Miracle-dz/UniFAR-FTUs huggingface dataset:
A Neural Network Based Landing Method for an Unmanned Aerial Vehicle with Soft Landing Gears

This paper presents the design, implementation, and testing of a soft landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces. With full consideration of unmanned aerial vehicles and landing gear requirements, a quadrotor helicopter, comprised of one flying unit and one landing assistance unit, is employed. Considering the touchdown speed and posture, a novel design of a soft mechanism for non-flat surfaces is proposed, in order to absorb the remaining landing impact. The framework of the control strategy is designed based on a derived dynamic model. A neural network-based backstepping controller is applied to achieve the desired trajectory. The simulation and outdoor testing results attest to the effectiveness and reliability of the proposed control method.
SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data

In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data. We propose SpectraNet, a novel multivariate time-series forecasting model that dynamically infers a latent space spectral decomposition to capture current temporal dynamics and correlations on the recent observed history. A Convolution Neural Network maps the learned representation by sequentially mixing its components and refining the output. Our proposed approach can simultaneously produce forecasts and interpolate past observations and can, therefore, greatly simplify production systems by unifying imputation and forecasting tasks into a single model. SpectraNet achieves SoTA performance simultaneously on both tasks on five benchmark datasets, compared to forecasting and imputation models, with up to 92% fewer parameters and comparable training times. On settings with up to 80% missing data, SpectraNet has average performance improvements of almost 50% over the second-best alternative. Our code is available at https://github.com/cchallu/spectranet.
Terrain Matching by Fusing HOG With Zernike Moments

A new matching algorithm is proposed by fusing the histogram of oriented gradients’ and the Zernike moments’ descriptors extracted from the real-time synthetic aperture radar (SAR) images and the real-time elevation maps (REMs), respectively. Both the SAR images and the REMs are acquired by an on-board interferometric SAR simultaneously. The two reciprocal descriptors are fused by weighting their Canberra distances. The numerical experiments demonstrate the advantage of the proposed matching algorithm preliminarily.
Multi-Objective Optimization of Decoupling Capacitors for Placement and Component Value

A multiobjective evolutionary method is proposed for the optimization of surface mount device (SMD) multilayer ceramic chip (MLCC) capacitors used for decoupling on printed circuit boards (PCBs) with resonant power–ground plane pairs. The proposed approach provides options for designers to meet power integrity specifications by employing uniform-valued capacitors that help to reduce the bill-of-material (BOM) cost and mitigate the part procurement risk. It is also shown that uniform MLCC capacitors with variable distance from power pins can have a similar decoupling effect to an assortment of capacitors. By simultaneously optimizing the pin-capacitor distance and value, the proposed method is shown to allow for the allocation of fewer decoupling capacitors under the ball-grid-array (BGA) pin field of an integrated circuit (IC) device.
Application of kernel density estimation to extrapolating the fatigue loads on a high-speed train

The fatigue loads on the carbody of a high-speed train (HST) increase gradually with operating mileage. Because of the long design life of the carbody, it is difficult to conduct a long-term test of its fatigue load history. Instead, the loads are usually measured over relatively short periods of time and then extrapolated. This paper extracts several representative load-history samples during the service cycle of carbody of HST and base on the rainflow-counting matrix of these samples. The method of two-dimensional variable-bandwidth kernel density estimation (KDE) is used to extrapolate the HST carbody loads. Finally, the KDE extrapolation data are verified against measured HST data and compared with the results of conventional linear extrapolation. The finding is that KDE extrapolation based on multi-sample load data can give results that are similar to the measured data. Compared with traditional linear extrapolation, KDE extrapolation is better at simulating low-cycle large-scale loads. Comparing the damage calculated by KDE extrapolation with the measured data, the error between the two is 6.59%, indicating that KDE extrapolation meets the mechanical engineering requirements.
Quantification and visualization of spatial uncertainty in isosurfaces for parametric and nonparametric noise models

