Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), Australia

Accurate and current information has been highlighted across the globe as a critical requirement for the COVID-19 pandemic response. To address this need, many interactive dashboards providing a range of different information about COVID-19 have been developed. A similar tool in Australia containing current information about COVID-19 could assist general practitioners and public health responders in their pandemic response efforts. The COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER) has been developed to provide accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. This system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.
Deep Learning Methods for Channel Decoding: A Brief Tutorial

The recent development of deep learning methods demonstrates a new insight to optimize the decoding of linear codes. In this paper, we survey the typical neural network decoding methods, including data-driven and model-driven schemes. We investigate the design principle, algorithm mechanism, parameter assignment, and training process of these neural decoders for high-density parity check (HDPC), low-density parity-check (LDPC), and polar codes. Finally, we summarize the advantages of neural network decoding and point out some research directions in the future.
Gas Leak Inspection System Using Mobile Robot Equipped With LIDAR

Gas leaks occur in many oil companies in Indonesia, both local companies and foreign companies. Gas can be detected using the sense of smell in both humans and animals. Exposure to these gas leaks can have adverse health effects, even death. This study has developed a gas leak inspection system in the room using a LIDAR-equipped mobile robot based on Internet of Things (IoT) technology. The system involves LIDAR sensors, CO-B4 electrochemical gas sensors, SO2-BF and H2S-B4, RaspberryPi 4, ROS (Robot Operating System), Teensy 3.6 development board, ESP32 development board, Blynk application and VNC Viewer on smartphones. The results of the experiments showed that the response of electrochemical gas sensors tended to be linear with the concentration of the gas. This system is controlled manually using a keyboard on a computer or smartphone. The system is also capable of displaying real-time mapping and concentration of gases for closed room applications. Based on measurements showed that there was a low concentration of gases within 3 meters. Significant increases in gas concentrations occurred at a distance of 0.5 meters, namely 21.85 ppm, 0.18 ppm and 1.21 ppm for CO, H2S and SO2.
Ultracapacitor Based Voltage Lift Converter with PID Controller as a Function of Current

Ultracapacitor’s steep droop discharging characteristics require a converter maintaining constant output voltage at various loading conditions. An ultracapacitor based Basic Self-Voltage Lift DC-DC converter has been adopted in this article. The Mathematical modeling and design procedure of the ultracapacitor and the converter with resistive load has been discussed. A conventional single loop PID controller tends to be unstable as the load current shifts from the operating current. This work proposes a current-dependent PID controller formed by considering the integral and differential constants as a function of load current. These functions are represented as first-order polynomial equations obtained through the linear regression technique. The system is validated by constant, increasing, decreasing and random loading patterns in MATLAB environment. The Voltage Lift converter provides an extra utilization of ultracapacitor around 39% compared to Boost converter.
Modeling and Minimization of Switching Loss in Dual Active Bridge Converters

This paper presents a closed-form formulation to model the switching loss in single-phase dual active bridge converters (DAB). The proposed model is general and covers all modulation techniques with one, two, or three control parameters. Moreover, in this paper, the switching loss is minimized in a DAB converter controlled by the extended phase shift modulation (EPS) utilizing the model. In other words, the proposed loss model is used in a standard nonlinear optimization approach targeting the switching loss. By this optimization, the efficiency of the DAB converter is improved notably. Particularly, in IGBT-based converters with high switching loss, the optimization approach reveals significant improvement. In the designed 10kW DAB converter controlled by EPS modulation with optimal parameters, the switching loss is reduced by 22% compared to the traditional single-phase shift modulation (SPS). The total efficiency is increased by 0.6%, employing the proposed model and the optimization process.
Influence of Intake Manifold Heat Transfer Effects on Accuracy of SI Engine Air-Charge Prediction

Estimation of the actual cylinder air charge and air-charge prediction several engine events into the future are needed to implement an air-fuel ratio feedforward controller. The estimator/predictor is usually based on the isothermal intake manifold model which assumes constant temperature of manifold air. However, fast thermocouple measurements have pointed to significant changes of manifold air temperature during typical tip-in/tip-out engine transients. In order to capture the temperature change effect, the manifold dynamics should be described by the so-called polytropic model. This paper presents an algebraic and simulation analyses of the influence of manifold modeling errors on the accuracy of air-charge prediction. The analysis is conducted for a typical predictor given in two variants depending on whether the throttle mass flow sensor or the manifold pressure sensor is used.Copyright © 2004 by ASME
Time Synchronization of IEEE P1451.0 and P1451.1.6 Standard-based Sensor Networks

