Miracle-dz/UniFAR-FTUs huggingface dataset:
Features of Application of Frequency Measurement Technique Based on Spectral Analysis for Real Electrical Power Networks

This article describes the features of the power frequency measurement technique based on the application of spectral analysis in real power electrical networks. The cases of two-point and three-point interpolation of the discrete spectrum in the cases of a rectangular window and a Henning window are considered. Instrumental components of the measurement channel error and their influence on the spectrum measurement errors are considered. Measurement channel nonlinearity is considered separately. The analytical dependences of the impact of the instrumental components error on the measurement error of spectral components and power frequency are obtained. The influence signal harmonics, flicker and noise have on the methodological component of the frequency measurement error by the considered technique is estimated by means of simulation mathematical modeling. Simulation mathematical modeling is performed by the software package Simulink 8.
Deep Canonical Correlation Analysis Network for Scene Change Detection of Multi-Temporal VHR Imagery

Change detection at semantic scene level has now been an important topic of high spatial resolution remote sensing imagery analysis. In this paper, combining with Deep Canonical Correlation Analysis (DCCA), we proposed an end-to-end network (DCCA-Net) for scene change detection. DCCA-Net firstly utilizes a pretrained Convolutional Neural Network (CNN) to extract high-dimensional features of the input scene pairs. Then, the DCCA module is deployed to project the extracted features into a new feature space and maximize the correlation of the projected feature pairs. Finally, based on the transformed feature vectors, the semantic label of each scene image could be obtained by a softmax classifier. The binary scene changes could be obtained using a binary classifier based on the transformed features. The objective function of DCCA is integrated into the loss function of classification and binary change detection, so that they could be optimized simultaneously. We implemented the proposed network and performed experiments on a VHR remote sensing imagery dataset (Multi-temporal Scene - Wuhan, Mts-WH) for scene change detection. The experimental results have shown the effect of the DCCA-Net, and our method could outperform other conventional scene change detection methods and the baseline methods based on CNN.
Computation Complexity Evaluation of FastSLAM Algorithm for Unmanned Ground Vehicles

FastSLAM algorithm is commonly used in Unmanned Ground Vehicles (UGVs) recently. One of the main problems under research is the computation cost of this probabilistic algorithm. Since the speed of the UGV is limited by the latency of the algorithm, the computation complexity and its effect on the step time of the FastSLAM needs to be investigated. The present work addresses the effects of the number of particles and number of map features on the computation complexity of the FastSLAM algorithm. The study included the prediction, the observation, data association and resampling phase's complexities. Also, the correlation between the uncertainty of the UGV location and the number of particles was addressed. The simulation study was validated experimentally using hardware in the loop (HIL) setup. The analysis showed that when there is a prior knowledge of the average number of map features, an optimum number of particle filters could be set for that UGV in the given environment while maintaining an improved performance of the algorithm.
Actors in VR storytelling

Virtual Reality (VR) storytelling enhances the immersion of users into virtual environments (VE). Its use in virtual cultural heritage presentations helps the revival of the genius loci (the spirit of the place) of cultural monuments. This paper aims to show that the use of actors in VR storytelling adds to the quality of user experience and improves the edutainment value of virtual cultural heritage applications. We will describe the Baiae dry visit application which takes us to a time travel in the city considered by the Roman elite as “Little Rome (Pusilla Roma)” and presently is only partially preserved under the sea.
Performance of a Feature-Based Algorithm for Displacement Estimation of an Underwater Vehicle

An algorithm for estimating the displacement of an underwater vehicle was developed, which is based on detecting and matching features in seafloor images collected by the underwater vehicle itself. This feature-based algorithm consists of four main steps: (i) correcting radial distortions in an image, (ii) correcting tangential distortion for tilts of the camera image plane, (iii) detecting and matching features in an image sequence and (iv) converting image pixels into units of physical length. The success of image feature detection and matching depends on various factors such as the density of keypoints in the seafloor image, the feature detector, the threshold for Hessian keypoint detector, and the overlap of two adjacent images. This study aimed at investigating the effects of the above factors on the performance of the feature-based positioning algorithm. Two 5-minute seafloor videos with sparse and dense keypoints, respectively, collected off southwestern Taiwan at a depth of about 1000 meters by a deep-towed vehicle are used for testing the proposed feature-based algorithm. Performance evaluations were conducted by comparing the vehicle’s trajectory estimated by the feature-based algorithm to the DVL navigation trajectory.
Interval Data Type Creation with Object-Relational Mapping

