of a car’s seat, a multibody occupant and a surface along which the seat may slide. This system will be validated by comparing two real sled tests and a seat normally functioning. Once validated, the mechanism is enabled to make simulations which are conducted to analyze the differences in kinematics of the occupant’s neck and the various key parameters such as head accelerations, contact forces and T1 vertebrae acceleration. The multibody occupant is composed of multiple bodies inter-connected with joints and it will simulate a real occupant. As for the evaluation of injury potential for the neck, the neck injury criteria (NIC) are calculated for the comparisons of the two situations: when the backseat has normal rigidity and when the recline mechanism is activated. It was observed that, by using this recline mechanism, the key parameters were reduced. This paper presents the new developed mechanism with the obtained parameter reductions.
Stereo Visual-Inertial SLAM With Points and Lines

Visual–inertial SLAM systems achieve highly accurate estimation of camera motion and 3-D representation of the environment. Most of the existing methods rely on points by feature matching or direct image alignment using photo-consistency constraints. The performance of these methods usually decreases when facing low textured environments. In addition, lines are also very common in man-made environments and provide geometrical structure information of the environment. In this paper, we increase the robustness of visual–inertial SLAM system to handle these situations by using both points and lines. Our method, implemented based on ORB-SLAM2, makes the combination of points, lines, and IMU measurements in an effective way by selecting keyframes very carefully and handling the outlier lines efficiently. The cost function of bundle adjustment is formed by point, line reprojection errors, and IMU residual errors. We derive the Jacobian matrices of line reprojection errors with respect to the 3-D endpoints of line segments and camera motion. Loop closure detection is decided by both point and line features using the bag-of-words approach. Our method is evaluated on the public EuRoc dataset and compared with the state-of-the-art visual–inertial fusion methods. Experimental results show that our method achieves the highest accuracy on most of testing sequences, especially in some challengeable situations such as low textured and illumination changing environments.
Analysing the Sentiment Expressed by Political Audiences on Twitter: The Case of the 2017 UK General Election

A significant amount of research on the intersection of sentiment analysis and social media platforms has been published in the past few years. While previous studies have focused on methods to identify the polarity of online posts, little has been done in terms of using the impact of such posts to enhance the discovery and description of trends in real time. Here, we present a tool for the retrieval and analysis of microblogging posts in real time. We have gathered a large sample of tweets related to the 2017 UK General Election. We introduce a novel classification of the polarity of sentiments, considering the correlation between words, events and sentiments.
Autonomic Nervous Pattern of Motion Interference in Real-Time Anxiety Detection

Anxiety is a kind of extremely negative emotion, and long-term anxiety is the cause of many serious physical and mental diseases. In daily life, real-time and accurate detection of anxiety state is conducive to block the physical and mental hazards of anxiety timely. Anxiety detection based on autonomic nervous patterns has achieved high-detection precision in certain scenes. However, motion interferes with anxiety detection in arbitrary scenes of daily life, leading to the false report of anxiety. This paper designed four different intensities of motions to analyze the autonomic nervous activity of typical motion states, found out the motion states with similar autonomic nervous patterns to the anxiety status, and identified the motion interference that would cause false detection of anxiety state. A multi-modal real-time anxiety detection system was built in this paper. The ability of the above system to detect motion interference and exclude false anxiety status was validated both in the laboratory and in real life scenarios.
Model-Based Design and Real-Time Testing of Commercial More-Electric Aircraft Power Systems

This paper focuses on model-based design and real-time testing of a commercial more-electric aircraft power system. Important design considerations are first discussed in detail and the models for system design and testing are presented. The paper also elaborates on a distributed, real-time simulation platform for power system testing and evaluation. This platform is based upon FPGA (Field Programmable Gate Array) that handles on-chip computation in real time with latency of 32 FPGA clock cycles (potentially within 200 nanoseconds). Also, high-fidelity, physics-based models can capture a variety of electromagnetic transients, electromechanical dynamics, aerodynamics, controls, and thermal dynamics at time scales ranging from microseconds to seconds. By using this real-time platform, a representative commercial more-electric aircraft power system was designed and studied to verify the power adequacy, transient performance, and power quality. Simulation results show that starting two large induction motors simultaneously may drive the bus voltage out of the limits (upper and lower limits); however, separating the starting of 2 motors by 100 milliseconds can help to maintain the voltage within the limits. The voltage variation due to inrush currents under motor starting increases with the generator frequency. Studies about unbalanced loading conditions show that there exists an approximate linear relationship between the voltage unbalance (%) and phase power unbalance ratio. As the generator frequency increases, the voltage unbalance (%) increases when the power unbalance remains the same. The voltage unbalance under a capacitive load seems to be lower than that under a resistive load, while under an inductive load is looks higher than that under a resistive load. These studies demonstrate that through proper machine design and control, the generator is capable of maintaining the bus voltage due to direct-start inrush currents of induction motors driving large pumps within the required ranges and handle the unbalanced to a certain extent. In these studies, real-time simulation results match offline simulation results and experimental data about the generator and motors very well.
Meteorological Parameters Analysis for hourly Forecast of Electricity Generation by Photovoltaic Power Station on the Day Ahead

