Miracle-dz/UniFAR-FTUs huggingface dataset:
The Experimental Model of the Cantilever Beam with Piezoelectric Patch Elements

This paper discusses the approach of transporting from a real test stand of cantilever beam containing a piezoelectric actuator and sensor into the Simulink model and at the same time use this model to design an observer to observe separated modes. The fixed end of the beam is excited using the kinematic exciter while Doppler Laser is used for calibration. Experimental identification is made by using the Discrete Fourier Transform and the Simulink model is made using the State Space modeling. The aim was to create a simple and easy to understand tool for testing, debugging and a better understanding of new control algorithms.
Covariance Mean-to-Standard-Deviation Factor for Ultrasound Imaging

We previously proposed mean-to-standard-deviation ratio-based adaptive weighting factors for ultrasound imaging. However, there exists a trade-off between side lobe suppression and speckle statistics preservation performances. The goal of this paper is to implement a new covariance mean-to-standard-deviation factor (CMSF) for ultrasound imaging. In CMSF, adaptive selection of subarray length and diagonal reducing are introduced to effectively reduce off-axis energy. Simulated and experimental data sets were used to evaluate the imaging performance of the proposed method. The quantitative results indicate that, compared with RSMSF, CMSF $(\delta=\mathbf{1})$ leads to average improvements of 50.8%, 28.4%, 19.4%, and 15.0% in resolution, contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and speckle signal-to-noise ratio (speckle SNR), respectively. Simulation and experimental results suggest that CMSF has the potential to improve the lateral resolution and maintain the speckle reliably.
Bimodal-based Object Detection and Instance Segmentation Models for Substation Equipments*

Detection and segmentation of the substation equipments is the important first step towards establishing an AI-based thermal fault detection of substation equipments. The traditional detection and segmentation methods have been built up based on the single mode of thermal or visible light image. In this paper, we propose the framework of bimodal fusion: the visible-light images and the temperature map, to establish the deep neural network models for object detection and instance segmentation of the substation equipments, based on the Mask R-CNN. In our private fused dataset, we realize and compare diverse fusion methods, including the pixel-based fusion, feature-based fusion and decision-level fusion methods for the detection and segmentation task of substation equipments. The comparison experiments shown that the FPN feature layer fusion model in the feature-based fusion can achieve better detection and segmentation effects than the others and the models of the single mode. We also demonstrate that the fused method can slightly improve the performance in the night scene by simulation. However, the improvement of performance measured by the mAP and AR of these method are all slight.
Advantages of Arrowhead Framework for the Machine Tooling Industry

Immersed in the digital era and fully experiencing the changes introduced by the new industrial revolution of the so-called Industry 4.0, there are still many aspects of industrial digitization to resolve. Interoperability among devices and machines is one of the challenges. Sensors, components and machines from different vendors work as independent silos offering large amounts of heterogeneous data which relational capabilities are not fully exploited. Quick development, deployment and testing of new software solutions that take advantage of those data is another important matter. The requirements in terms of equipment resources and engineering efforts is high when planning new implementations. Platforms that enable the efficient application of those solutions at the right level (machine, edge, plant or cloud) are also necessary. (p)(/p)This paper presents an industrial case study on the application of the Arrowhead framework. The framework is implemented in the Machine Tooling ecosystem and enables the integration of grinding machines with other sensors, components or machines. Different software engineering tools offered with Arrowhead are used to design new solutions in Cyber-Physical System and Internet of Things in Industry 4.0 and make them Arrowhead compliant, for fast deployment of platforms and applications (Dockers) or for testing purposes (Management tool). Finally, the potential of agile construction of new applications is analysed by providing an Human-Machine Interface at machine level and the provision of services for data consumption at cloud level.
A Novel Incipient Fault Diagnosis Method for Analog Circuits Based on GMKL-SVM and Wavelet Fusion Features

To enhance the reliability of analog circuits in complex electrical systems, a novel incipient fault diagnosis method is presented in this article. The wavelet packet feature quantities, which consist of the energy, fluctuation coefficient, skewness, and margin factor, are obtained via multiscale time-frequency analysis with wavelet packet transform (WPT). Then, generalized discriminant analysis (GDA) is employed to realize the fusion of wavelet packet feature quantities because it can handle the data nonlinearity and eliminate redundant information. Furthermore, the generalized multiple kernel learning support vector machine (GMKL-SVM), which has the advantages of a strong generalization ability and high accuracy, is developed to identify the incipient fault classes of analog circuits. Moreover, a new particle swarm intelligent optimization algorithm, the sine cosine algorithm (SCA), is adopted to optimize key parameters of GMKL-SVM because of its high convergence speed and strong global optimization ability. The method is fully evaluated with the Sallen-Key bandpass filter circuit, the four-op-amp biquad high-pass filter circuit, and the leapfrog filter circuit. The experimental results demonstrate that the proposed incipient fault diagnosis method for analog circuits can produce higher diagnosis accuracy than other typical incipient fault diagnosis methods.
Methodology for Risk Assessment based on Grey Relational Analysis: A Case Study in the Automotive Industry