When processing a large amount of interval-valued data, stored in a relational database, developers cannot use the pre-property provided by the DBMS without dividing a single logical unit into lower and upper boundaries and storing them in different fields of the tables, thus jeopardizing the integrity of the database. The analysis of approaches to the implementation of methods used for storage and manipulating the data containing interval values shows, it is needed to develop a design procedure for the user-defined interval-valued data type used to create application software and design a DBMS. The article describes the implemented approach of creating an interval data type in SQL Server as well as the possibilities of its implementation with object-relational mapping technology that allow to solve the problem. The advantages and disadvantages of this approach and the complexity of its implementation are revealed. Research methods based on the interval arithmetic and interval analysis theory, object-oriented programming, the theory of relational databases designing.
Optimization Model of Control System of Charge-Discharge Battery Cycles in Smart Grid Systems

The article focuses on the features of a system approach to modeling of combined solar-wind power plants as microgrid systems. The necessity of using a battery with adaptive-intelligent control between sources and consumers is noted to stabilize the operation of solar-wind power plants in changing weather conditions with unpredictable electricity consumption market. The methods of optimization of microgrid operation are analyzed in publications in order to reduce the costs of the power system. The advantage of the dynamic programming method in contrast to the linear and quadratic programming is that the objective and limiting function can be of any kind. Since the accumulated energy in the storage device is a product of sale, the charge degree of the battery indirectly reflects the economic indicator of the studied microgrid system. The model of technical and economic optimization of charge-discharge battery cycles in the microgrid is proposed on the basis of economic indicators and technical characteristics of the batteries. A criterion of maximizing the profit from the sale of accumulated electricity is formulated, taking into account the limitations on the technical indicators of the battery nodes. Since the dynamic programming method works with sequential tasks, the evolution of the battery state (the degree of its charge) during the accumulation or sale of electricity is considered in stages during the day. A comparative evaluation of the daily control of the system is illustrated in the implementation of the accumulated energy according to the directive rules and optimized algorithm.
Learning-Assisted Write Latency Optimization for Mobile Storage

I/O activities of mobile storage are highly synchronous. Flash garbage collection activities in mobile storage introduce extra delay to write requests and negatively impact on user perceived-latency. Runtime write demand is subject to correlation between multiple parameters, such as network connectivity, GPS coordinates, and current time. We propose predicting write demand with a learning algorithm, XGBoost, and conducting background, rate-based garbage collection to optimize write latency without premature, excessive flash erasure. Our method reduced the 99-th percentile write latency by 56% compared to on-demand garbage collection and decreased flash erase count by 51% compared to unconditional background garbage collection.
Doppler & SNR Dependent Thresholding of CW-Data

In anti-submarine warfare, high resolution, broadband sonars are commonly used. This, however, might challenge the sonar operator in littoral environments as the sonar image often becomes cluttered due to false alarms. The origin of these false alarms can be anything from topographical features in general to man made objects, such as pipelines. The large amount of false alarms in littoral waters makes correct classification of contacts difficult. Normally, a fixed detection threshold on signal-to-noise ratio is employed in order to reduce clutter and make classification feasible. For continuous wave echoes, the same fixed threshold method can be used on Doppler to reduce the false alarm rate. However, this will also lower the probability of detecting a valid target, such as a submarine. A better method for reducing the false alarm rate with a lower reduction of detection probability, is by applying a variable threshold that is dependent on both SNR and Doppler, simultaneously.Here we apply different dependencies between SNR and Doppler in order to find the best target to false detection ratio possible. Due to the classified nature of sonar data with submarines, the dataset used is synthetic with several synthetic submarines present. The various dependencies will be tested on several different synthetic submarines (small, large, fast, slow) to verify that they are not also submarine dependent, as this would reduce the usefulness of the SNR-Doppler dependency significantly.We find that using thresholds that consider both SNR and Doppler simultaneously will in most cases perform much better than a single fixed threshold. Furthermore, the effect on using SNR and Doppler dependent thresholding before clustering has a huge impact on the presented sonar image, improving the amount of false tracks greatly. Finally, we find an unexplained dip in false alarms at a narrow Doppler interval in our synthetic data. This dip will most likely benefit simpler thresholding methods more than the more advanced. Hence, this dip should be understood before recomending using the simpler fixed SNR and Doppler method in false alarm rate reduction.
On-line Transmission Line Fault Classification using Long Short-Term Memory

