Miracle-dz/UniFAR-FTUs huggingface dataset:
Next Generations Data Science Application and Its Platform

Data science can be depicted by extracting useful information from both structured and unstructured data. Data science has focused all the ventures of the current veracity necessarily. All the industries on the planet today that utilize data. In that capacity, Data science has become fuel for enterprises. There are different enterprises like banking, finance, manufacturing, transport, and healthcare, and agriculture that use data science. In this paper, we review the next generation’s data science application, platform, and dataset. Finally, we summarize the purpose and feature of data science platform. Furthermore, we have compared the different datasets concerning data science applications.
Acoustic Noise of Induction Motor With Low-Frequency Model Predictive Control

This paper studies the acoustic noise of induction motor with low switching frequency model predictive control and compares its noise performance with the traditional pulse width modulation with proportional-integral controllers. First, the relationship among the air-gap magnetic field, the electromagnetic force, and the acoustic noise is modeled to describe the influence of control algorithm on the noise characteristics of motors. As the counterpart with the traditional pulse width modulation control, a finite control set model predictive control strategy with low switching frequency is proposed. The steady-state performance and switching frequency constraint ability of the proposed model predictive control strategy are verified on the experimental platform. Second, the noise spectrums of these two control strategies are sampled and compared in different operating conditions, and the influence of the weighting factor on the noise spectrum is also discussed. It is proved by the experimental results that model predictive control has better noise performance than the traditional sinusoidal pulse width modulation under the same switching frequency and the total harmonic distortion of stator current.
Extended Boost PV inverter topology for the reduction of common-mode leakage current in three-phase applications

Photovoltaic (PV) inverters must not generate high levels of common-mode (CM) ground leakage currents, which may flow due to fast variations in their CM voltage. Moreover, a highly desirable feature for PV inverters is their voltage step-up capability, which enables operation with lower DC input voltage levels, thus extending energy harvesting over wider solar irradiance and temperature ranges. This paper presents a new three-phase Boost PV inverter topology and its modulation strategy, designed based on a new concept for suppressing CM leakage currents in three-phase PV inverters. In contrast to past-proposed three-phase step-up topologies, the proposed PV inverter uses a modified Boost converter instead of a transformerless inverter, as well as a modulation strategy generating high-quality PWM voltage. Due to their combined characteristics, the variation of the CM voltage generated by the proposed PV inverter is minimized, which radically improves its CM current suppressing capability. The structure and operating principle of the proposed topology are presented, and its effectiveness with respect to CM current suppression is demonstrated through simulation results in MATLAB/Simulink.
A Dual-Circular-Polarized Endoscopic Antenna With Wideband Characteristics and Wireless Biotelemetric Link Characterization

In this article, the first-ever dual-circular-polarized wideband conformal endoscopic antenna is designed at the medical implant communication service band (402 MHz) and industrial, scientific, and medical bands (915 MHz and 2.4 GHz). The proposed antenna with a small footprint of 8 mm3 (32 mm $\times 10$ mm $\times0.025$ mm) is printed on flexible polyamide and wrapped onto the inner wall of the capsule to spare the space for the capsule integrated components. For evaluation of the antenna’s performance within a full-package endoscopic device, a homogeneous muscle box and realistic Duke human phantoms were used. For measurement, the fabricated antenna is enclosed inside a 3-D-printed capsule along with the endoscopic system, and the enclosed system is waterproofed. The S-parameters and radiation patterns are measured in a saline-filled American Society for Testing and Materials (ASTM) model and a minced-pork phantom. The measured impedance bandwidths and gains at 402 MHz, 915 MHz, and 2.4 GHz are 236.2%, 104%, and 55.4% and −29.71, −28.7, and −20.8 dBi, respectively. The measured axial-ratio bandwidths are 45.9% and 21.3% at 915 MHz and 2.4 GHz, respectively. Furthermore, the link budget is numerically analyzed and measured via real-time telemetry using software-defined radios. The proposed antenna complies well with the human safety standards and satisfies the range and high data-rate requirements.
Research on Feature Extraction and Recognition Method of Underwater Acoustic Target Based on Deep Convolutional Network

Underwater acoustic target recognition technology is an important support technology for underwater acoustic information acquisition and underwater acoustic information confrontation. Its core includes target feature extraction and classification recognition. In this paper, deep convolution neural network is applied to feature extraction and classification of underwater acoustic target signal based on LOFAR spectrum. Firstly, the extraction method and preprocessing of LOFAR spectral features are introduced. Then, the applicability of deep convolution neural network in feature extraction and LOFAR spectrum of underwater acoustic target signal is briefly analyzed. Finally, the model construction and parameter optimization method of deep convolution neural network are explained in detail. Through analysis and research, this method has good applicability in the field of underwater acoustic target recognition.
Experimental EMI study of a 3-phase 100kW 1200V Dual Active Bridge Converter using SiC MOSFETs

