Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Crowd-Powered System Architecture for Knowledge Discovery in Scientific Domains

A substantial amount of work is often overlooked due to the exponential rate of growth in global scientific output across all disciplines. Current approaches for addressing this issue are usually limited in scope and often restrict the possibility of obtaining multidisciplinary views in practice. To tackle this problem, researchers can now leverage an ecosystem of citizens, volunteers and crowd workers to perform complex tasks that are either difficult for humans and machines to solve alone. Motivated by the idea that human crowds and computer algorithms have complementary strengths, we present an approach where the machine will learn from crowd behavior in an iterative way. This approach is embodied in the architecture of SciCrowd, a crowd-powered human-machine hybrid system designed to improve the analysis and processing of large amounts of publication records. To validate the proposal’s feasibility, a prototype was developed and an initial evaluation was conducted to measure its robustness and reliability. We conclude this paper with a set of implications for design.
Improved Real Time Ride Sharing via Graph Coloring

Ride-sharing is one of the solutions to the ever-increasing problem of congestion and air pollution in cities. However, its effectiveness in reducing private vehicles is contingent on its efficiency while maintaining economic viability from the perspective of both the operator and the passenger. We propose a ride-sharing framework that jointly takes into account the quality of sharing as well as the operator revenue while fixing the consumer cost. We formulate a weighted graph coloring optimisation problem that has the flexibility to incorporate factors that promote ride-sharing efficiency while maximizing the operator revenue. Application of our method on dataset of taxi rides in New York City, USA provides promising results vis-a-vis the state-of-the-art approaches. We illustrate that our framework has the potential to increase the occupancy of vehicles, enable sharing of bigger vehicles while assigning naturally shared rides. Further, when compared to there being no ride-sharing, on an average, the percentage of riders sharing rides and percentage of vehicles being shared is more than 85% and 75% respectively with over 60% reduction in the number of vehicles being used. The key insight obtained from this work is that incorporating quality and efficiency of shared rides has the potential to enhance operator revenue as well.
Real-Time 2.5-Gb/s Correlated Random Bit Generation Using Synchronized Chaos Induced by a Common Laser With Dispersive Feedback

We experimentally demonstrate high-speed correlated random bit generation in real time using synchronized chaotic lasers commonly driven by a laser with dispersive feedback. The dispersive feedback from a chirped fiber Bragg grating induces frequency-dependent feedback delay and thus no longer causes time-delay signature, and resultantly ensures the signal randomness and security of chaotic laser. Driven by the time-delay signature-free chaotic signal, the two response lasers are routed into chaotic states and establish a synchronization with correlation beyond 0.97 while they maintain a low correlation level with the drive signal. Through quantizing the synchronized laser chaos with a one-bit differential comparator, real-time 2.5-Gb/s correlated random bits with verified randomness are experimentally obtained with a bit error ratio of 0.07. Combining with a robust sampling method, the BER could be further decreased to $1\times 10^{-4}$ corresponding to an effective generation rate of 1.7 Gb/s. Bit error analysis indicates that the bit error ratio between the responses is lower than that between the drive and responses over a wide parameter region due to the synchronization superiority of the responses over the drive.
Integrating Demand Response in Regional Electricity Markets: An Agent-Based Simulation

This paper presents an agent-based simulation of regional electricity markets for renewable energy sources in Germany for the year 2030. We compare two regional markets - the northern and southern quarter of Germany. All agents are able to learn from earlier bids. Demand agents have the option to use load shifting to minimise their consumption costs. Our results show that regional markets are attractive to both generators and consumers, although the technology mix strongly affects agent behaviour and revenue. Additionally, regional trading leads to increased regional balancing of demand and supply.
A Proposal of Creating Ideal UTAU Voice Based on Voice of the User's Own Key by Interactive Differential Evolution

Many papers and books on voice training exist around the world. Thus, some people seem to need change their voice. In a previous study to create software for improving the voice quality, we investigated the efficiency of the software in terms of ideal voice of the user using UTAU and interactive differential evolution (IDE). In the previous studies, we thought that the key of A3 was easy to utter for both male and female. Therefore, the key of the created UTAU voices was set to A3. However, some users were difficult to utter the voice with the key. In this research, for the purpose of creating the ideal voice easy for each user to utter, we developed a new system creates the ideal voice from the key close to that user's voice. As a result of the search experiment, the evaluation value increased significantly from the initial generation to the final generation. No significant increase was observed in the evaluation experiment. Part of the results showed that the proposed method could create the ideal voice from the subject's voice.
Capacitor Voltage Balancing Control Strategy For Single-phase Five-Level ANPC Photovoltaic Inverter

