Miracle-dz/UniFAR-FTUs huggingface dataset:
Encoderless Control of PMa-SynRG Based-Stator Current Vector for Renewable Energy Systems

In this paper, a novel encoderless control of permanent magnet assisted synchronous reluctance generator (PMa-SynRG) based-stator current vector for renewable energy systems is presented using the hardware-in-the-loop (HIL) platform. This platform is employed for emulating the PMa-SynRG. For the encoderless control, the PMa-SynRG rotor position angle along with rotor angular speed are estimated ultimately from the generator’s stator currents, as the stator inductance is only requisite nevertheless the stator resistance. Indeed, this encoderless control technique employs one current control loop for regulating the magnitude of the stator current instead of employing two current control loops. For eliminating the current measurement noise, frequency-locked loop (FLL) with a second-order generalized integrator (SOGI) are employed for enhancing the performance of the encoderless control. The proposed encoderless control technique with the SOGI-FLL has a very simple implementation while accomplishing a rapid along with precise estimation performance of the PMa-SynRG rotor position angle and speed.
A high-precision long-range cooperative radar system for gantry rail crane distance measurement

We present the implementation of a cooperative radar system on a gantry rail crane for distance measurements in an industrial environment. The measurement approach is based on the dual-ramp frequency-modulated continuous-wave principle, using identical sensor-nodes at the endpoints of the range of interest. Pseudo-range information is exchanged via a dedicated data-link between these stations. At the sensor-node a flexible high-performance signal processing and remote management engine is implemented. The system setup is controlled by a single host-PC, which is used as a man–machine interface for configuration of the remotely controlled measurement stations, system surveillance, and visualization of the measurement data. Indoor characterization of the developed hardware is sufficient for an efficient calibration of the system, minimizing distance offsets. On-site measurements at distances up to 1000 m with an accuracy better than 2 cm confirm the performance of the ranging system. Furthermore, the results are verified by simulation.
Tire camber angle influence on tire-pavement noise

Taking into account tire-pavement noise and tires classification with respect to noise emission special measurement methods are usually used. When two of them are applied (the Laboratory Drum Method (DR) and the Close-Proximity Method (CPX)) the investigator has to be sure that the position of the tire is correct. The authors of this paper thought about tire position as tire (wheel) alignment—in particular tire camber angle. T hey intended to present its influence on tire-pavement noise.
Optimal Control Design of Dynamical Tracking for Connected and Automated Vehicles

With the development of UAV technology, the re-search of leader-follower formation tracking has been extensively studied due to its simple formation structure and communication connection. However, the high maneuverability and dynamics of UAVs make the formation tracking control much more complicated and difficult. In this paper, the optimal formation control algorithms are proposed for both near-equilibrium and general dynamic control cases considering the time-varying leader velocity and communication delays. In near-equilibrium control case, a linear optimal control strategy is proposed, and the function coefficient can be derived by using a backward recursion. Then, the result of the near-equilibrium case is extended to the general dynamic case where the leader moves at an uncertain time-varying velocity. In each sampling period, based on the updated state information of UAVs, the optimal formation control strategy can be obtained by the same algorithm design approach as the near-equilibrium case, and the function coefficient is the convergent value deduced by using a backward recursion manner.
Neural Network based Inter bi-prediction Blending

This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, the motion compensation of blocks from already decoded reference pictures stands out as the principal tool used to predict the current frame. Especially, the bi-prediction, in which a block is obtained by averaging two different motion-compensated prediction blocks, significantly improves the final temporal prediction accuracy. In this context, we introduce a simple neural network that further improves the blending operation. A complexity balance, both in terms of network size and encoder mode selection, is carried out. Extensive tests on top of the recently standardized VVC codec are performed and show a BD-rate improvement of −1.4% in random access configuration for a network size of fewer than 10k parameters. We also propose a simple CPU-based implementation and direct network quantization to assess the complexity/gains tradeoff in a conventional codec framework.
2D DoA-based Positioning with Phase Jump Corrections and An Approximate Maximum Likelihood Estimator

Direction of arrival (DoA) estimation is crucial to improve communications systems’ performance, leading to much more accurate results in localization, one of the most vital applications in the Internet of Things (IoT). Unlike the range-based ones, the direction-based positioning algorithms estimate the unknown position by the measured angles whose values must be predefined in an interval of 2π-length. Noisy measurements with values near the edges of this interval can lead to drastic estimation errors, making the convergence of iterative procedures much more challenging. In this paper, we propose a Maximum Likelihood (ML) estimator, which applies iterative procedures for position estimation. Our procedure is based on the atan2 function, which has the 2π-long codomain to map the DoA. Moreover, a novel mechanism to make the estimation near the edges much more robust, phase jump corrections are proposed to rectify the final estimates. In addition, a new approximate ML estimator, where the effects of approximately normal distributed DoA estimation errors are limited to first-order perturbations, is also introduced. Outputs of this approximate estimator help to enhance the accuracy of the true ML estimator. Simulation results show significant performance improvements.
Mathematical Foundations for Balancing Single-Phase Residential Microgrids Connected to a Three-Phase Distribution System