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy QUANTIFICATION AND VISUALIZATION OF SPATIAL UNCERTAINTY IN ISOSURFACES FOR PARAMETRIC AND NONPARAMETRIC NOISE MODELS By Tushar Athawale May 2015 Chair: Alireza Entezari Major: Computer Engineering Isosurfaces are the key entities in visualization, and recent research has focused on the visualization of uncertain data. We study positional uncertainty in isosurfaces when data uncertainty is modeled with parametric and nonparametric density models. Noise in sampled data leads to topological and geometric variations in the extracted isosurface. We propose probabilistic techniques that reduce topological uncertainty in isosurfaces and resolve ambiguities in identifying topological configurations. We also characterize, in closed form, a random variable modeling the geometric uncertainty in isosurface extraction. Closed-form characterization of geometric uncertainty allows analytic computation of expected value of level-crossing location, as well as the variance. While former quantity can be used for constructing a stable isosurface for uncertain data, the latter can be used for visualizing the spatial uncertainty in the extracted isosurface. We show computational advantage of proposed analytic approach over expensive Monte-Carlo sampling approach for quantifying geometric uncertainty. The advantages of nonparametric statistical models over parametric noise models for uncertainty characterization are evident from our experiments on ensemble datasets and uncertain scalar fields. We also propose a novel approach that leverages nonlocal statistics for accurate characterization of underlying uncertainties in the nonparametric framework.
Cost Effective Single-Phase DSTATCOM for Low Power Applications

Abstract In this article, a single-phase Distribution Static Compensator (DSTATCOM) with reduced dc-link voltage is proposed for power quality improvement in a distribution system. Generally, the dc-link voltage of DSTATCOM is maintained twice the peak of Point of Common Coupling (PCC) voltage by voltage regulation loop. But, the dc-link voltage is maintained itself without voltage regulation loop in the proposed method. For this case, the required dc-link voltage magnitude is less when compared to conventional method. Under predefined load conditions such as agriculture pump set loads, this less voltage is sufficient to give satisfactory performance. Therefore, the rating of DSTATCOM is reduced and thereby the cost of system is reduced. The proposed method enables compensation of reactive power and mitigation of source current harmonics in a distribution system for low power applications. The advantages of the proposed method are elimination of dc voltage sensing elements, minimization of switching loss of Voltage Source Inverter (VSI) and cost effective. The proposed single-phase DSTATCOM performance is demonstrated through MATLAB/simulink and validated by experimental results with agriculture pump set load.
Geographic information systems visualization of wind farm operational data to inform maintenance and planning discussions

As utility scale wind farms age, maintenance and contingency planning become increasingly important. Decisions about when and how to repair or replace major turbine components can critically influence profitability. Condition monitoring and prognostic reliability modelling are sometimes used to support these decision-making processes. These often resource intensive, sophisticated techniques are frequently administered by third parties and can be black boxes to wind farm stakeholders. Early experience from the YR21 Investment Decision Support Program has highlighted the importance of broad engagement across wind farm teams in maintenance and planning discussions. The utilization of geographic information systems to illustrate data trends across wind farms proved to be a valuable tool in fostering fundamental understanding of an operation’s signature performance characteristics. This graphical representation of the farm provides a useful visualization of the operation’s best and worst performers in terms of power produced, wind speeds experienced, total revolutions, or highest gear box temperature. These transparent representations of the data represent valuable starting points for discussion of performance or potential maintenance issues across farms. In some cases, it can reveal unexpected trends that may raise bigger questions about how the farm is operating in general. Finally, these simple figures can serve as complementary inputs to larger, more complex data-driven decision systems. Geographic information system plots are presented for three wind farms to demonstrate the potential utility in simple, transparent, and accessible data visualization.
Multitask Bayesian Spatiotemporal Gaussian Processes for Short-Term Load Forecasting

Short-term household electricity load forecasting is important for utility companies to ensure reliable power supplies. Traditional methods for load forecasting relied on historical records from one single data source and have limitations with insufficient or missing data. Recently, an emerging family of machine learning algorithms, multitask learning (MTL), has been developed and has the potential for load forecasting. By MTL, the electricity consumption data from multiple communities can be fused to improve forecasting accuracy. However, appropriate modeling of the relatedness to enable the between-community knowledge transfer remains a challenge. This paper proposes an improved MTL algorithm for a Bayesian spatiotemporal Gaussian process model (BSGP) to characterize the relatedness among the different residential communities. It hypothesizes on the similar impacts of environmental and traffic conditions on electricity consumption in improving short-term load forecasting. Furthermore, this paper proposes a low-ranked dirty model along with an iterative algorithm to improve the learning of model parameters under an MTL framework. This paper uses a real-world case study from two residential communities in Tallahassee, Fl, USA, to demonstrate the method effectiveness. The proposed method significantly outperforms state-of-the-art forecasting methods and effectively captures the relatedness to provide between-community knowledge transfer compared with other MTL methods.
Risk management in construction projects by coupling the SMACC agent with the MADS MOSAR method – application to the dam project in Mascara, Algeria