To determine whether hazards are allowable or not, risk assessment is crucial for occupational safety. In this study, a methodology is developed for risk assessment. There are three stages in this developed methodology. At the first stage, 25 process steps are considered where hazards occur in examined automotive industry. At the second stage, grey relational analysis method is used to assess risk. Finally, at the third stage, different weights assessments were performed to determine how much weight changes can impact the sorting results. The results show that the developed methodology has good potential for sorting hazards and it can be applied in many different areas.
DiSA: A Display-driven Spatial Analysis Framework for Large-Scale Vector Data

We present DiSA, a Display-driven Spatial Analysis framework for interactive analysis of large-scale geographical vector data. DiSA calculates visualization of analysis results directly using a parallel per-pixel approach with efficient fine-grained spatial indexes. Compared with conventional object-based methods, DiSA can greatly reduce the computational complexity (from O(n) to O(log(n)) in some cases), making it less sensitive to data volumes. Experimental results verify that DiSA can provide analysis of billion-scale spatial objects in milliseconds. We demonstrate DiSA with various application scenarios including raw data exploration, spatial buffer and overlay analysis, and global cellular signal strength analysis. Users can explore 10 millions of spatial objects, adjust algorithm parameters, and always see the results in real-time on a personal computer.
A Review and Comparison of Lithium-Ion Battery SOC Estimation Methods for Electric Vehicles

In this research, a Thevenin battery model is proposed considering the hysteresis effect. A general comparison of model-based methods is followed by simulations with different State-of-Charge (SOC) estimation methods i.e., Coulomb Counting, Open Circuit Voltage, Extended Kalman Filter and Adaptive Extended Kalman Filter, Luenberger Observer and Neural Networks based on the same dataset. The results show that the proposed battery model can accurately provide SOC estimation and Adaptive Extended Kalman Filter can provide SOC estimation even with false initialization. Neural Networks deliver a slightly more accurate prediction. However, the requirement of large memory storage can be restricted for some applications.
A Domain Expertise and Word-Embedding Geometric Projection Based Semantic Mining Framework for Measuring the Soft Power of Social Entities

Social science research (communication studies in particular) often involves measuring the soft power of social entities from the related big data of media texts. This work proposes a soft power measurement framework that integrates the domain-expertise-driven conceptualization of the traditional social science paradigm and the data-driven operationalization of the computational social science paradigm. Based on the geometric projections of word vectors from the social entity keywords to the soft power dimension keywords, the weights of social entity keywords in the full-sample media texts are projected to the corresponding soft power dimensions in the high-dimensional word-embedding semantic space and accumulated to the total and dimensional values of the soft power of social entities respectively. We demonstrate the applicability, compatibility, computational complexity, validity, adaptability, and scalability of the proposed framework by showing a standard instantiation (measuring the national image carrying capacity of Chinese elite private entrepreneurs from the full-sample WeChat articles, each of which has more than 100,000 unique views) and a variational instantiation (measuring the city brand influence of global cities from the big data of the Global News Knowledge Graph in the Global Database of Events, Language, and Tone (GDELT) of Google) of the proposed framework.
Inaccurate Switching Loss Measurement of SiC MOSFET Caused by Probes: Modelization, Characterization, and Validation

SiC metal–oxide–semiconductor field-effect transistor (MOSFET) has a fast switching speed and high slew rate. However, its ultrashort switching time approximates the rise time and propagation delay of the measurement instruments, which results in an inaccurate assessment of the switching loss and challenges the thermal design of the power converter. In this article, aiming to reveal the principles of accurate measurement for the switching behavior of SiC MOSFET, insightful models are proposed for baseline probes and transient trajectories to characterize the measurement error of the switching losses. By using the Gaussian function, the mathematical models for the rise time, bandwidth, and propagation delay of the measurement instruments are achieved, which is also confirmed by the surveyed specifications of commercial probes. Concerning the accurate measurement, the turn-on and turn-off losses of the SiC MOSFET influenced by the rise time and propagation delay of probes are comprehensively modeled and characterized. With respect to different current probes, voltage probes, and gate driver resistances, extensive experiments are demonstrated to confirm the validity of the proposed models. The experimental findings are in line with the conducted predictions of the proposed models. It is found that, due to the very fast switching transients of the SiC MOSFET, the limited bandwidth and inevitable propagation delay of measurement instrument may result in a prominent error of the switching loss and impede the widespread implementation of the SiC MOSFET.
Improved Torque Density of a Permanent Magnet Brushless AC Motor with Novel Pulse Width Modulation Magnet for Electrified Application