With the increased installation of single-phase rooftop PV systems, in-house battery storage units, and high-power plug-in loads (i.e., EVs) at single-phase residential sites, it is prevalent that an increasing number of residential distribution systems are becoming severely unbalanced, causing power quality problems and thermal risks at distribution sub-stations. While different techniques have been investigated to resolve this issue, there is still a lack of adequate theoretical foundations to guide these approaches. In this study, a detailed analytical analysis is carried out for a typical North American residential community with single-phase power generation, storage, and high-power random plug-in loads. This analysis has laid the foundation for a class of operating scenarios and provides an essential theoretical basis to unify different techniques for dynamically balancing single-phase microgrids connected to three-phase distribution systems. Detailed formulations have been developed for the first time to draw explicit power transfer relationships among power surplus and power-deficient phases to achieve an overall dynamic balance. A user-friendly, free interactive online tool has also been developed for potential users to evaluate their own application scenarios.
Exsumm-VIZ: Visual Interpretation of Attention Model In Text Summarizations

Deep learning has enhanced the state of the art in several domains, including text summarizing, with pointer generator summarization one of the dominant approaches in recent years. However, several obstacles remain for the summarizing model, such as over-and-under summarization and processing large phrases, requiring human evaluation. Furthermore, summarization raises issues of explainability, and user interaction, compelling the development of better analytics systems. To address these limitations, we introduce EXSUMM, a tool for visualizing analysis of attention weights, data, and evaluation metrics associated with text summarization. Through its linguistic and semantic filtering, the tools help users understand what their models are “focusing on. ” such as abstractiveness and factual consistency. We validated our visual system's effectiveness through various use cases and user studies and discovered a favorite for EXSUMM over typical text-based summarization systems, particularly for lengthy texts.
Design Challenges and Methodology of High-Performance SRAM-Based Compute-in-Memory for AI Edge Devices

The traditional von Neumann architecture splits computing units and memory, greatly limiting the development of high energy efficient devices used in the field of AI edge computation. Compute-in-Memory (CIM) is a significant breakthrough to this memory wall and energy wall. This paper introduces the background of CIM macros, and discusses the challenges that the development of static random access memory based CIM (SRAM-CIM) designs faces. We also outline the corresponding methods proposed in recent works and compare compound performance based on these high-performance memory devices.
Stabilization of Dicalcium Silicate-Zn Composite Approaching Layered Double Hydroxide Structure for Bioactive Cement Applications

Layered Double Hydroxide (LDH) is ionic clay that is characterized by the union of metal cations and OH- hydroxides. LDH composites exhibit considerably high releasing and recharging capacity and have applications as bioactive cements. They can be prepared by direct co-precipitation of metal salts at controlled pH. The preparation is carried out from an acid solution of Zn (NO3)2.6H2O, Al (NO3)3.9H2O and a basic solution of Na2CO3 and NaOH, with a Zn/Al ratio = 3, the pH is stabilized between 9 and 9.5 at a constant temperature of 45°C. The objective of this study is to incorporate Zinc and Aluminum elements at different percentages in dicalcium silicate phase to produce C2S phase incorporating LDH composite. The characterizations of the developed phases by XRD and SEM indicate the formation of stoichiometric LDH phases Zn6Al2(OH)16CO3.4H2O and non-stoichiometric Zn0.61Al0. 39 (OH)2(CO3)0.195. xH2O, the incorporation of Zn in the belitic C2S phase and not Al. The obtained micrographs by SEM(EDAX) analysis show new morphology of the stabilized composite.
Electric Power Sources Based on the Seebeck Effect of Manganese Sulfides with Rare Earth Substitution

The thermoelectric properties of compounds with variable valence Mn1-ХReХS (0 ≤ X ≤ 0.2) in the temperature range of (80 – 1100) K are studied. The maxima on the temperature dependences of the Seebeck coefficient (thermal EMF) for all substitution concentrations and the change of the sign of the Seebeck coefficient from positive to negative with an increase in the substitution concentration in Mn1-XYbXS are determined. A model of impurity donor 4f-states is proposed and a satisfactory agreement with the data on the thermal EMF is obtained.
Fischereiökonomische Forschung in Deutschland – Ergebnisse zur deutschen Fischereiflotte aus einem EU-Verbundprojekt