This paper presents a system-level EMI investigation in a new 3-phase 1.2kV 100kW insulated DC/DC converter. In this prototype, achieving full operation was prevented by false triggering of semiconductors due to large common-mode current through gate drivers. A passive filter embedded into the structure of the transformer to connect it to the ground is proposed as a corrective and cost effective solution to mitigate the electromagnetic interferences. New grounding layout enabled to reduce the common-mode current through gate drivers by up to 75% without decreasing the switching rate. This allowed to proceed to measurements on the prototype at nominal operation. The method is validated in simulation and extensive experimental results have been obtained from the considered prototype. This proved the feasibility of the proposed solution and showed the effect of the transformer design in EMI behavior of insulated DC/DC converters.
Abundance and activity patterns of medium-sized felids (Felidae, Carnivora) In Southeastern Mexico

Abstract We compared abundance and activity patterns of medium-sized felids in the neotropics. We used 29 camera traps to record species in the region of Los Chimalapas, Oaxaca, Mexico, during 2011–2013. We estimated population size with the capture-recapture model (Cormack-Jolly-Seber model). We assessed the differences in activity patterns between species through the Mardia-Watson-Wheeler test. Leopardus pardalis, Leopardus wiedii, and Puma yagouaroundi were recorded by cameras. The population size of L. wiedii was high in comparison with L. pardalis, which presented a medium abundance compared to other regions. Puma yagouaroundi individuals were relatively rare, likely due to a difference in habitat selection. We provide evidence that in the southeastern region of Mexico there is a significant L. wiedii population despite the presence of L. pardalis.
Survey on State of Art IoT Protocols and Applications

Internet of Things (IoT) paradigm has created enormous growth in the current living environment through smart cities, Intelligent Transport System (ITS), digital health, smart homes, automated environmental monitoring, etc. In IoT based system, plenty of solutions have been developed to interconnect smart objects for systems with diverse scales and purposes. IoT platforms employ techniques and methods that do not completely satisfy the necessities of IoT. This paper presents a detailed review of current IoT protocols and its application to support the researcher and developers for selecting the appropriate protocols in IoT based environment. In this paper, a comparison of CoAP, MQTT, XMPP, Web Socket, DTLS, AMQP, MQTT-SN, DDS and REST is carried out. The architecture, merits, demerits and applicability of these protocols for IoT are discussed based on many factors like reliability, security, interoperability and power constraints. Lastly, a conclusion is derived based on the comparative study.
Tuning of LFC in Multi-source Electrical Power Systems Implementing Novel Nature Inspired MFO Algorithm Based Controller Parameter

Moth Flame Optimization (MFO) is one of the latest metaheuristic optimization algorithms used in this paper for finding the PID optimal controller parameters in Automatic Generation Control (AGC) of single area with hydroelectric, thermal and gas hybrid power generation units. The optimizer inspires the navigation method of moths in nature which is called as the transverse orientation. The effectiveness of this algorithm is compared with the recent published TLBO and DE optimization algorithm applied in the same field to obtain the controller parameters. The controller performance of the controller's performance is compared with other objective functions. Responses such as like overshoot of frequency output, power deviation of the tie line and time of settling are compared by changing the system parameters and load. It is observed that the system become comparatively more stable with the use of the proposed controller. The performance is found to be better in comparison to that of TLBO and Differential Evolution algorithm-based feedback controller with change in parameters of the system and loading.
System for Infant Cry Emotion Recognition using DNN

In general, a child, especially a neonate, requires the utmost care and attention of parents or elders. But nowadays, parents are very busy with their work and hardly find time to take care of infants. This may lead to physical and mental health issues for infants as well as their parents. The proposed system identifies the emotion of an infant through cry analysis. Infant cry signal vary according to emotion, in terms of pitch, formants, and energy. These variations can be represented by using various parametric features such as mel-frequency cepstral coefficients (MFCC). In this work, a deep neural network (DNN) classifier is employed for infant cry emotion recognition. Using different features, infant cries are categorized into three classes, namely, ‘hunger’, ‘sleep’ and ‘discomfort’. Apart from recording the infant cry, the proposed baby care system also monitors the body temperature, humidity of diaper and outdoor temperature by using appropriate sensors. These values and the recognized cry emotion are sent to the parent's mobile via the GSM module of Arduino UNO. The proposed system then activates immediate soothing measures such as playing music and/or playing parent's voice.
Modeling and control of an overactuated aerial vehicle with four tiltable quadrotors attached by means of passive universal joints