The paper presents an analysis of the mechanisms of functioning of the new model of the wholesale electricity market. According to this, the work of stations based on the using renewable energy sources (RSE), in particular photovoltaic power stations (PPS), should take place on the basis of the announced hourly generating graph by the owner of the station one day ahead. In order to ensure the accuracy of the predicted graph, a study was carried out on the determination of meteorological parameters that most affected the generation of electricity at photovoltaic power plants. Results of cluster and regression analysis were conducted on the basis of meteorological data, measured at the station located in the Vinnitsa region.
Resonant pumping of polaritonic SSH chains

An optical-lattice realization of a Su-Schrieffer-Heeger chain is theoretically investigated. The topological state is controlled by the relative phase of the lasers constructing the lattice. The hopping amplitudes are calculated within the harmonic approximation to the optical potential in the vicinity of its minima. Finally, we examine polaritons loaded into adiabatically shaken optical lattices with damping and pumping and demonstrate that, in the case of resonant pumping, the system manifests different quantitative and qualitative behaviour for topologically trivial and non-trivial phases.
An Efficient Approach for Measurement-Based Composite Load Modeling

Measurement-based load modeling, especially in the presence of new loads such as power electronics-interfaced loads and electric vehicles with fast dynamics, requires fast-converging algorithms that provide the model parameters with high reliability. In the current practice, all or only a subset of the parameters of an aggregated load model are estimated using iterative optimization algorithms. Thus, the identification problem either has a high dimension, which leads to a large variance for the estimated parameters, or does not include a subset of the parameters with low sensitivity. In this paper, an efficient approach for the estimation of the composite load model parameters is proposed that addresses these issues. This method partitions the parameters into two subsets; one that appears nonlinearly in the model output, and a second set that affects the outputs linearly. Then, the optimization is performed only with respect to the nonlinear set, with the linear parameters treated as explicit functions of the nonlinear ones. This approach effectively reduces the dimension of the search space since it only includes the nonlinear parameters in the optimization, and also includes the linear parameters by computing them using linear regression at each iteration. These features lead to a much faster convergence while all of the composite load model parameters are estimated reliably. Experimental and simulation data are presented to demonstrate the performance of the proposed method.
Remote Sensing Image Registration with Multiple Features and Parameter Optimization

Remote sensing image registration plays an essential role in agriculture and military fields, it has been widely used in agriculture and urban land use planning, military damage assessment, and environmental monitoring, etc. In this paper, we propose an image registration method based on multiple features and parameter optimization. Which has a contribution, the parameter $L$ is optimized in the local feature. And we optimize the parameter in different registration modes to promote the registration accurate. We evaluated the performances of the proposed method by a series of remote sensing images from Google Earth, and compared with five state-of-the-art methods. Where our method shows the best alignments in most scenarios.
Overdue Prediction of Bank Loans Based on LSTM-SVM

In the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, DNN, are not benefit from the data growth. This article is based on the work of Overdue Prediction of Bank Loans Based on Deep Neural Network. And we propose to analyze the dynamic behavior of users by LSTM algorithm, and use the SVM algorithm to analyze the user's static data to solve the current prediction problems. This article uses users' basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. These static data are the basic input for SVM. For LSTM model, we extract user's recent transaction type from browsing behavior as input to LSTM, to predict the probability of users' overdue behavior. Finally, we calculate the average of the two algorithms as the final result. From the experimental results, this LSTM-SVM model shows a great improvement than traditional algorithms.
Multispectral Bathymetry via Linear Unmixing of the Benthic Reflectance

Bathymetry is important to shallow coral reef management. Remote sensing is one of the most important techniques for mapping bathymetry. For instance, multispectral remote sensing images have been widely used to map bathymetry of coral reefs. These physically based methods, which are able to retrieve bathymetry, water column inherent optical properties (IOPs), and benthic reflectance simultaneously, generally can achieve good bathymetry results. However, there are strong limitations when facing the scenarios of mixing benthic reflectance for bathymetry with multispectral images. In this study, aiming at handling the mixing phenomenon on the use of multispectral remote sensing images for mapping bathymetry, we propose a new unmixing-based method, namely unmixing-based multispectral optimization process exemplar method (UMOPE). The new method incorporates endmember variability with a linear combination of three fixed endmembers and the relaxation of the sum-to-one constrained. UMOPE was validated with two existing methods with in situ data and a WorldView-2 image in the South China Sea. Results from in situ data show that the proposed method performs the best, with the smallest absolute root mean square error ${\rm{(RMSE)}}$(2.26 m) and the best agreement ( $R^{2}= {\text{0.91}}$) between the measured and estimated water depth. Moreover, results from WV-2 imagery demonstrate the superior performance for the UMOPE at depth range from about 9 to 26 m. Furthermore, since the errors from IOPs can propagate to bathymetry, we further take this issue into account by analyzing the rule of influence of bottom on IOPs. Finally, the result of benthic classification map from UMOPE is shown.
Energy-Aware Automatic Tuning of Many-Core Platform via Gradient Descent