The economic situation of the German fishing fleet, economic indicators and backgrounds have been analysed in the overall framework of the EU-Concerted Action ‘Economic Assessment of European fishing fleets’. Trends in number of vessels, employment on board and catches for main target species are decreasing. This development may clearly be related to bad fish stock conditions, missing investment op-portunities for vessels and the short-term fishing quota man-agement system. To facilitate fisheries economics research a better data collection system is needed. As a consequence economic advice may be given for the development of a long-term sustainable management system.
Escores de Variáveis Latentes: Uma Opção para o Índice ANEEL de Satisfação do Consumidor

Structural Equation Models with unobservable variables and measurement error have been used in the production of customers satisfaction indexes to evaluate products and services quality as well as the economic performance of companies, sectors and nations. Previous studies pointed out the PLS (Partial Least Square) better than Maximum Likelihood method to estimate parameters in marketing applications, mainly because of data marketing feature. However, no study has shown the effects of estimation methods in the latent variable scores, specially, in the satisfaction scores. The objective of this study is to analyze the effects of estimation methods in the satisfaction scores based on the IASC model (Customer Satisfaction Index of Brazilian Electricity Regulatory Agency). The data and true scores will be generated by Monte Carlo Simulation and the estimated scores will be compared to the true ones through the following information measures: linear correlation, mutual information and an empirical information measure.
An analytical mathematical method for calculation of the dynamic wheel–rail impact force caused by wheel flat

ABSTRACT The simplified method to determine a vertical impact force of wheel with flat and rail interaction is presented in this article. The presented simplified method can be used to identify maximum contact force and its distribution in the contact length between the damaged wheel and the rail. The vertical impact force depends on geometrical parameters of the rail and wheel with flat, speed of vehicle and the angle of deviation of rail. This article demonstrates the influence of wheel with flat geometrical parameters, speed of vehicle to maximum contact force and its distribution in the contact zone. The obtained values of the simplified method for determination of a vertical contact force are compared with the results obtained from field measurements.
Integrated-optic lattice-form tuneable chromatic dispersion compensator with increased number of operable channels

A lattice-form tuneable chromatic dispersion compensator whose number of operable channels is doubled by adding an interleave filter is reported. In this scheme, interleaved signals are introduced into two different inputs of the lattice-form compensator, and are provided with the same dispersion compensation value every half free spectral range. Thus, this configuration can activate inoperable channels that account for nearly half of the total bandwidth in existing lattice-form compensators. The proposed compensator was fabricated by monolithically integrating all components with silica-waveguide technology. The dispersion compensation value of −321 to 299 ps/nm was obtained over 40 GHz bandwidth with a channel period of 50 GHz.
A self-consistent numerical model for bipolar transport in carbon nanotube field-effect transistors

Carbon nanotube (CNT) field-effect transistors (CNTFETs) utilize a semiconducting CNT channel controlled by an isolated electrostatic gate. The essential physics of these devices is captured in a numerical model that allows calculation of energy band diagrams and currentvoltage (I-V) relationships to describe their behavior. The numerical CNTFET device model is based on a solution to Poisson’s equation that links the electrostatic potential everywhere in the device to charge induced in the CNT. This charge is a function of the local electrostatic potential and can be calculated from the carrier distribution and the local density of states for the nanotube. Under equilibrium conditions the carrier distribution within the CNT channel is known precisely. However, under non-equilibrium conditions (when a source-drain bias is present) carrier distributions in the channel are distorted from their equilibrium forms due existence of hot electrons and holes emitted from the source and drain, respectively. Quasi-equilibrium Fermi statistics are used to approximate non-equilibrium carrier distributions using an equilibrium distribution function shifted in energy. Solving Poisson’s equation self-consistently for charge and the electrostatic potential provides a profile of potential energy along the length of the CNT channel. Current flow in the device is a function of the ability of carriers to tunnel through or be thermionically emitted over the potential barriers in the channel. The contribution of both electrons and holes is considered when solving Poisson’s equation and calculating the drain current. The numerical CNTFET model is a flexible framework for the examination of a wide variety of device geometries and materials parameters. It employs a finite element package to solve Poisson’s equation in a two-dimensional projection of a cylindrical model space with azimuthal symmetry. The technique of conformal mapping is used to allow exact electrostatic solutions in unbounded structures without imposing artificial boundary conditions. The geometry and materials parameters of the CNTFET are systematically varied and the effects of these changes are observed in the performance predictions of the model. The model is also used to explore the behavior of more complex experimental architectures, such as partially gated CNTFETs, and provides new insight into their operation.
Online shopping abandonment rate a new perspective : the role of choice conflicts as a factor of online shopping abandonment