We present a novel overactuated aerial vehicle based on four quadrotors connected to an airframe by means of passive universal joints. The proposed architecture allows to independently control the six degrees of freedom of the airframe without having fixed propellers at inefficient configurations or making use of dedicated rotor tilting actuators. After deriving the dynamic equations that describe its motion, we propose a linear control strategy that is able to successfully decouple rotation and translation, relying exclusively on on-board sensors. A prototype is built and preliminary experimental results demonstrate that the concept is feasible.Video: https://youtu.be/9ASP3FyhCJw.
An approach for aggregation and historicization of production entities in the graph

A production system consists of multiple production entities. To enable a manufacturing process, these entities have to exchange information. Information models offer the possibility to standardize the data exchange between the entities. In addition to a common communication protocol, concepts that integrate and aggregate the different entities into a common address space, have to be developed. Another important issue is the historicization of information, for example, to improve the production process or for legal reasons. Graph databases enable semantic relations between objects for representation and storage of information. Therefore, it is a promising concept to be used in the production context. This paper presents a concept for the aggregation and historicization of production entities into a graph database.
Transfer Learning Based Fruits Image Segmentation for Fruit-Picking Robots

It is an important prerequisite for a fruit-picking robot to accurately segment and locate the object in fruit images. However, image segmentation by manually selected features or deep learning-based approaches is a troublesome task. It requires a long time and a large number of annotated images for the model to be trained. In this study, transfer learning is used so that the learned parameters of a pre-trained convolutional neural network can be used as the initial settings in the new task. Three networks, Mobilenet_v2, Resnet_v1_50_beta and Xception_65, are used as backbone networks, which were used in the well-known semantic image segmentation model—DeepLab. The proposed transfer learning-based fruits image segmentation not only alleviates the stringent need of a large image dataset, but also saves much time for training. Experimental results show that the Xception_65 based network has the best performance in terms of the segmentation metric of mean intersection over union. A high-precision instance fruits segmentation guarantees subsequent accurate locations of fruit images for fruit-picking robots, which is of great significance for intelligent agriculture.
Fixed-TimeAdaptive Neural Control for Strict Feedback Nonlinear Systems via Event-Triggered Mechanism

This article investigates an event-trigger-based fixed-time adaptive tracking control problem for strict feedback nonlinear systems with external disturbances. Relying on the values of the control input and tracking error, the relative fixed event-triggered control scheme is introduced to save communication resources on the basis of ensuring the control effect. Based on backstepping technology and neural network schemes, a fixed-time controller is devised to certify that the tracking error converges to a small neighborhood of the origin in the settling time. Meanwhile, all the signals of the closed-loop systems are bounded. The simulation results are presented to demonstrate the effectiveness of the proposed method.
Performance Analysis of New Family of Modified Hybrid ZSB DC-DC Converter

This paper presents a performance analysis of a new family of modified hybrid ZSB dc-dc converter. The output voltage of PV panels is very low therefore a high boost stage is often required. Conventional Z-source converter (ZSC) suffers from some limitations on boosting voltage so a new family of modified hybrid ZSB dc-dc converter is proposed. Different hybrid ZSCs are used to design a new family of modified hybrid ZSB DC-DC converter. In proposed converters, an additional switch and a diode is connected at the output terminal of traditional hybrid ZSCs, adds the benefit to gain. In contrast to the hybrid Z-source topologies, the high boost factor is realized with a small duty cycle $(0 < D < 0.125)$. It is observed that modified converters have high voltage gain about twice of their respective hybrid ZS boost converters. The performance of the proposed converters is compared with traditional Z-source converters and traditional hybrid Z-source boost converters. All results are validated by using MATLAB/SIMULINK R2016a.
Evaluation of dissolved gas analysis and long-term performance of non-edible natural ester

This paper aims at investigating the dissolved gas analysis (DGA) and long-term performance of Environtemp (FR3) and Jatropha Curcas oil (JAT). Thermal stress is applied to both oils in a sealed setup at 150°C for up to 2000 hours and samples are removed and tested at 500 hour intervals. It is observed that FR3 is more prone to stray gassing as compared to JAT. The alterations in the structure of the oils are studied by Fourier-transform infrared spectroscopy (FTIR) and 600 MHz nuclear magnetic resonance (NMR) spectroscopy analyses. The DGA interpretation is done using the Duval triangle, Dornenburg ratios, Rogers ratios and IEC methods. In the proposed study, a regression model for the two types of oils is developed to predict the total dissolved combustible gases with time. The findings show that JAT holds considerable properties of an insulating fluid and is a substitute for mineral insulating oils.
Application of Hybrid Integrated Model Based on Mechanism Model Residuals in Fiber Production Melt Conveying

