Miracle-dz/UniFAR-FTUs huggingface dataset:
Cucker-Smale Flocking Control of Leader-Follower Multiagent Systems With Intermittent Communication

This paper investigates the Cucker-Smale flocking control problem of leader-follower multiagent systems in the presence of periodic intermittent communication, in which networked agents update the states by communicating with their neighbors during active periods and remain stable during dormancy periods. A distributed update algorithm that relies on the position error between agents is designed. Based on this algorithm, the problem of Cucker-Smale flocking with periodic intermittent communication is first transformed into a convergence problem of infinite products of nonnegative matrices. Then, this convergence problem is theoretically solved by using hybrid tools including nonnegative matrix analysis and graph theory. Finally, a sufficient condition, which relates to the weight function and the period length of intermittent communication is established. Moreover, numerical examples are presented to demonstrate the validity of the theoretical result.
Beam steering in a Half-Frequency driven Airborne CMUT transmitter array

An airborne Capacitive Micromachined Ultrasonic Transducer (CMUT) transmit array was designed using electromechanical modelling for unbiased airborne operation. The array elements are designed for maximum swing at 10V p-p unbiased drive, whereas conventional practice is to bias CMUT close to the collapsed voltage to achieve higher swing. The devices were fabricated using a customized single photolithographic process with a combination of wet and dry etching. The wafer level fabrication enabled the usage of 2x2 and 3x3 arrays. Driving CMUTs in an unbiased mode at half frequency drives the ‘static pressure’ depressed silicon membrane at a larger swing without letting it collapse. The 2x2 array displays 3.375 kHz bandwidth when characterized in air. The phase and amplitude differences due to the dispersion of resonance frequencies of the elements are compensated for beamformed and beamsteered airborne operation.
GWINs: Group-Based Medium Access for Large-Scale Wireless Powered IoT Networks

Internet of Things (IoT) provides a framework for interconnecting, controlling and monitoring of real-world objects and smart-devices through sensors. The sensors need energy sources to continuously operate. Since the design and the size constraints may impede a sensor node to have a distinct power source, the Energy Harvesting (EH) techniques emerge to cope with the energy constraint problem. Massive IoT networks with the Radio Frequency (RF) EH capability requires new accessing methods to reduce or mitigate interference and collisions. In this paper, we propose a group-based Medium Access Control (MAC) protocol, where a group head coordinates the corresponding nodes. The group heads working as Hybrid Access Points (HAPs) provide nodes with power and relay their data to the base station (BS). The HAPs use a distributed coordination function (DCF) to access the channel, along with in-group coordinating mechanism. In a group, nodes reduce collisions by using a frame slotted ALOHA to access the medium, with the addition of the EH mechanism. Nodes utilize the harvest-transmit-and-sleep protocol to harvest energy, to transmit information utilizing the harvested energy and to sleep when channel is inactive. We study channel models for different communication environments influencing the usability of RF energy harvesting and examine their impact on the network performance. We aim to mitigate inter-group and intra-group collisions and to provide an energy harvesting and data relaying mechanism in a distributed manner. Performance evaluations are carried out through extensive simulations and the model and analysis are verified. It is shown that the analysis results match well with those of simulations. The proposed distributed group-coordination mechanism achieves significant network performance gain over the conventional schemes. The effectiveness of the proposed scheme is shown by the network throughput, and the energy efficiency.
Targeted Next-generation Sequencing of Circulating Biomarkers in Non-Small Cell Lung Cancer (NSCLC)

In NSCLC, EGFR mutations are highly actionable; activating mutations (p.L858R, exon 19 deletions) confer sensitivity to tyrosine kinase inhibitors (TKIs) while p.T790M mutation confers resistance to TKIs. As an alternative to invasive methods of patient mutational profiling (tumour biopsy, fine needle aspiration cytology), variants detected from blood plasma samples of advanced lung adenocarcinoma patients were characterised using next-generation sequencing (NGS). This is in accordance with promising research into liquid biopsy and high throughput sequencing. The sensitivity and specificity of three assays in detecting EGFR mutations were evaluated and compared: (i) quantitative polymerase chain reaction (qPCR) assay developed in-house, (ii) NGS AmpliSeq Lung and Colon Panel assay, and (iii) NGS Oncomine Lung cfDNA assay. With patient tumour biopsies as the gold standard, the Oncomine assay was shown to attain the highest sensitivity (>60%) across all three EGFR mutations while the AmpliSeq assay had the greatest specificity (100%). NGS also enabled the detection of non-EGFR variants (AmpliSeq: n=11; Oncomine: n=12) across 11 genes due to its high throughput capabilities. In the use of liquid biopsy, cell-free DNA (cfDNA) was shown to be more useful biomarkers than circulating tumour cells (CTCs). The merits of NGS in observing and enhancing patient treatment were validated with the detection of variants such as BRAF p.V600E with existing or developing targeted therapies. When liquid biopsy sequencing results were compared with corresponding clinical status derived from tumour biopsy, additional p.T790M mutants were detected across all three assays. Alongside observations on monitoring of EGFR mutations within three sets of serial samples, the potential for NGS to be used as a non-invasive tool for monitoring of patient treatment response to EGFR TKIs was demonstrated. Next-generation sequencing; cell-free DNA; liquid biopsy; nonsmall cell lung cancer (NSCLC); circulating tumour cells
Network Calculus-based Modeling of Time Sensitive Networking Shapers for Industrial Automation Networks