The high rates of online shopping abandonment are a major problem in a trillion dollar industry. In this research we focused on the extent to which choice conflicts influence online shopping cart abandonment. We began by investigating exploratorily the nature, frequency, and sources of choice conflicts as they manifest themselves in the online environment. Next, we studied experimentally the effects of choice conflicts on the shopping cart abandonment using a custom developed webshop to replicate a real online shopping session. For this purpose, we used a 2x3 factorial experimental setup and we analyzed data from 164 participants. We found that choice conflicts were not a direct cause of online shopping abandonment. Instead, results indicate that with each experienced choice conflict, the chance of a perceived higher decision difficulty increased 17 times. Subsequently, a perceived higher level of decision difficulty increased the chance of abandoning the shopping cart by 22%. Additionally, we found that the effort to search information and product attribute alignment also influenced the perceived decision difficulty; at the same time, maximizing behavior was identified as the main source of choice conflicts. The resulting research model illustrates the conjoint effect of choice conflicts and perceived decision difficulty on increasing the chance of online shopping abandonment. We found that displaying products with nonaligned attributes decreased the perceived decision difficulty and reduced the chance of shopping abandonment. Further research is needed to develop methods to lower the chance of experiencing choice conflicts and high decision difficulties.
Effect of Microgroove Structure in PDMS-Based Silicone Implants on Biocompatibility

Capsule and capsule contracture around implants are important concerns in a clinic. The physical topology of the material surface regulates the formation of the capsule, but the specific regulatory mechanism is unclear. In this study, four types of silicone implant materials with different microgroove structures (groove depths of 10 and 50 μm and widths of 50 and 200 μm) were constructed using lithography to form different gradient surface topologies. Mass spectrometry, Cell Counting Kit-8, 5-ethynyl-2′-deoxycytidine (EdU), enzyme-linked immunosorbent assay, western blot, immunofluorescence, and immunohistochemistry were used to explore the changes in protein adsorption, cell adhesion, cell proliferation, and collagen deposition on the surface of the materials. At the same time, RNA-seq was used to detect transcriptome differences caused by different structures. Furthermore, collagen deposition and capsule formation were observed in the rats. The groove structure was observed to significantly increase the surface roughness of the material. The deeper groove and the narrower width of the polydimethylsiloxane would increase the surface roughness of the material and the surface water contact angle but reduce the total amount of adsorbed protein in the first two hours. In vitro cell experiments revealed that microtopology affected cell proliferation and adhesion and regulated collagen secretion. Further analysis indicated the deeper and narrower groove (group 50–50) on the surface of the material caused more evident collagen deposition around the material, forming a thicker envelope. Surface roughness of the material was thus related to collagen deposition and envelope thickness. The thickness of the envelope tissue around smooth materials does not exceed that of the materials with surface roughness. In conclusion, the narrower and deeper grooves in the micron range exhibited poor histocompatibility and led to formation of thicker envelopes around the materials. The appropriate grooves can reduce envelope thickness.
Combining multiple clustering and network analysis for discoveries in gene expression data

Clustering is a challenging research task which could benefit a wide range of practical applications, including bioinformatics. It targets success by optimizing a number of objectives, a characteristic mostly ignored by clustering approaches. This paper describes a synthetic clustering algorithm which first applies multi-objective based approach to produce the alternative clustering solutions. Then the best clusters from each solution are selected and combined into a seed for a compact and effective solution which is expected to be better than all the individual solutions because it combines the best of each. This way, the developed algorithm may be classified as a fuzzy clustering approach because each object may belong to more than one cluster in the synthesized solution with a degree of membership in each cluster. Another interesting aspect of the algorithm is that it identifies the outliers. Further, a network is built from the relationships of the objects within the various clusters. The network is analyzed to reveal interesting discoveries not clearly reflected in the clustering outcome. The validity and applicability of the presented methodology has been assessed using synthetic and real data from the cancer.
A semantic-based access control approach for systems of systems

Access control management in a System of Systems---i.e., a collaborative environment composed of a multitude of distributed autonomous organizations---is a challenging task. To answer the challenge, in this paper we propose a novel approach that incorporates semantic technologies in the Attribute-Based Access Control (ABAC) approach. Building on the basic principles of ABAC, our approach allows for a highly expressive modeling of the context in which access decisions are made, by providing mechanisms to describe rich relationships among entities, which can evolve over time. In addition, our system works in a truly decentralized manner, which makes it suitable for geographically distributed enterprise systems. We show the feasibility in practice of our approach through some experimental results.