In order to perform on-line transmission line fault diagnosis, this paper proposes a classification algorithm, which combines the long short-term memory (LSTM) network with a calibration training filter. The LSTM network adopted in this research is a multilayer recurrent neural network. As a deep learning algorithm, LSTM is extremely suitable to complex time-series classification problems, such as speech recognition and natural language processing. As the number of units in LSTM is much larger than conventional artificial neural networks (ANNs), the training progress is time consuming, and not able to be performed by on-line diagnosis devices. However, the parameters of the transmission line are always varying with time, which requires frequently calibration training on the network. In order to accelerate the calibration training of LSTM, a filter enhanced calibration is proposed. The filter selects samples having the same pattern as the signal under diagnosis, and further reduces the training complexity. The experimental study compares the proposed filter calibrated LSTM (FC-LSTM) against other neural networks and machine learning algorithms on a on-line test model. The numerical comparison not only shows FC-LSTM has a better classification accuracy and a very short time delay.
Dynamic Discount Pricing in Competitive Marketing

Discount is a frequently used marketing tool. This paper is devoted to designing an effective dynamic discount pricing (DDP) strategy in competitive marketing. First, we introduce a competitive word-of-mouth (WOM) propagation model with DDP mechanism. On this basis, we model the original problem as an optimal control problem. Next, we derive the optimality system for the optimal control problem and, thereby, propose the concept of competitive DDP strategy. Finally, through comparative experiments, we find that the profit of a competitive DDP strategy is satisfactory. Our findings contribute to maximizing the marketing profit in the presence of commercial competitors.
Design, Development and Testing of a New Solar-powered Bionic Underwater Glider with Multi-locomotion Modes

Most of Unmanned Underwater Vehicles (UUVs) can only perform single-locomotion mode, such as propeller- driven, gliding, paddling and so on. So it is difficult for these vehicles to deal with complicated missions at a fixed-point or in some limited and dangerous spaces. Additionally, energy supply is one of the important factors that seriously affects the detection range and endurance of the UUVs which are not capable to obtain power-importing from outside. As a result of these concerns, we propose a new solar-powered bionic underwater glider with multi-locomotion modes (SBUG). Based on the sea turtles hydrofoil motion and the fish body and /or caudal fin (BCF) locomotion, the bionic structure design of the SBUG includes basically two main parts, a pectoral fin actuation and a caudal actuation. The three-degree-of-freedom (3DOF) pectoral fin actuation and the 2DOF caudal actuation ensure that the SBUG can easily change locomotion modes between gliding, paddling, flapping, tail wagging and walking for various tasks. The flexible solar arrays are attached on the upper curved surface shell and wings of the SBUG so as to provide energy support to increase the voyage time. The basic concept, mechanical design and development of the SBUG are presented in this paper. Moreover, the movement performance of the mechanical prototype is confirmed through in-water trials.
Flexible Wideband Microstrip-Slotline-Microstrip Power Divider and its Application to Antenna Array

In the satellite microwave power transmission (MPT) system, antenna arrays are usually adopted for a higher gain. For the passive wideband antenna array, wideband power dividers are essential when the feed network is constructed. Because the microstrip-slotline coupled power divider could achieve wide bandwidth and compact size at the same time, it becomes a potential candidate for the feed network. However, impedance matching is always needed in the slotline, e.g., the fixed slotline length or the stepped impedance transformation. So, the power divider is not flexible. In this paper, a flexible Microstrip-Slotline-Microstrip (MSM) power divider is presented. When the ratio of the coupling coefficients $n_{1}$ of the microstrip-to-slotline transition (MST) and $n_{2}$ of the slotline-to-microstrip transitions (SMT) is 2, the MST and SMTs could be self-matched respectively. Then no impedance matching is needed anymore in the slotline. Thus, the slotline length can be arbitrarily chosen, and no stepped impedance transformations are needed. This power divider eliminates the drawback limiting the free wire routing for the microstrip-slotline coupled feed network. It could serve as a new kind of feed network, especially be considered when the simple T-junction feed network cannot achieve enough bandwidth. Besides, a wideband Vivaldi array with this feed network is presented.
Peculiarities of Decomposition of Two-Timescale Models of Nonlinear Dynamic Systems

The report is dedicated to the features of the dynamics of two-timescale dynamic systems and the problem of their models decomposition. A general model of nonlinear dynamic system represented by a system of differential equations in the Cauchy normal form is considered. In the process of model decomposition there is a need for a sequential solution of two problems: the determination of its belonging to a class of two-timescale models and the construction of a simplified model. For the case of a linear initial model, a procedure for solving both of these problems is proposed, which allows obtaining a simplified model of reduced order. In the case of a nonlinear initial model, there are no general methods for solving these problems. The conditions allowing one to reduce in principle the initial model in the Cauchy normal form to a singularly perturbed model with its subsequent decomposition are considered. However, the absence of strict theoretically based methods of such model transformation allows solving the problem only in some special cases. In the present report the authors discuss an approach of approximate solution of the decomposition problem of two-timescale model of the general form. The peculiarity of this approach is the representation of the decomposition result by a set of linear models at different time intervals of the processes implementation.
Visual acquisition system for georeferenced monitoring and reconstruction of the sea bottom using audio for data synchronisation