Abstract Construction projects are subject to numerous risks and their management is an important issue. To meet this challenge, we developed Stochastic Simulation for Multi-Agent Construction Project (SMACC), a construction project simulation tool considering project risks and based on an agent-based approach. But, the simulation of a construction project by SMACC requires specific information about the project and the risks that may affect it. Yet, our previous work was not interested in the instantiation process of the model from a real project. This article presents an approach being placed upstream of SMACC for instantiating the agents of the model and evaluating them with a focus on risks. This approach is based on the combination of project management approaches and MADS MOSAR (Organized Method of Systemic Risk Analysis). It is applied to a real case: the construction of a dam vault in Oued Taht in Mascara in Algeria.
Experimental validation of an isoflux Earth coverage with a bimode ARMA antenna on a nanosatellite

Abstract Spatial telemetry links on nanosatellites require more and more reconfigurable beam antennas to improve the Earth coverage. The bi-mode Agile Radiating Matrix Antenna (8.0–8.4 GHz) was successfully designed to solve such kind of problems by using an isoflux mode associated with a switchable directive one. However, such an antenna introduces some manufacturing problems for the isoflux mode, mainly due to the small available volume on the nanosatellite platform. This paper describes a solution to this problem thanks to the ARMA concept. A comparison between theoretical and experimental results for the isoflux mode in circular polarization is presented to validate the results.
Physics of Within-Tissue Wave Propagation Generated by Pulse Propagation in the Carotid Artery

(1) Background: We aimed to assess the validity of laser Doppler vibrometry (LDV) as an emerging method to measure the local pulse wave velocity (PWV) from skin displacement generated by the pressure pulse inside an underlying artery. (2) Methods: A finite element model representing a simplified common carotid artery embedded within a soft tissue mimicking material was used to reproduce how tissue motions due to a wave propagation along the artery radiates to the skin surface. A parametric study was set up, varying: (i) the pressure conditions inside the artery (shock and traveling pressure impulse), (ii) the arterial depth and (iii) the geometry in a patient-specific artery model. (3) Results: under all conditions, the arterial pulse induced primary and secondary waves at the skin surface; of which the propagation speed deviated from the imposed PWV (deviations between −5.0% to 47.0% for the primary wave front). (4) Conclusions: the propagation of a short pressure impulse induced complex skin displacement patterns revealing a complicated link between PWV and measured propagation speeds at the skin surface. Wave propagation at the skin level may convey information about arterial PWV, however, advanced signal analysis techniques will be necessary to extract local PWV values.
A Charge-Sharing-Based Two-Phase Charging Scheme for Zero-Crossing-Based Integrator Circuits

As an effort to improve the energy efficiency of switched-capacitor circuits, zero-crossing- based integrators (ZCBI) that consist of zero-crossing detectors and charging circuits have been proposed. To break the trade-off between accuracy and speed, ZCBI typically employs a two-phase charging scheme that relies on an additional threshold for zero-crossing detection. This paper proposes a simpler realization method of the two-phase charging scheme by means of charge sharing. To demonstrate feasibility of the proposed method, we designed and fabricated a second-order delta-sigma modulator in 180-nm complementary metal–oxide–semiconductor (CMOS) technology. The measurement results show that the modulator exhibits a peak signal-to-noise-and-distortion ratio (SNDR) of 46.3 dB over the bandwidth of 156 kHz with the power consumption of 684 µW. We also designed the same modulator in 65-nm CMOS technology and simulation results imply that the proposed circuit is able to achieve a much better energy efficiency in advanced technology.
Active load with cross‐coupled bulk for high‐gain high‐CMRR nanometer CMOS differential stages

Nanometer CMOS technologies are characterized by low intrinsic gain and limited rejection to common‐mode disturbances, issues that analog designers must counteract through nonconventional circuit solutions able to operate under sub–1‐V supply voltages. In this letter, an approach originally proposed as a common‐mode control loop for body‐driven amplifiers is exploited by cross‐connecting the bulk terminals of the active‐load transistors of a source‐coupled pair to sensibly improve its small‐signal performance. A design example in 65‐nm process supplied from 750 mV shows that the presented solution offers both a nominal 20‐dB increase in differential gain and 29‐dB increase in CMRR, as compared with the standard counterpart.
Fluorescensuppkonvertering för framtidens solenergi