Researchers have recently attached more attention on permanent magnet brushless AC motors for electrified propulsion. Considering the further improvement of torque density, this paper proposes a novel pulse width modulation (PWM) magnet array and quantitatively analyzes the magnetic field with different magnet configurations. It is a good potential for reducing the magnet volume. Based on a slotless motor design which eliminates the influence of additional harmonic sources except for that from magnets, investigations on motor performance with proposed magnet array are made. Also, the improvement of torque density is discussed and verified.
A Survey of Smart Buildings and Homes using Low-Power Wide-Area Network (LoRa WAN)

Living in the age of internet, life has become smarter and more convenient for us today. Smart home automation is the process which all the household appliances can be controlled, monitored and maintained automatically by the intelligence of device far from home regardless is typically called home automation which makes home smart. It connects everything in the network through internet. The idea of house being totally mechanized used to be numerous. Presently, it is a reality. Shrewd homes and penetrating home items are prominent in light of the fact that they offer more accommodation and security for family. User can check the update status of a device time to time. These gadgets can convey, track and send data, and react according to user command. In this technology we can control household appliances like lighting, AC, fan, washing machine, TV, Fridge, security system etc. By using artificial intelligence, it can also be self-controlled and monitored by itself. These frameworks additionally help increment home’s vitality effectiveness, which can lessen vitality bill. This survey depicts the smart home and building automation in terms of energy management by using systematic usages of home appliances with low power wide area network. It is a novel proposed approach which we reviewed at minimum cost system to determine an authentic smart home by using its intelligent management of home appliances in efficient manner. Home mechanization framework includes a lot of system and diverse technology with low power wide area network.
Aviation Scenarios for 5G and Beyond

Aviation (both manned and unmanned) offers many novel application scenarios for 5th and 6th (XG) generation mobile networks. For drones (Unmanned Aircraft), navigation in challenging environments, such as urban with tall buildings with GNSS satellite position is problematic. XG could offer positioning as a free add-on thanks to advanced radio techniques such as beam-forming. Therefore, it could provide energy-efficient positioning service for UAs. Furthermore, drones could be used as portable base stations or relay points to rapidly deploy a network, for example in natural disaster environment. With proper signal direction, XG base stations can also provide Internet connectivity to passengers and planes over ground, to save expensive satellite communications. It could be even possible for messages to cross oceans with hop-by-hop data relay between airplanes. Finally, aviation itself includes the need for data exchange between pilots, controllers and plane manufacturers. Current air traffic communication systems are very slow, unreliable and suffer from a complete lack of security. Therefore, XG could be an excellent replacement by providing ultra-reliable and authenticated data exchange in critical scenarios, such as pilot and plane communication during landing. In this paper, we perform a literature survey to explore possible XG scenarios for aviation, with special focus on security.
The use of Multiwall model for reference RSSI data preparation for indoor localization purposes

The article discusses the suitability of the multiwall radio wave propagation model for RSSI reference data preparation for fingerprinting-based indoor positioning applications. Localization system employs Bluetooth Low Energy beacons and mobile device to determine position of the user within the test area. The proposed system uses particle filter algorithm to estimate the user's current position from RSSI measurements and knowledge of reference electromagnetic field distribution computed for each beacon located in the building. The results of the comparison between the use of multiwall and ray tracing propagation models for indoor localization purposes has been shown. The relation between the time needed to prepare mentioned reference data was also demonstrated. Presented results show that for some cases it is possible to use less complex propagation models without losing sufficient localization accuracy but for the benefit of the time and complexity of calculations needed to prepare reference data for presented indoor localization application.
Thermal Insulating and Mechanical Properties of Cellulose Nanofibrils Modified Polyurethane Foam Composite as Structural Insulated Material

Cellulose nanofibrils (CNF) modified polyurethane foam (PUF) has great potential as a structural insulated material in wood construction industry. In this study, PUF modified with spray-dried CNF was fabricated and the physical and mechanical performance were studied. Results showed that CNF had an impact on the foam microstructure by increasing the precursor viscosity and imposing resistant strength upon foaming. In addition, the intrinsic high mechanical strength of CNF imparted an extra resistant force against cells expansion during the foaming process and formed smaller cells which reduced the chance of creating defective cells. The mechanical performance of the foam composite was significantly improved by introducing CNF into the PUF matrix. Compared with the PUF control, the specific bending strength, specific tensile strength, and specific compression strength increased up to three-fold for the CNF modified PUF. The thermal conductivity of PUF composite was mainly influenced by the closed cell size. The introduction of CNF improved thermal insulating performance, with a decreased thermal conductivity from 0.0439 W/mK to 0.02724 W/mK.
Expansion and Superposition of Switching Cycles to generate Simulation Datasets for NILM