With the interconnection of distributed devices from different industrial automation networks, heterogeneous traffics with different Quality of Service (QoS) requirements increase dramatically. Shapers in Time-Sensitive Networking (TSN) enable the coexistence of different industrial traffics within a single network infrastructure. In this paper, with full consideration of the time-criticality of different industrial traffics and shaping mechanisms, we propose a network calculus-based modeling scheme for the widely-used Credit Based Shaper (CBS) and Time Aware Shaper (TAS). Then, we analyze the QoS of industrial traffics with different real-time characteristics by CBS and TAS, respectively. To evaluate the accuracy of network calculus-based modeling scheme, we compare our theoretical analysis with simulations, wherein a compactness function is defined to describe QoS compactness. Simulation results verify the effectiveness of proposed modeling scheme, by which we further validate both CBS and TAS can satisfy the QoS of traffics for different industrial automation scenarios.
Model Predictive Control for DFIG-based Wind Turbines with NPC Voltage Source Converter

This paper deals with high power wind turbines with doubly-fed induction generators and investigates the Model Predictive Control (MPC) approach for a power converter system with three-level Neutral-Point Clamped (NPC) topology. The modeling of each system component is presented. A simulation study provides insight on the operational performance of the implemented MPC, highlights both, its advantages but also its challenges. The most commonly applied field oriented control is given as a comparative reference. Selected performance indicators are determined for the entire operation range and show, that the MPC scheme in its simplest form can operate with comparatively low average switching frequencies to relax thermal loading in the power converter system, although a compromise need to be made with respect to mechanical loading and power quality issues. MPC still has great potential in improving overall operational performance.
Trajectory and Beamforming Vector Optimization for Multi-UAV Multicast Network

In this paper, we consider a multiple unmanned aerial vehicles (UAVs)-enabled multicast network, where each UAV is employed to send common messages to its corresponding group users via beamforming technology. Due to the simultaneous information transmission, the users may also receive the interference signals from other UAVs. The objective of this paper is to maximize the sum time-averaged multicast rate by jointly optimizing multiple UAV trajectories and beamforming vectors. The optimization problem is non-convex and too complicated to be solved directly. In light of this, an iteration algorithm is applied, which alternately optimizes variables to decompose the coupling problem. Finally, the optimized UAV trajectory compared with the pre-defined circular trajectory is presented. Numerical results imply that the proposed optimization algorithm gains larger sum-multicast rate than the circular trajectory scheme and static scheme.
Two-Dimensional Observation of Single Cathode Spot Copper Vapor in Vacuum Arcs by Laser-Induced Fluorescence

Vacuum arc cathode spots are centers of metal vapor evaporation, electron emission and plasma production. Arc properties are dominated by the behavior of the cathode spots. A systematic investigation on the density of copper metal vapor emitted from a single cathode spot is benefit for understanding the mechanism of vacuum arc discharge. The objective of this paper is to get the spatial density distribution of copper neutral atoms emitted from a single cathode spot in ~55A vacuum arc by planar Laser Induced Fluorescence (LIF) method. The experiments are conducted with CuCr10 (Cr weight of 10%) butt contacts in a demountable vacuum chamber. 2D distribution of copper metal vapor density is photographed by an intensified CCD cameral (i-CCD) with an exposure time of 50 ns. The uniform constant Axial Magnetic Field (AMF) within the inter-contacts region is supplied by Helmholtz coils. 2D copper vapor density distribution between two electrodes as well as the influence of external AMF on vapor density distribution are investigated. When the arc current is ~55 A, a typical cathode spot or two spots are observed in the experiments. Copper vapor density reaches the maximum value 6×1019 m-3 in the center of cathode spot.
A Multi-level DC-Link Extremum-seeking PI Controller for a PMSM with Low Inductance

