Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Load Field Prediction of Surface-Mounted PM Machines Considering Nonlinearity Based on Hybrid Field Model

Analytical models show weakness in dealing with saturation in surface-mounted permanent-magnet machines. A hybrid field model (HFM) integrating complex permeance method (CPM) and lumped parameter magnetic circuit model (LPMCM) is proposed in this paper for predicting the on-load magnetic field considering nonlinearity effect of stator lamination. In the proposed model, the CPM calculates the field in the air gap and magnet regions, while LPMCM calculates the magnetic potential distribution inside the iron reflecting nonlinearity effect. The equivalent current sheet is obtained to replace such distribution on the stator bore. Moreover, local magnetic saturation of tooth tip is also transformed into equivalent current on the tooth surface. A solving procedure is proposed to calculate the equivalent current and guarantee the convergence. Compared with CPM, the proposed model considering the saturation effect significantly improves the prediction accuracy of the on-load performance. The HFM predictions are compared with finite element and experimental results. The excellent agreement validates its effectiveness.
Development of ZEABUS 2018 AUV

Zeabus 2018, an Autonomous Underwater Vehicle (AUV), has developed by undergraduate students from various departments in the Faculty of Engineering at Kasetsart University in order to join the RoboSub competition since 2014. The aims of developing the AUV are to efficiently control in all 6 degree-of-freedom, to accurately detect underwater objects, and to have quick and easy adaptation for various of competition tasks. Currently, the AUV has completed many tasks in the competition; however, there are several issues that require improvements. In the 2018 model, the AUV is maneuvered with 8 thrusters mounted at conscientious positions together with various sensors for navigation and control. Data are collected from these sensors and processed by two small-size computers inside the AUV. There are power and communication systems that were developed carefully so that the AUV can be operated accurately. Moreover, a software system was implemented thoroughly to control the AUV and to detect underwater objects. These elaborations make the Zeabus 2018 having higher efficiency and performance than the previous models.
Spark Load Balancing Strategy Optimization Based on Internet of Things

The data collected by the Internet of Things (IOT) technology is becoming larger and larger, and the traditional data processing methods have encountered tremendous challenges. Spark, as a memory-based distributed computing framework, provides support for the data processing of the IOT. Load balancing is an important indicator to measure the performance of Spark computing. The load balancing strategy of Spark cluster only takes into account the locality of data, and neglects the computing capability and resource utilization of each node, which is prone to load unbalance and affecting the IOT data processing efficiency. Aiming at this issue, this paper optimizes and improves the current load balancing strategy of Spark based on the computing performance of each node in the Spark cluster, and proposes a task execution node assignment algorithm based on genetic algorithm and particle swarm optimization (TENAA). Experiments show that, compared with the Spark load balancing strategy, the load balancing strategy proposed in this paper has a significant increase both in load deviation and task completion time.
Urban Computing Leveraging Location-Based Social Network Data

Urban computing is an emerging area of investigation in which researchers study cities using digital data. Location-Based Social Networks (LBSNs) generate one specific type of digital data that offers unprecedented geographic and temporal resolutions. We discuss fundamental concepts of urban computing leveraging LBSN data and present a survey of recent urban computing studies that make use of LBSN data. We also point out the opportunities and challenges that those studies open.
Battery Models Investigation and Evaluation Using a Power Demand Generated from Driving Cycles

This paper investigates how different battery models perform in terms of battery dynamics and state-of-charge (SOC) estimation. We have selected Shepherd and equivalent circuit models to represent the battery pack of lithium-ion batteries in electric vehicles (EVs). We generate the power demand using existing driving cycles to discharge the battery. We conclude that the EC model captures the dynamics of the battery more accurately than the Shepherd model. Nevertheless, both models produce similar results when we assess the SOC estimation.
Adaptive Reactive Power Control of PV Power Plants for Improved Power Transfer Capability Under Ultra-Weak Grid Conditions