Single-phase five-level active neutral point clamped (ANPC) topology is attractive for photovoltaic system. However, it has capacitor voltage unbalancing problems for both dc-link capacitors and flying capacitors. Due to the coupling effect, capacitor voltages are usually indirectly controlled or even uncontrolled, which may influence the quality of output waveform. This paper investigates phase shift PWM (PS-PWM) based capacitor voltage balancing rules and proposes capacitor voltage balancing decoupling control strategy. By injecting the compensation voltage to modulation wave and adjusting the operation time of redundant switching states in each switching period, both the voltages of dc-link capacitors and flying capacitors can be directly controlled. Because the proposed method uses two different degrees of freedom of the topology, decoupling control is realized naturally. Moreover, the dc-link capacitor voltage balancing strategy can be used in other single-phase inverter topologies by simply change the coefficient of the compensation voltage formula. The effectiveness of the proposed method is verified by simulation and experiment results.
Active Phase Shifting Method for Different Resonant Converter Configurations in Inductive Power Transfer (IPT) Systems

Active phase shifting method incorporates change in phase and amplitude of fundamental primary inverter output and secondary rectifier input voltages of a resonant DC/DC converter. This paper presents implementation of this method on four basic topologies of resonant tank configurations or compensation circuits of a resonant converter. Smooth variation of input power along with Zero Voltage Switching (ZVS) regions both on primary and secondary side have been observed for different configurations. Equations for input power as a function of the phase angles, switching frequency, input and output voltage amplitudes and the values of the reactive components have been derived. The results are verified with simulations of all the configurations and practical setup for one of the best configuration for IPT systems.
E-Loc: Enhanced CSI Fingerprinting Localization for massive Machine-Type Communications in Wi-Fi Ambient Connectivity

A location-based service in the massive machine-type wireless communications (mMTC) must respect different requirements such as a minimal energy consumption at the target device or estimating the location in ambient connectivity. The solutions in mMTC must then consider localization approaches that provide target locations with few transmitted signals and with the support of only one anchor gateway. It is also major to use a relevant input data that manages the complex radio propagation mediums. In indoor environments, a solution builds on fingerprinting approach based on the channel state information (CSI) between the target device and a single anchor gateway. This paper presents a novel CSI fingerprinting localization method, E-Loc for mMTC dedicated to indoor systems in the Wi-Fi ambient connectivity context. E-Loc architecture is based on a convolutional neural network implementing inception models with an innovative design. CSI has been collected in a complex indoor environment, post-processed to handle the transmit power diversity, phase and timing offsets and fed to E-Loc. In various spatial distributions of training locations, E-Loc outperforms other tested solutions with a 99% confidence level for localization errors around 5 meters.
Statcom Operation of Parallel-Hybrid Modular Multilevel Converter

The Parallel Hybrid MMC (PH-MMC) is characterized by a fixed modulation index (MI). The MI can be varied by injecting a third harmonic voltage component to the chain-links (CLs), which increases the voltage and current ratings of the converter. Moreover, the range of MI obtained with third harmonic injection method (THIM) is limited. This paper analyses the reactive power injection capability of the PH-MMC during balanced and unbalanced loading conditions. The PH-MMC as static compensator (STATCOM) provides wide MI range without any requirement of THIM. During unbalanced loading conditions, the sum of the capacitor voltages of the CLs tends to diverge from its reference value owing to unequal exchange of power by each CL with the grid. An expression for negative sequence current is derived, which regulates the capacitor voltages by redistributing cluster powers within CLs. The proposed analyses are substantiated using the developed models in PSCAD. Results obtained prove the excellent performance of PH-MMC-STATCOM.
SHEPWM Based New Hybrid Multilevel Inverter Topology with Reduced Switch Count

In this paper, a new single-phase hybrid multilevel inverter topology is proposed with reduced switch count. The basic unit of the proposed topology is capable of generating 13 levels at the output. A higher number of levels can be generated by extending the basic unit. Two different extensions of the basic unit have been proposed in the paper. The topology consists of a level generation unit (LGU) and the polarity changing unit (PCU). The level generation unit of the proposed topology is based on the series connection of multiple switched dc voltage sources with half and full bridge configurations. Selective Harmonic Elimination PWM (SHEPWM) based switching control technique has been employed for an improved harmonic spectrum. Performance and effectiveness of the proposed topology with the employed SHEPWM control have been substantiated by simulation results and verified by experimental results obtained from a laboratory prototype.
Influence of Co-Registration Errors on the Performance of Anatomical Constraints in MEG Source Connectivity Analysis*