Even though attaining high performance has been the user's pursuit traditionally, in the many-core era, the emphasis has shifted towards controlling the power and energy consumption, so as to maintain a satisfying performance while consuming an acceptable amount of energy. This paper describes an auto-tuning algorithm for the energy efficiency optimization of many-core platform, in this case, a Graphic Processing Unit (GPU). We employed gradient descent algorithm as the basis for this optimization. Metrics such as energy and energy delay product (EDP) are examined using programs representing different types of workloads such as sequential, parallel and hybrid. Based on the experimental results, our method achieves the level of savings over 15% in terms of energy consumption when compared with the default on-board governors that also adjust the voltage and frequency of the GPU. Our approach shows an advantage when optimizing towards EDP as well. This shows the effectiveness of our proposed approach.
Novel genotype-phenotype and MRI correlations in a large cohort of patients with SPG7 mutations

Objective To clinically, genetically, and radiologically characterize a large cohort of SPG7 patients. Methods We used data from next-generation sequencing panels for ataxias and hereditary spastic paraplegia to identify a characteristic phenotype that helped direct genetic testing for variations in SPG7. We analyzed MRI. We reviewed all published SPG7 mutations for correlations. Results We identified 42 cases with biallelic SPG7 mutations, including 7 novel mutations, including a large multi-exon deletion, representing one of the largest cohorts so far described. We identified a characteristic phenotype comprising cerebellar ataxia with prominent cerebellar dysarthria, mild lower limb spasticity, and a waddling gait, predominantly from a cohort of idiopathic ataxia. We report a rare brain MRI finding of dentate nucleus hyperintensity on T2 sequences with SPG7 mutations. We confirm that the c.1529C>T allele is frequently present in patients with long-standing British ancestry. Based on the findings of the present study and existing literature, we confirm that patients with homozygous mutations involving the M41 peptidase domain of SPG7 have a younger age at onset compared to individuals with mutations elsewhere in the gene (14 years difference, p < 0.034), whereas c.1529C>T compound heterozygous mutations are associated with a younger age at onset compared to homozygous cases (5.4 years difference, p < 0.022). Conclusions Mutant SPG7 is common in sporadic ataxia. In patients with British ancestry, c.1529C>T allele represents the most frequent mutation. SPG7 mutations can be clinically predicted by the characteristic hybrid spastic-ataxic phenotype described above, along with T2 hyperintensity of the dentate nucleus on MRI.
The Design of an Embedded Multi-Sensor Data Fusion System for Unmanned Surface Vehicle Navigation Based on Real Time Operating System

This paper describes the design and implementation of a practical multi-sensor data fusion system for unmanned surface vehicle (USV) navigation. The system employs an embedded Linux board as the main on-board control module to extract and preprocess raw measurements from various navigational sensors using the real time operating system (RTOS). An unscented Kalman Filter (UKF) based data fusion algorithm has been developed to fuse the obtained and preprocessed sensor measurements and provide more reliable and accurate estimations of USV's navigational data in real time. The results demonstrate the effectiveness of the data fusion algorithm in reducing unpredicted errors of a standalone sensor.
Towards Real-Time Drink-Drive and Over-Speed Monitoring and Detection in South Africa

This paper proposes an effective approach for real-time monitoring and detection of drink-driving and over-speeding on South Africa (SA) roads using technologies of vehicular networks (VANET) and Internet of Things (IoT). This is because drink-driving and over-speeding have been known as the major causes of injuries and deaths on roads globally and SA is not an exception. As a viable solution, our proposed approach is based on real-time measure to provide quick responses by traffic personnel aim at saving lives before the actual accident occurs. To this end a theoretical framework of the system was designed and an application prototype developed to monitor the detected traffic offenders with respect to drink-driving and over-speeding. Moreover, we presented and discussed the system’s operations and its functionalities. A novel application simulation performed shows that the system is feasible and can accomplish the task of road safety more effective than the existing approaches
Exploration of Multidimensional Radio Frequency Imaging to Derive Remote Intelligence of Building Interiors

The Remote Intelligence of Building Interiors poses a number of serious challenges to a radio frequency imaging system. The complex nature of the building environment alongside the requirement to attain a sufficient amount of energy on the target necessitates massive measurement diversity to provide useful intelligence. QinetiQ is currently undertaking a research and demonstration project under UK MoD's Chief Scientific Advisor funding that aims to develop and exploit active and passive RF imaging of building interiors as a widely applicable intelligence tool. The objectives of the project are to derive an understanding of RF interactions with the building environment and objects and activities of intelligence interest; develop active and passive imaging techniques exploiting that understanding; evaluate potential intelligence concepts and capabilities; and mature system technology and processing techniques, facilitating rapid development and transition into service. These objectives are being addressed through the development of an experimental sensing and processing system comprising a miniature, coherent, active/passive sensor based on commercial off-the-shelf (COTS) technologies; the use of 20kg class unmanned multi-rotor aircraft as a low-cost sensor platform; the deployment of multiple sensor platforms in a distributed, coherent aperture; and the use of advanced synthetic aperture processing techniques to derive high resolution, multidimensional image products. This paper will provide a general review of these activities and the principles behind them.