The polyester fiber production process includes three sub-processes of polymerization, melt conveying and spinning. Aiming at the prediction of melt performance indicators in the process of melt conveying, this work introduces a hybrid integration model by integrating mechanism model and data-driven model. In the melt conveying process of fiber production, the data from the heat exchanger to the entire section of the spinneret were selected for comparative experiments. Comparing the hybrid integration model proposed in this paper with Gated Recurrent Unit (GRU), Support Vector Regression (SVR) and mechanism model which is called the first principle model, both MSE and MAPE evaluation indexes can reflect the superiority of hybrid integration model in prediction accuracy.
Enhancing the Sensitivity of Dielectric Sensors With Multiple Coupled Complementary Split-Ring Resonators

This article presents a new concept for sensitivity enhancement of dielectric sensors by loading a microstrip line with multiple coupled resonators. The concept is based on the interresonator coupling mechanism, which is represented as a mutual capacitance in an equivalent lumped circuit model. This mutual capacitance increases the sensitivity of the sensor (in terms of a shift in the resonance frequency) to detect the presence of dielectric materials (i.e., detecting changes in the real-relative permittivity of dielectric laminates). Complementary split-ring resonators (CSRRs) are utilized and coupled to a splitter-combiner microstrip section to design a 2CSRR and a 4CSRR sensor. The new sensors provided an appreciable enhancement in the sensitivity when detecting dielectric material. The concept is tested using full-wave numerical simulations to detect variations in the dielectric constant of a slab. Finally, full experimental validation is provided using fabricated sensors.
Worst-Case Delay Slicing for Time-Sensitive Applications in Softwarized Industrial Networks

The pervasiveness of network softwarization has drawn keen interest from industrial domain as network convergence becomes increasingly desirable amongst industrial operators. Coupled with the concept of network slicing, operators will be able to tailor resources to applications regardless of the contrast in application requirements. However, while the concept of slicing is prevalent in industrial network circles, the realization of the concept in practical deployment scenarios is rarely emphasized. This paper provides a step-by-step analysis for the creation of dynamic network slices to guarantee real-time deterministic requirements of periodic industrial applications. The objective of the paper is achieved by the design and integration of worst-case delay models in the virtual network embedding problem with a focus on the behavior of time-sensitive forwarding mechanisms introduced in standard Ethernet bridges. The result shows that the approach is able to guarantee delay requirements irrespective of the arrival order of slices.
Research on LTE-V2X Outfield Expressway Performance Testing Method in the 5.9GHz Band

As an end-to-end wireless communication channel for intelligent transportation systems, LTE-V2X requires efficient allocation of spectrum resources for its scientific and effective operation. In research of the Internet of Vehicles, frequency and compatibility verification of LTE-V2X systems operating in the 5.9GHz band is a hot topic of this research. The outfield expressway performance testing is a branch of this research. Whether the LTE-V2X system’s communication performance in the expressway meets the technical requirements is the prerequisite for the promotion and application of the LTE-V2X system in China. Aiming at this problem, this paper proposes a LTE-V2X outfield performance testing method in the 5.9GHz frequency band. In the testing environment of straight and curved expressways , the V2V / V2I link communication performance is tested separately. Finally, the testing data is analyzed to obtain static and dynamic testing results on the expressway.
Deep Integration and Topological Cohabitation of Active Circuits and Antennas for Power Amplification and Radiation in Standard CMOS

In this article, a densely integrated and unified paralleled amplifier and antenna architecture is proposed and demonstrated for next-generation millimeter-wave (mmW) and terahertz (THz) front-end modules. It makes use of topologically paralleled transistors cohabitated within a compact rectangular patch antenna (CRPA) as a unified cell. This design simultaneously amplifies and radiates power without resorting to any power divider, input and output matching networks, or power combiner whatsoever, therefore, greatly reducing front-end loss, size, and complexity. Then, the unified unit cell is self-consistently distributed to form a unified planar array with active elements between array elements as power feeders. In this demonstration, the input power is sequentially amplified, radiated through each array element, and spatially added up, resulting in low on-board circuit losses and efficient radiation. In this article, we investigate and present challenges and early results of implementing this unification concept of active circuitry and antenna at mmW frequencies. Using a standard 65-nm CMOS process, a set of chips were designed, fabricated, and tested under different amplifier biasing conditions at 146 GHz for experimental demonstration. The realized low-loss, matched, and compact unified prototypes have demonstrated an amplified radiation, with power enhancement of 3.4 dB through single element and 6 dB through $2 \times 2$ layout compared to their respective passive counterparts. Moreover, frequency tuning is observed varying drain bias and self-matching is improved with increasing gate bias. Therefore, the feasibility of a unique feature in the architectural implementation of low-loss, compact, and densely integrated and topologically cohabitated mmW and THz front-end modules is confirmed.