We investigate how co-registration errors affect the performance of anatomical constraints in magnetoencephalographic (MEG) source modeling for source-space connectivity analysis. We simulated MEG recordings from pairs of interacting cortical sources with variable locations and with a co-registration error of 0–15 mm. We computed a beamformer-based source estimate with either a free-orientation or constrained-orientation source model and tested four popular connectivity measures: imaginary part of coherency, amplitude-envelope correlation, phase-slope index and frequency-domain Granger causality. We found that even small co-registration errors (≥2 mm) compromise the performance of source orientation constraints in connectivity estimation.
Automatic Identification of the Rock-Soil Interface and Solution Fissures From Optical Borehole Images Based on Color Features

For high-resolution borehole images obtained from digital panoramic borehole cameras, a method for recognizing the rock-soil interface and solution fissures based on color is proposed. Owing to the significantly different colors of geotechnical stratifications, such as the rock-soil interface and solution fissures, compared with bedrock, a self-adaptive geotechnical stratification detection model based on the hue, saturation, value (HSV) color space was established. A binarized image of the geotechnical stratification in the borehole was then obtained using this model. The binary image was filtered to depress the noise, and then segmented. The density of pixels in each segment was calculated to determine the depth, area, and orientation of the rock-soil interface and solution fissures, thereby identifying the geotechnical stratification in the borehole image. Verification of the method with many borehole images and comparisons with corresponding borehole radar images illustrates that it can accurately and quickly identify all rock-soil interfaces and solution fissures in a borehole. The proposed method provides a new automated, efficient, and reliable means of identifying borehole rock structures for engineering applications.
Mitigation of methane gas emission in rice by drip irrigation

Background: Rice farming faces major challenges, including water limitation, drought and climate change in the current scenario of agriculture. Among the innovative water-saving techniques, drip irrigation is a forerunner, with maximized water-saving potential, increased grain yield and methane mitigation. Methods: A field experiment was conducted comprising four different drip irrigation practices: (i) sub-surface drip irrigation (SDI) with 1.0 litre per hour (lph) discharge rate emitters (DRE) (SDI+1.0 lph DRE) (ii) SDI+0.6 lph DRE, (iii) surface drip irrigation (DI) with 1.0 lph discharge rate emitters (DI+1.0 lph DRE), (iv) DI+0.6 lph DRE and were compared with (v) a conventional flood aerobic irrigation (considered conventional). Results: The estimated grain yield of rice was found to be 23.5%, 20.3%, and 15.1% higher under SDI+1.0 lph DRE, SDI+0.6 lph DRE and DI+1.0 lph DRE practices, respectively, than the conventional method. A water saving of 23.3% was also observed for all drip practices compared with conventional practices. Seasonal methane emission flux declined 78.0% in the drip methods over the conventional irrigation: better mitigation than previously reported values (alternate wetting and drying (47.5%) and system of rice intensification (29.0%) practices). Continuous soil aeration and enhanced soil methanotrophs (P<0.05) limit the peak methane emission in rice during the flowering phase in drip irrigation, which is reflected in the methane emission flux values. Consequently, the equivalent CO 2 (CO 2-eq) emissions and yield-scaled CO 2 eq-emission were found to be significantly lower in SDI (43.8% and 49.5%, respectively), and DI (25.1% and 26.7%, respectively) methods as compared with the conventional that ensures better methane mitigation and future climate-smart rice production systems. Conclusions: Drip irrigation could reduce the cumulative methane emission in aerobically grown rice. SDI + 1.0 lph DRE practice can be applied in areas with inadequate water availability and effective in reducing the CO 2-eq emission with better yield than conventional.
Road Detection for autonomous truck in mine environment

Road boundary line detection provides information of safe driving range and ensures the safety of the vehicles, which is an important part in the sensing system. Due to the mine roads are complicated and the road boundary lines are blurred, the existing road detection methods are not applicable.In this paper, a real-time road boundary detection algorithm is proposed based on Lidar sensors. For the uneven ground of mine roads, this paper proposes a ground detection algorithm called Double Meshing. The elevated point is divided into beam areas after filtering the detected ground. Finally, the road boundary points are extracted in the beam area and the road boundary line is fitted. The performance of the proposed method is verified by experiments, which highlights its accuracy and real-time.
Personalized Telerobotics by Fast Machine Learning of Body-Machine Interfaces