This paper analyzes the power transfer limitation of the photovoltaic (PV) power plant under the ultra-weak grid condition, i.e., when the short-circuit ratio (SCR) is close to 1. It explicitly identifies that a minimum SCR of 2 is required for the PV power plant to deliver the rated active power when operating with the unity power factor. Then, considering the reactive power compensation from PV inverters, the minimum SCR in respect to power factor (PF) is derived, and the optimized coordination of the active and reactive power is exploited. It is revealed that the power transfer capability of PV power plant under the ultra-weak grid is significantly improved with the low PF operation. An adaptive reactive power droop control is next proposed to effectively distribute the reactive power demands to the individual inverters, and meanwhile, maximize the power transfer capacity of the PV power plant. Simulation results of a 200-MW PV power plant demonstrate that the proposed method can ensure the rated power transfer of PV power plant with the SCR of 1.25, provided that the PV inverters are operated with the minimal PF=0.9.
Study of Switching Transient Over Voltage due to Transient Short Circuit Fault in a Power Distribution Network

This Fault means unsatisfactory, undesirable situation or feature which is a commonly known word in power system network. Over all system’s behavior, reliability, system’s protection, controlling is very much dependent on fault analysis. When a system is in faulty condition, it states in abnormal situation and lose it’s system properties such as continuous power flow to the consumers. Moreover, it’s long existence can impairment the system equipment and causes fire whose continuation causes property loss, hence over all economical loss of the power system. To mitigate this circumstance, it necessary to understand the system behavior in the faulty situation and so there needs a term named fault analysis. This paper focused on the transient fault analysis of the distribution side of the power system network to show the transient overvoltage developed during clearing of fault. PSCAD software has been used to simulate the network and understand the behavior of voltage and current during faulty period and voltages developed at different buses during clearing of fault. In PSCAD software, a schematic diagram is built to run the simulation process, analysis the result and depicted the data in a graphical circumstance.
Bistable Current Sensing Scheme Applicable to Two-Wire DC Appliances

This paper proposes a bistable cantilever-based current sensing scheme for measuring dc current flow in a two-wire appliance cord constantly without using a cord separator to realize wide measurement range and high sensitivity without extra component from the typical cantilever-based oscillating type dc sensing system. Bistability has been introduced into dc current sensing system for the first time simply by changing the positions of magnet pole and current core. Through theoretical analysis and analytical studies, we find that as the applied dc current increases, the bistable sensing system also exhibits interwell oscillations and intrawell oscillations as the bistable energy harvester. It is proved that the output displacement amplitude is approximately proportional to the applied dc current in the interwell oscillations area where the vibrational trajectory is symmetric and the output displacement amplitude is dramatically improved. Therefore, on the same conditions the measurement range of the proposed work is 11 times wider than the oscillating type and the sensitivity is over 2 times higher than the oscillating one. In addition, we find that as interval decreases or magnet’s volume increases, the sensitivity increases accordingly while the maximum range determined by interwell oscillations area decreases. The differential equation of the proposed system is further analyzed to predict the relative output displacement. The method proposed here could shed light on bistable current sensing scheme and might be applied to self-powered current sensing system in vibrational environment.
Identifying Top-k Nodes in Social Networks

Top-k nodes are the important actors for a subjectively determined topic in a social network. To some extent, a topic is taken as a ranking criteria for identifying top-k nodes. Within a viral marketing network, subjectively selected topics can include the following: Who can promote a new product to the largest number of people, and who are the highest spending customers? Based on these questions, there has been a growing interest in top-k nodes research to effectively identify key players. In this article, we review and classify existing literature on top-k nodes identification into two major categories: top-k influential nodes and top-k significant nodes. We survey both theoretical and applied work in the field and describe promising research directions based on our review. This research area has proven to be beneficial for data analysis on online social networks as well as practical applications on real-life networks.
L-Band Efficient Dissipative Soliton Erbium-Doped Fiber Laser With a Pulse Energy of 6.15 nJ and 3 dB Bandwidth of 47.8 nm