This work describes a Smart Dive Scooter (SDS) to be used as a support tool for monitoring application of different marine species. Professional divers of Environmental Protection Agencies are periodically involved in monitoring activities. Ligurian Regional Agency for the Environmental Protection (ARPAL) and the University of Pisa (UNIPI) are collaborating towards the integration of classical methodologies with ICT tools to support the work of divers in terms of safety, cost effectiveness and time effectiveness. The SDS is the first step in this direction. It is a classical Dive Scooter, used for rapid movements underwater, that is integrated with sensors for environment monitoring (a set of cameras) and for data georeferencing (acoustic localisation system). The SDS will be used by ARPAL divers to quickly acquire images of the bottom of a target area. Processing of optical and positioning data will allow to build a virtual model on which perform all the analysis and measurement activities. This approach results in limiting the time underwater for operators increasing the area mapper per each dive. The paper focuses on the technique used for the synchronization of optical data among the various cameras and of them with the acoustic position measurements. This goal is obtained exploiting the audio tracks acquired by the cameras avoiding the necessity of bulky and energy expensive dedicated computers. Results of the validation based on experimental data collected at sea are reported.
SCADA data based realistic simulation framework to evaluate environmental impact on performance of wind turbine condition monitoring systems

Wind turbines are an integral part of renewable energy based power generation targets all over the world. With expanding fleets, varying geographical locations, improving technologies, competitive markets and a variety of manufactures to choose from, wind farm operators often end up with heterogeneous fleets. This paper presents a methodology to comprehensively evaluate the performance of fault indicators for wind turbines condition monitoring using power curves. A comprehensive analysis is done by taking into account various factors that can influence performance indicators such as the differences between the geographical locations of the wind farms and multiple fault signatures. A controlled and realistic simulation framework is presented and used to create a database covering multiple wind farms from different geographical conditions having distinct environmental profiles. Realistic fault signatures are used for condition monitoring and performance analysis using two fault detection approaches. Results show a trend in performance indicators associated to the distinct environmental profiles of the wind farms under observation. The results also highlight the limitations of local and site specific detection and performance analysis approaches and identify the distinct environmental profiles as key phenomenon for consideration in performance evaluation of fault detection methods.
Evaluating LevelEd AR: An Indoor Modelling Application for Serious Games Level Design

We developed an application that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple's ARKit and a smartphone to assist with serious games level design. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability.
Music for Sleep and Wake-Up: An Empirical Study

In this paper, we present our study on investigating the effect of music for sleep induction and wake-up. Unlike existing studies that mostly focused on understanding how specific types of music affect sleep for insomnia patients or older people in laboratory conditions, we aim to study the effect of pop music with respect to the musical features on both sleep and wake-up of young adults without serious sleep disorder in the real-world environment. We conduct two extensive experiments, for which we carefully design a system consisting of hardware and software components for reliable data collection. Our analysis focuses on what types of musical features are effective for fast sleep induction and wake-up. Furthermore, we present two application scenarios of sleep music recommendation based on our findings.
Multipath Ghost and Side/Grating Lobe Suppression Based on Stacked Generative Adversarial Nets in MIMO Through-Wall Radar Imaging

For multi-input multi-output (MIMO) through-wall radar imaging (TWRI), multipath ghosts and side/grating lobe artifacts degrade the imaging quality of the obscured targets inside an enclosed building, therein hindering target detection. In this paper, an approach based on two stacked generative adversarial nets (GAN) is proposed to achieve multipath and side/grating lobe suppression with regard to MIMO TWRI. Specifically, the Stage-I GAN constructs a spatial structure mapping from the original input images to the Stage-I GAN output images with the suppressed multipath ghosts. However, the side/grating lobe artifacts are intentionally preserved in the stage-I GAN as additional constraint information to prevent uncontrollable over-fitting. Then, the Stage-II GAN takes the output images of Stage-I GAN as input to suppress the side/grating lobe artifacts. Extensive electromagnetic simulations and comparisons demonstrate that the proposed approach achieves better suppression of multipath ghosts and side/grating lobe artifacts and other significant superiorities, including priori wall information not being required, the preservation of weak targets, and robustness for different array deployments and building layouts.