In this paper, an adaptive PI controller using a multi-level DC link (MLDCL) inverter is proposed and applied for angular position trajectory tracking in a permanent magnet synchronous motor (PMSM) with relatively low stator inductance and resistance. Modern lightweight motors have smaller resistance and inductance values when compared to conventional motors, which give rise to higher levels of current ripples when using large PWM frequencies. To alleviate the problem, the proposed controller combines the merits of a multi-level DC link inverter with a multivariable sliding-mode extremum seeking tuned PI controller to achieve fast and precise trajectory tracking. The extremum seeking tuning method adjusts the PI gains in realtime which makes the system tracking performance more precise. The MLDCL inverter used in this paper reduces the PMSM current ripples of the three-phase inverter through utilizing multiple half-bridge cells instead of an individual DC source used in traditional pulse-width modulation (PWM) inverters. The tracking performance of the proposed control scheme is investigated through simulations by comparing the results with a recently proposed fixed-gain PI controller using a traditional PWM inverter. The results demonstrate the effectiveness of the proposed controller in achieving lower torque ripples and more precise tracking response.
A Three-Stage Stackelberg Game for Secure Communication with a Wireless Powered Jammer

This paper investigates physical layer security of a pair of legitimate source-destination nodes in the presence of an eavesdropper. Specifically, the source-destination pair hires a wireless powered jammer to interfere with the eavesdropper for improving the secrecy outage performance, and then the wireless powered jammer hires a power station for broadcasting energy. We model the interactions among the source-destination pair, the jammer and the power station as a three-stage Stackelberg game. In particular, the power station who acts as a leader prices the energy transmitted to the jammer, the jammer who acts as a follower of the power station decides the required transmit power of the power station and prices the interference energy caused to the eavesdropper, and the source-destination pair who acts as a follower of the power station and the jammer decides the required jamming power and the time allocation. An algorithm based on backward induction is proposed to achieve the Stackelberg equilibrium, and its effectiveness is verified by simulations. The proposed algorithm is shown to achieve much lower secrecy outage probability while consumes lower transmit power of the power station compared to the benchmark algorithm.
Robustly Predicting Pedestrian Destinations Using Pre-trained Machine Learning Model for a Voice Guidance Robot*

In this paper, we propose a method robustly predicting the destination of a pedestrian heading toward a robot in order to provide suitable voice guidance to him/her by communication robots installed at the reception desks of public facilities. For this purpose, we measure a pedestrian trajectory with a laser range scanner attached to the robot, and predict the destination among more than three branches by cascading multiple predictor models for two branches pre-trained by a machine learning algorithm. In order to verify the effectiveness of the proposed method, we conduct experiments using a dataset of tracking pedestrians at a shopping mall, and data observed in the real environment. The result shows that our method can predict three branch destinations with an accuracy of about 80%.
Quality Assessment of 980 nm GaAs Based Laser Diodes with Use of Low-Frequency Noise Measurements

Low-frequency noise has always been a fast and non-destructive tool to characterise the performance and quality of materials and electrical devices. In this paper, a nondestructive method of predicting reliability was introduced for 980 nm GaAs based semiconductor laser diodes. Measurement and analysis were carried out for the noise and transport characteristics of forward voltage biases. The results demonstrated a close relationship between LD quality and the characteristic parameters of low-frequency noise such as frequency exponent, noise intensity and amplitude.
Design and Analysis of a Spoke-type Synchronous Motor with Ferrite Magnets for Home Appliance Applications

Due to relatively lower price of ferrite magnets, and the flux concentration effects, spoke-type synchronous motors with ferrite magnets become more and more attractive, especially for applications with a wide speed range. In this paper, several topologies of spoke-type rotor for home appliance applications are investigated and compared with each other. Mechanical performance and irreversible demagnetization resistance are taken into consideration, as well as mass production convenience. After that, the final design is selected. The results show that the final design has a sufficient performance for home appliance applications.
Foreign body recognition for coal mine conveyor based on improved PCANSet

Due to the high noise and complicated scene of the visual scene of the coal mine conveyor belt, the foreign body recognition for the conveyor belt image needs to be more efficient. In this paper, we introduce a method for coal and foreign body recognition in coal mine conveyor belt images, based on Principal Component Analysis Network (PCANet) and Flat Neural Network (FNN). Our model consists of two sub-networks: a principal component analysis network for feature extraction, and a breadth neural network for image categories. The number of nodes of the width neural network is adaptively determined by the training dataset and adding node needs to adjust local parameters. Our results show that compared with the traditional PCANet framework and deep neural network, this model has a fast training speed and can improve the efficiency of detecting foreign bodies in coal mine.
Classification of source code solutions based on the solved programming tasks

The programming is based on the source code. It is often difficult to determine from the source code what task was solved, due to the diversity of programming. For this reason, developers often do not recognize the functionality of existing codes and waste time by re-implementing the problem. This paper presents a machine-based model that can effectively identify and assign the source code to the solved task. Machine learning implements by a convolution network, which requires the presence of consistent (properly functioning, outputs the expected output for each input) source codes to the input. To convert the text data will be used for text processing using CBOW model, which model will be able to classify the source codes written in the same language.
Design of Digital Twins for Optimization of a Water Bottling Plant