Here, an efficient dissipative soliton mode-locked fiber laser in the L-band using nonlinear polarization rotation technology is demonstrated. With a maximum pump power of 495.3 mW and polarization state optimization, the fiber laser delivers a maximum output power of 68.6 mW with an 11.16 MHz repetition rate and corresponding single pulse energy of 6.15 nJ. The dissipative soliton spectrum is centered at 1599.6 nm with a 3 dB bandwidth of 47.8 nm. The full width at half maximum of the compressed pulse is 156 fs. To the best of our knowledge, this is the highest single pulse energy reported so far for an L-band passively mode-locked all-fiber laser.
Metabolic Regulators Nampt and Sirt6 Serially Participate in the Macrophage Interferon Antiviral Cascade

Molecular determinants underlying interferon (IFN)-macrophage biology can help delineate enzyme systems, pathways and mechanisms for enabling host-directed therapeutic approaches against infection. Notably, while the IFN antiviral response is known to be directly coupled to mevalonate-sterol biosynthesis, mechanistic insight for providing host pathway-therapeutic targets remain incomplete. Here, we show that Nampt and Sirt6 are coordinately regulated upon immune activation of macrophages and contribute to the IFN-sterol antiviral response. In silico analysis of the Nampt and Sirt6 promoter regions identified multiple core immune gene-regulatory transcription factor sites, including Stat1, implicating a molecular link to IFN control. Experimentally, we show using a range of genetically IFN-defective macrophages that the expression of Nampt is stringently regulated by the Jak/Stat-pathway while Sirt6 activation is temporally displaced in a partial IFN-dependent manner. We further show that pharmacological inhibition of Nampt and small interfering RNA (siRNA)-mediated inhibition of Nampt and Sirt6 promotes viral growth of cytomegalovirus in both fibroblasts and macrophages. Our results support the notion of pharmacologically exploiting immune regulated enzyme systems of macrophages for use as an adjuvant-based therapy for augmenting host protective pathway responses to infection.
Emerging Therapeutic Approaches for Cystic Fibrosis. From Gene Editing to Personalized Medicine

An improved understanding of the cystic fibrosis (CF) transmembrane conductance regulator (CFTR) protein structure and the consequences of CFTR gene mutations have allowed the development of novel therapies targeting specific defects underlying CF. Some strategies are mutation specific and have already reached clinical development; some strategies include a read-through of the specific premature termination codons (read-through therapies, nonsense mediated decay pathway inhibitors for Class I mutations); correction of CFTR folding and trafficking to the apical plasma membrane (correctors for Class II mutations); and an increase in the function of CFTR channel (potentiators therapy for Class III mutations and any mutant with a residual function located at the membrane). Other therapies that are in preclinical development are not mutation specific and include gene therapy to edit the genome and stem cell therapy to repair the airway tissue. These strategies that are directed at the basic CF defects are now revolutionizing the treatment for patients and should positively impact their survival rates.
A New Algorithm for Line Detection Based on Relative Maximum Point

In this paper, a new algorithm for line detection was proposed. It finds out both ends of a possible straight line by searching the relative maximum points on closed envelope of Canny edge, then obtains the fitting variance of the possible straight lines through least squares fitting, and at last detects the straight lines in images through the ratio between fitting variance and line length as the criteria of straight line determination. Experiments show that compared with traditional methods such as PPHT and LSD, the method has higher precision and faster detection speed.
Tightly-Coupled Visual-Inertial Localization and 3-D Rigid-Body Target Tracking