Fluorescence upconversion for the future of solar energy In a forthcoming transition from fossil fuels to green energy, solar radiation is likely to serve as an important source of renewable energy. For the solar energy to really reach a breakthrough the efficiency of solar cells and similar devices has to be increased, alternatively the possible applications of sunlight has to be widened. One way to enable both alternatives is to convert photons with low energy to photons with higher energy through so called photon upconversion. This thesis treat upconversion through triplet-triplet annihilation. The goal was to upconvert visible light to the ultraviolet spectra with the aim to enable production of hydrogen gas from water. Hydrogen gas can in turn be used as an environmental friendly fuel. To obtain further understanding of the upconversion process, upconversion systems operating in other parts of the solar spectrum were also examined. Analysis of molecules and upconversion systems were executed with spectrophotometrical equipment to obtain absorption and emission spectra. To facilitate the work, a dedicated instrument was improved and its quality and limitations were evaluated. Based on obtained emission spectra, the efficiency of relevant systems were evaluated as the fraction of absorbed photons resulting in an upconverted photon. The selections based on molecular properties resulted in a closer examination of two functioning blue-ultraviolet systems; pyrene-biacetyl and 2-methylanthracene-biacetyl as well as two green-blue systems; 9,10-dicyanoanthracene (DCA) platinumoctaethylporphyrene (PtOEP) and 9-ethoxy-10-ethylantracen (EEA) PtOEP. The study resulted in that among the candidates, 2-methyl-anthracene was the most efficient upconversion system to the ultraviolet region which in experiments upconverted 0,09 % of the incoming light. The absorption spectrum of the system means that 10 % of the sunlight spectrum is possible to utilize for hydrogen production. For the green-blue upconversion systems the efficiencies of 0,13 % for DCA and 1,5 % for EEA were obtained. To further analyse the limiting factors of the green-blue upconversion systems kinetic experiments were made, in which rate constants and lifetimes were obtained and compared. The thesis is written in Swedish.
Switchable stiffness morphing aerostructures based on granular jamming

One of the persistent challenges facing the development of morphing aerostructures is the need to have material and structural solutions which provide a compromise between the competing design drivers of low actuation energy and high stiffness under external loads. This work proposes a solution to this challenge in the form of a novel switchable stiffness structural concept based on the principle of granular jamming. In this article, the concept of using granular jamming for controlling stiffness is first introduced. Four-point bending tests are used to obtain the flexural rigidity and bending stiffness of three different granular materials under different levels of applied vacuum loading. Nonlinear finite element analysis simulations using experimentally derived nonlinear material properties show good agreement with experiment. A specific application of this concept is then proposed based on the Fish Bone Active Camber morphing airfoil. A unit cell of this concept is built, tested and analysed, followed by the first prototype of a complete switchable stiffness Fish Bone Active Camber morphing airfoil, which is experimentally shown to be able to achieve an increase in stiffness of up to 300% due to granular jamming.
Precise synchronous detectors with improved dynamic reserve

The current paper describes a special class of synchronous (phase-sensitive) detectors (SDs) for measurement purposes. They are precise and have high dynamic range. Proposed solutions are based on op-amps, having synchronously switched capacitors in the negative feedback path. Such structures give significant improvement of several specifications, especially of the input dynamic reserve. Functioning principles, circuit diagrams and gain equations are given.
Long short‐term memory and convolutional neural network for abnormal driving behaviour recognition

Abnormal driving behaviours, such as rapid acceleration, emergency braking, and rapid lane changing, bring great uncertainty to traffic, and can easily lead to traffic accidents. The accurate identification of abnormal driving behaviour helps to judge the driver's driving style, inform surrounding vehicles, and ensure the road traffic safety. Most of the existing studies use clustering and shallow learning, it is difficult to accurately identify the types of abnormal driving behaviours. Aimed at addressing the difficulty of identifying driving behaviour, this study proposed a recognition model based on a long short-term memory network and convolutional neural network (LSTM-CNN). The extreme acceleration and deceleration points are detected through the statistical analysis of real vehicle driving data, and the driving behaviour recognition data set is established. By using the data set to train the model, the LSTM-CNN can achieve a better result.
Pulsar timing arrays and gravity tests in the radiative regime

In this paper, we focus on testing gravity theories in the radiative regime using pulsar timing array observations. After reviewing current techniques to measure the dispersion and alternative polarization of gravitational waves, we extend the framework to the most general situations, where the combinations of a massive graviton and alternative polarization modes are considered. The atlas of the Hellings–Downs functions is completed by the new calculations for these dispersive alternative polarization modes. We find that each mode and corresponding graviton mass introduce characteristic features in the Hellings–Downs function. Thus, in principal, we can not only detect each polarization mode, measure the corresponding graviton mass, but also discriminate the different scenarios. In this way, we can test gravity theories in the radiative regime in a generalized fashion, and such method is a direct experiment, where one can address the gauge symmetry of the gravity theories in their linearized limits. Although current pulsar timing still lacks enough stable pulsars and sensitivity for such practices, we expect that future telescopes with larger collecting areas could make such experiments feasible.