Datasets are essential for the development of new algorithms in the research area of Non-Intrusive Load Monitoring. Therefore, a method will be investigated to generate simulation datasets based on real measurements. For this purpose, a method is developed using the FIT-PS transformation to extend switching cycles of devices to any length. It is investigated how well the recorded signals can be reconstructed from individually composed switching cycles. Then, signal expansion and superposition are used to generate simulation datasets with different degrees of complexity, which are available under the name HELD2 datasets. The first benchmarks for event detection and classification conclude the paper.
Ambient RF Energy Harvesting System Based on Wide Angle Metamaterial Absorber for Battery-Less Wireless Sensors

In this paper, a wide – angle rectenna based on metamaterial absorber is proposed. By using the absorbing surface consists of multiple periodically arranged unit cells, the proposed ambient RF energy harvesting device can power sufficient DC power for a wireless sensor from the ambient RF sources in environment. The rectifiers employed in this rectenna system are optimized to have a RF-DC conversion efficiency of 38% and 50% at very low input power of -10 dBm and -5 dBm, respectively. The symmetric unit cells are designed to yield a high absorptivity at wide incident angle in both H plane and E plane and by attaching two rectifiers for each one of them, the device is able to achieve a wide – angle dual – polarized operation up to 140 degrees. Numerical simulations are conducted to validate the design while a comparison to previously introduced rectennas is also provided to elucidate the advantages of the ambient RF energy harvesting system in this work.
Naturally occurring recombinant isolate of Pea seed-borne mosaic virus – Short Communication

Whole genome sequences of three Czech Pea seed-borne mosaic virus isolates belonging to P1 pathotype and causing different symptom intensity were obtained. Using RDP4 analysis the natural recombinant isolate PSB204CZ bearing two breakpoints in nucleotide positions 4053 and 6080 was identified. The isolate was composed of fragment 2028 nt in length partially covering CI and 6K2 regions of the minor parent (PSB262CZ) incorporated into the major parent (PSB194CZ). The results suggest that the observed recombination in CI-6K2 region is responsible for severity of developed symptoms. This observation detected for the first time natural recombination within PSbMV isolates of an important pathogen of leguminous plants.
Robust Terminal Sliding Mode Tracking Control for Flexible-Joint Robots

A robust trajectory tracking control problem for robots with flexible-joint (FJ) is presented in this work. Aiming to obtain a better trajectory tracking performance and reject the adverse impacts of unknown matched and mismatched time-varying disturbances on FJ robotic systems, a robust finite-time control approach based on a terminal sliding mode control (TSMC) method and two finite-time observers (FTOs) is proposed. First, the FTOs are utilized to estimate the matched disturbance and the mismatched disturbance, respectively. With the aid of these estimations, a novel terminal sliding surface is constructed. Then, the proposed robust TSMC scheme is developed, which possesses a property against the matched and mismatched time-varying disturbances in a finite-time. The results are given to verify the effectiveness of the proposed control approach.
Raising the Abstraction Level of a Deep Learning Design on FPGAs

Autonomous and intelligent systems based on deep learning, continuously attract the attention of researchers and engineers. With the progress on the application of deep learning for modern applications arises the challenge of reaching real-time processing. To face this challenge, Field Programmable Gate Arrays (FPGAs) can be used; however, deep learning generic implementations on an FPGA are still a topic of research. Advances in FPGA technology allow for designs based on High-Level Synthesis (HLS) for accelerating and facilitating implementations of complex problems on hardware. A platform based on HLS for emulating a generic parameterizable deep learning system on an FPGA is proposed in this paper, allowing for the implementation of any structure based on the following layers: convolution, max-pooling, batch-normalization, and fully connected networks. Through this platform, it is possible to implement a deep learning system on FPGAs using an N-Fold or a Flow architecture without the assistance of central processing units. Whereas the N-Fold architecture requires fewer hardware resources, as it re-uses resources, the Flow architecture presents a higher throughput. The developed platform improves the deep learning design productivity by automating the generation of the system, achieving efficiency and raising the level of abstraction, as was experimentally verified and evaluated.
A Modified Full Bridge Converter with Magnetic Integration Technique

In this paper, a full bridge converter based on magnetic integration technique has been presented. The input inductor, output inductor and the high frequency isolation transformer of the proposed converter are integrated into one sole magnetic element. Through the analysis, the magnetic integration technique not only improves the power density but also increases the dynamic response of the bridge converter. A 36-V output voltage, 1-kW full bridge converter has been built and tested. Experimental results demonstrated the effectiveness and advantages of the proposed converter. Meanwhile, the similar magnetic integration technique can be widely used in various types of high frequency isolation DC-DC converter topologies.