In this letter we present a novel method to perform target tracking of a moving rigid body utilizing an inertial measurement unit with cameras. A key contribution is the tightly-coupling of the target motion estimation within a visual-inertial navigation system (VINS), allowing for improved performance of both the processes. In particular, we build upon the standard multi-state constraint Kalman filter -based VINS and generalize it to incorporate three-dimensional (3-D) target tracking. Rather than representing the target object as a moving point particle (which is often the case in the literature), we instead utilize a dynamic, 3-D rigid-body model, wherein orientation, position, and their derivatives are estimated, as well as the structure of points on the object. We then leverage visual bearings to this set of features for target motion estimation, rather than requiring continuous observation of a single representative point over the tracking period. Moreover, we propose three motion models which capture most commonly-seen tracking scenarios in practice such as UAVs, fixed-wing aircraft, and ground vehicles over changing slopes and perform an observability analysis with geometric interpretation, providing insights into parameter initialization, and modes of estimation drift. The proposed estimator is validated with both Monte-Carlo simulations and real-world experiments where it is shown to offer accurate performance even for challenging trajectories that do not completely fit the selected model.
Neuroimaging of Sudden Unexpected Death in Epilepsy (SUDEP): Insights From Structural and Resting-State Functional MRI Studies

The elusive nature of sudden unexpected death in epilepsy (SUDEP) has led to investigations of mechanisms and identification of biomarkers of this fatal scenario that constitutes the leading cause of premature death in epilepsy. In this short review, we compile evidence from structural and functional neuroimaging that demonstrates alterations to brain structures and networks involved in central autonomic and respiratory control in SUDEP and those at elevated risk. These findings suggest that compromised central control of vital regulatory processes may contribute to SUDEP. Both structural changes and dysfunctional interactions indicate potential mechanisms underlying the fatal event; contributions to individual risk prediction will require further study. The nature and sites of functional disruptions suggest potential non-invasive interventions to overcome failing processes.
Delft University of Technology Numerical Simulations of IBC Solar Cells Based on Poly-Si Carrier-Selective Passivating Contacts

This work presents an analysis of physical mechanisms related to operation and optimization of IBC polysilicon based devices. Concepts of carrier selectivity and tunneling are used to identify parameters that impact on the fill factor. Then, based on TCAD numerical simulations, we describe the device performance in terms of transport and passivation. A validation of the model is performed by matching measured and simulated R, T and EQE spectra and electrical parameters. As result of such process, the opto-electrical losses of the reference device are identified. Then, we execute a study of the impact of process parameters on the performance of the IBC device under analysis. Assuming uniform SiO2 layer, simulation results reveal that both n-type and p-type poly-Si contacts can be theoretically perfect (i.e. approx. lossless), if assuming no interface recombination but considering tunneling of both carrier types. In other words, there exists an optimum oxide thickness (1 nm) for which majority carriers tunneling works already very well, and minority tunneling is still low enough to not result in significant recombination. Moreover, SiO2 thickness up to maximum 1.6 nm is crucial to achieve high efficiency. Regarding rear geometry analysis, the efficiency curve as function of emitter width peaks at 70% pitch coverage. Further, it is shown that diffused dopants inside c-Si make the device resilient to passivation quality. Finally, the calibrated model is used to perform an optimization study aiming at calculating the performance limit. The estimated performance limit is 27.3% for a 100-μm thick bulk, 20-nm thick polysilicon layers, silver as rear contact and double ARC.
Reconfigurable On-Chip Mode Exchange for Mode-Division Multiplexing Optical Networks

Data exchange plays a key role in enhancing network resources utilization and optimizing network performance. In this paper, a data exchange device based on mode-division multiplexing (MDM) technology is proposed and demonstrated, which is capable of exchanging data information between a fundamental mode (TE<sub>0</sub>) signal and an arbitrary high-order mode (TE<italic><sub>x</sub></italic>) signal. We experimentally demonstrate the exchange between a TE<sub>0</sub> mode signal and a TE<sub>1</sub> mode signal using single microring resonator. The insertion loss is about 7.5 dB, including terminal coupling loss, the extinction ratio is more than 20 dB within the <italic>C</italic>-band. The crosstalk for the TE<sub>1</sub>-to-TE<sub>0</sub> and TE<sub>0</sub>-to-TE<sub>1</sub> mode conversions is measured to be less than −22 and −16 dB, respectively. Clear and open-eye diagrams of two different wavelength signals at 10 Gb/s are obtained. The proposed scheme is expected to be used for on-chip MDM networks in the future due to its scalability, compact size, and low power consumption.
A Weighted Multichannel Wiener Filter and its Decomposition to LCMV Beam Former and Post-Filter for Source Separation and Noise Reduction