Human-Robot Interfaces (HRIs) can be hard to master for inexperienced users, making the teleoperation of mobile robots a difficult task. The development of Body-Machine Interfaces (BoMIs) represents a promising approach to making a user more proficient, by exploiting the natural control they can exert on their own body motion. Since human motion presents individual traits due to several factors, including physical condition, age, and experience, generic BoMIs still require a significant learning time and effort to reach adequate ability in teleoperation. In this work, we present a novel approach which provides a Body-Machine Interface tailored on the specific user. Our method autonomously learns from the user their preferred strategy to control the robot, and provide a personalized body-machine mapping. We show that the proposed method can significantly reduce the duration of the training phase in teleoperation, thus allowing faster skill acquisition. We validated our approach by performing both simulation and real-world experiments with human subjects. The first involved the teleoperation of a fixed-wing simulated drone, while the second consisted in controlling a real quadrotor. We used our framework to extrapolate common and peculiar features of movements among individuals. Observing reoccurring strategies, we provide insights on how humans would naturally interface with a distal machine.
An Approach to Maximize Torque Density in a Brushless Doubly-fed Reluctance Machine

A brushless doubly-fed machine (BDFM) is an attractive option for megawatt-scale turbo-electric propulsion systems due to use of a partially-rated power converter, reduced maintenance, and absence of permanent magnets. However, the BDFM has inherently poor torque density because of machine saturation, even at low current-density, that offsets all the benefits. This paper proposes an approach to maximize the torque density by finding appropriate electrical excitations on the two stator windings for a given machine dimension while remaining within flux- and current-density limits. A single-objective optimization problem is formulated. The obtained results prove that while designs with equal electrical loadings on both stators, and an initial current phase offset of π/2 between the two stators, may seem a good design approach, they are far from being optimal. Our optimized solution establishes that the phase offset of 2π/3 provides maximum torque capability for an identical dimension. This procedure is validated using FEA simulations. Operating with this design also leads to higher machine efficiency and better power factor on the secondary stator, thus reducing the converter rating.
Scalability of Synchronous Reluctance Machines Considering Thermal Performance

Synchronous reluctance (REL) machines attract increasing attention in recent years, mainly due to the merits of cost-saving, high-efficiency, low-maintenance and high-reliability. For industrial mass production, the development of machines with a series of power ratings is of great demand. To this purpose, a detailed design of one machine is usually accomplished first, then a design of similar machines but different ratings are achieved by geometry scaling. This paper aims to investigate the scalability of the REL machines, taking into account the electromagnetic behavior and thermal performance. Analytical equations are presented to describe the scaling law, and the scaling factors for various parameters are studied. The scalability analysis is carried out in terms of linear and saturated situations, and additionally, the constant-temperature-rise conditions. At last, these analyses are verified by finite element simulations with various REL machine models.
A Hybrid Physical-Statistical Algorithm for SAR Wave Spectra Quality Assessment

A new approach for assessing the quality of Synthetic Aperture Radar (SAR) wave spectra is presented here. The algorithm addresses two specific issues, related to the 180° directional ambiguity in the propagation direction inherent to SAR measurements, and the removal of noise. In spite of several and progressive advances in the mapping and retrieval of SAR wave spectra, these issues are persistent in the existing official databases and hinder the use of these data for practical uses. This new algorithm is based on a recently developed database of long-term global wave spectral characteristics, which allows estimating the occurrence probability of every individual wave system found in the observed spectra, and that of its ambiguous pair. In addition, assuring the spatial consistency of the wave systems along track, helps obtaining more robust results. In this sense, wave spectra within a track are processed in several steps, so that wave systems with more likelihood of being correct are processed first, and consequently used to evaluate the more challenging components. Accordingly, a specific quality label is assigned to every wave system depending on the certainty achieved. An Envisat track over a challenging area with multiple crossing swells is used to illustrate the performance of the algorithm.
A Novel Fast Retina Keypoint Extraction Algorithm for Multispectral Images Using Geometric Algebra

The feature extraction for multispectral images plays an important role in many computer vision applications. Recently, geometric algebra (GA) based scale invariant feature transform algorithm (GA-SIFT) and GA based speeded up robust Features algorithm (GA-SURF), have been proposed to extract feature of multispectral image in GA space. However those methods are difficult to be implemented in real-time applications. Now, the challenge is to design a new algorithm to extract the features of multispectral image more efficiently and quickly, so that it can be used in real-time applications. Although the proposed fast retina keypoint (FREAK) algorithm is faster to compute and more robust than SIFT and SURF, it can not be utilized to extract features directly for multispectral images. In this paper, we propose a novel fast retina keypoint extraction algorithm based on GA, named as GA-FREAK, for multispectral images. Firstly, the multispectral images are represented as multivectors in GA space, then the interest points are detected with the procedure of FREAK in GA space. Finally, our experiments demonstrate that the GA-FREAK is faster and more robust than some previous algorithms in multispectral images. It is expected that the proposed GA-FREAK will be a competitive alternative in real-time applications of multispectral images.