The advent of Industry 4.0 has resulted in a paradigm shift in the role of assembly lines in a manufacturing system. Previously, assembly lines were the central component in bringing about mass production. However, the shift in focus from mass production to mass customization has resulted in several changes to the traditional assembly line. A notable change is the need to create a Digital Twin, which can replicate physical assets, processes and systems that fulfill the various processes in a real world plant. This paper looks at creating a Digital Twin for the optimization of a water bottling plant in a production line. The Digital Twin utilizes an Open Platform Communication server to collect sensor information from the production line through a PLC. Once the Digital Twin has processed the information, the applied solution can then be sent back to the Open Platform Communication server and read in by the individual PLC's to reduce bottlenecks and production delays. This is accomplished by setting up a Digital Twin with three SMART Manufacturing Units that are tasked with the production of 500ml water bottles. Here premade 500ml bottles are fed into the system, filled by the first SMART Manufacturing Unit, capped by the second and thirdly packaged to order. The Digital Twin will be used to analyze the performance of the SMART Manufacturing Units connected in a production line. This performance can then be used as an estimation of how well the physical production line can hope to perform and give an indication of time to market.
Highly Efficient 2K-H Compound Planetary Reduction Gearbox Using Balancer

In recent years, research on robots has been conducted all around the world to address the reduction of the working population and the shortages of care workers needed for the aging population and disaster-relief activities. Currently, harmonic and cycloid gearboxes are widely used as reduction gearboxes for the joints of robots. Harmonic gearboxes have a high reduction ratio and a small backlash and are compact and lightweight, but their efficiency and stiffness are low. Cycloid gearboxes have a high reduction ratio, small backlash, and high efficiency, but their size is too large. Furthermore, both gearboxes are very difficult to backdrive with a reduction ratio of 1/100. Thus, we focused upon planetary gearboxes and made a 3K compound planetary reduction gearbox, made more efficient by optimizing the numbers of teeth and the profile-shift coefficients. A reduction gearbox with a reduction ratio of 1/97 and an efficiency of 93% was realized and back driving was possible. Building on this, we aim to develop a highly efficient 2K-H compound planetary reduction gearbox to achieve higher efficiency than the 3K model. However, the 2K-H compound planetary reduction gearbox has high efficiency when there is only one planet gear, so the eccentricity of the planet gear must be considered. In this paper, we show the method and experimental result of canceling the eccentricity of the planet gear using a balancer after deriving the efficiency equation in the 2K-H compound planetary reduction gearbox and showing the efficiency result by optimizing the profile-shift coefficient.
Security Development Lifecycle for Cyber-Physical Production Systems

As the connectivity within manufacturing processes increases in light of Industry 4.0, information security becomes a pressing issue for product suppliers, systems integrators, and asset owners. Reaching new heights in digitizing the manufacturing industry also provides more targets for cyber attacks, hence, cyber-physical production systems (CPPSs) must be adequately secured to prevent malicious acts. To achieve a sufficient level of security, proper defense mechanisms must be integrated already early on in the systems' lifecycle and not just eventually in the operation phase. Although standardization efforts exist with the objective of guiding involved stakeholders toward the establishment of a holistic industrial security concept (e.g., IEC 62443), a dedicated security development lifecycle for systems integrators is missing. This represents a major challenge for engineers who lack sufficient information security knowledge, as they may not be able to identify security-related activities that can be performed along the production systems engineering (PSE) process. In this paper, we propose a novel methodology named Security Development Lifecycle for Cyber-Physical Production Systems (SDL-CPPS) that aims to foster security by design for CPPSs, i.e., the engineering of smart production systems with security in mind. More specifically, we derive security-related activities based on (i) security standards and guidelines, and (ii) relevant literature, leading to a security-improved PSE process that can be implemented by systems integrators. Furthermore, this paper informs domain experts on how they can conduct these security-enhancing activities and provides pointers to relevant works that may fill the potential knowledge gap. Finally, we review the proposed approach by means of discussions in a workshop setting with technical managers of an Austrian-based systems integrator to identify barriers to adopting the SDL-CPPS.
CoPeD: Comparable Pedestrian Driver Data Set for Traffic Scenarios

We present the Comparable Pedestrian Driver (CoPeD) data set for traffic scenarios. It contains several comparable video sequences for the categories lanes, crossing, and obstacles. Additionally, the data set includes single images of traffic signs that are of importance for pedestrians, namely bus stops, pedestrian and bicycle paths, and crosswalk signs. The CoPeD data set will be used to develop a concept for transferring camera based driver assistance algorithms to the assistance of the visually impaired. It is licensed under the Creative Commons Attribution 4.0 International License and hosted publicly which makes it available for all researchers.