Speech enhancement and source separation are well-known challenges in the context of hands-free communication and automatic speech recognition. The multichannel Wiener filter (MCWF) that satisfies the minimum mean square error (MMSE) criterion, is a fundamental speech enhancement tool. However, it can suffer from speech distortion, especially when the noise level is high. The speech distortion weighted multichannel Wiener filter (SDW-MWF) was therefore proposed to control the tradeoff between noise reduction and speech distortion for the single-speaker case. In this paper, we generalize this estimator and propose a method for controlling this tradeoff in the multi-speaker case. The proposed estimator is decomposed into two successive stages: 1) a multi-speaker linearly constrained minimum variance (LCMV), which is solely determined by the spatial characteristics of the speakers; and 2) a multi-speaker Wiener postfilter (PF), which is responsible for reducing the residual noise. The proposed PF consists of several controlling parameters that can almost independently control the tradeoff between the distortion of each speaker and the total noise reduction.
Improvement on the Interpersonal Relationship of the Elderly with Mild Cognitive Impairment by Using Speaker Recognition

This study aims to develop an elderly care system for improving the interpersonal relationship of the elderly with mild cognitive impairment (MCI) by employing the speaker recognition technique. The speaker recognition units based on the Gaussian Mixture Model (GMM) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) are implemented to identify the visitor via individual input utterance. Experimental results indicate that the speaker recognition unit based on GMM-UBM achieves the best performance.
Direct Trajectory Planning Method Based on IEPSO and Fuzzy Rewards and Punishment Theory for Multi-Degree-of Freedom Manipulators

Manipulator is a kind of commonly used multi-degree-of-freedom nonlinear system. There is a strong coupling between the links, and the movement of each link constraints and affects each other, which increases the difficulty of motion analysis of the system and reduces its trajectory planning efficiency under specific task targets. To solve this problem, a direct trajectory planning method based on an improved particle swarm optimization (PSO) algorithm, called IEPSO, and the fuzzy rewards and punishment theory is proposed in this paper. First, on the basis of preserving the local search ability of PSO, the global search ability of the population is improved by increasing a population exchange item. At the same time, in order to avoid the population falling into the local optimal value, the last elimination principle is incorporated into the standard PSO algorithm. Second, the fuzzy rewards and punishment theory is introduced to reduce the redundant decoupling operation, which can not only ensure the accuracy of manipulator trajectory planning but also effectively reduce the calculation amount of the trajectory planning for the multi-degree-of-freedom manipulator, to improve the optimization efficiency. Finally, the direct trajectory planning method of the multi-degree-of-freedom manipulator is compared and tested. It can be seen that the efficiency scalar and accuracy of the proposed direct trajectory planning method are significantly higher than those of other optimization methods.
Analog MIMO-RoC Downlink With SF2SF

Analog MIMO radio-over-copper is an alternative/complementary solution to conventional fiber-based mobile-fronthaul suitable for the last 200m with goal to reuse existing indoor LAN cables with remarkable economic benefits. Although LAN cables contain four twisted-pairs with up to 500MHz bandwidth/ea. , their usage is limited by cable attenuation and crosstalk among pairs. In this letter, we demonstrate by numerical evaluations that a judicious mapping of each radio-frequency signal of each antenna onto a combination of cable pair-frequency allocations, referred to as space-frequency to space-frequency multiplexing, minimizes the cable impairments. LAN cables bandwidth capabilities are thus exploited to evaluate the radio access requirements of 5G indoor networks.