This paper introduces the time synchronization approaches to the Institute of Electrical and Electronics Engineers (IEEE) P1451.0 standard-based sensor networks for Internet of Things (IoT) applications. A time synchronization architecture of IEEE P1451.0 standard-based sensor networks is described including two-level time synchronization systems in IEEE P1451.0 and P1451.1. X standards-based wide-area network (WAN) and IEEE P1451.0 and P1451.5. X standards-based local area networks (LANs). However, this paper mainly focuses on the time synchronization approach of IEEE P1451.0 and P1451.1. 6 standards-based WANs and provides two implementations of time synchronization of IEEE P1451.0 and P1451.1. 6 using wireline and wireless networks with their preliminary results to verify that the time synchronization approach of IEEE P1451.1. 6 functions properly. In addition, the time synchronization transducer electronic data sheets (TEDS) of P1451.1. 6 is described.
Propagation characteristics of single-mode optical fibers with arbitrary refractive-index profile: the finite quadratic element approach

A numerical technique is discussed for the propagation characteristics of single-mode optical fibers. The method is applicable to an arbitrary refractive-index profile as well as a measured profile. The propagation constant is of high accuracy and can be used in numerical differentiation to calculate chromatic dispersion. The calculation is very fast, thus suitable for use in structural design and optimization. This method can also be used in the analysis of the multimode or few-mode optical fibers. >
Convolution Processing Unit Featuring Adaptive Precision using Dynamic Reconfiguration

As the demand of low latency machine learning IoT devices has rapidly expanded in the past decade for commercial and industrial applications, cloud computing models have relied on high-speed connectivity which many devices do not have access to. Cloud based solutions are also becoming less attractive for inference solutions with arising privacy and security concerns. Transitioning computation towards edge devices is becoming essential and finding ways to make such computer vision workloads operational on constrained IPs is a key element in this work. As edge devices are limited in processing resources, relying on reconfigurable hardware such as FPGAs will offer an embedded platform to dynamically shape the IPs and cores as needed for each specific application working on it. Convolutional Neural Networks and alike computer vision techniques rely heavily on computation and matrix based operations, this paper proposes a dedicated convolution processing element. The proposed a convolution processing element is implemented on a Pynq-Z2 FPGA that supports Dynamic Partial Reconfiguration between integer point convolution operations of 8, 16, and 32 bit precision. The use of Dynamic Partial Reconfiguration at a processing element level enables the design to be reconfigured 800× more quickly than traditional methods while minimally increasing system overhead.
A Comparison of Fixed-Parameter Active-Power-Oscillation Damping Solutions for Virtual Synchronous Generators

In order to emulate the frequency support characteristics of conventional synchronous generators (SGs), control algorithms based on the virtual synchronous generator (VSG) have been extensively applied to grid-tied converters. However, in the case that a large inertia is used in VSGs, active power oscillations can be introduced in the transients, due to the lack of enough damping effects. To avoid this phenomena, various solutions with additional damping terms have been proposed. This paper analyzes and compares six relevant fixed- parameter active-power-oscillation damping solutions in detail, where responses under set point and phase angle changes, the parameter sensitivity, and capabilities of limiting the rate of change of frequency (RoCoF) and attenuating power ripples are included. The results show the advantages and drawbacks of each solution, leading to the conclusion that extra damping terms may significantly degrade the inertial response of VSGs and have high dependency on the parameter estimation accuracy.
Evaluation of OPC efficacy

In this paper, we introduce standard metrics for the evaluation of three common problems in lithography: 1-D linewidth variation, line-end pullback, and 2-D corner rounding. Metrics that indicate both the magnitude of the problem and the quality of the formed features are presented. These can be used with digitized scanning electron microscope images of features formed from masks with and without overall process correction (OPC) to numerically determine the efficacy of the OPC techniques. All metrics tend to 0 in the case of perfect pattern fidelity.
Fault Diagnosis and Prognosis using a Hybrid Approach combining Structural Analysis and Data-driven Techniques

This paper presents a fault diagnosis and prognosis based on an hybrid approach that combines structural and data-driven techniques. The proposed method involves two phases. Firstly, the residuals structure is obtained from the structural model of the system using structural analysis without using mathematical models (only the component description of the system). Secondly, the analytical expressions of residuals are obtained from available historical data using a robust identification approach. The diagnosis part consists in checking the evolution of residuals during the process, any inconsistency of residuals can be considered as a fault, so that the thresholds for each residual are introduced. The residuals are obtained using the identified interval model that takes into account the uncertainty and noises affecting the system. Once the fault is detected, also it is possible to determine which fault occurred in the system using the FSM (Fault Signature Matrix) obtained from the structural analysis of the system and residual generation. The prognosis part is developed using the same steps, but instead of considering the actual situation, it evaluates the tendency of deviation respect the nominal operation condition to predict the future residual inconsistency, allowing estimating the RUL (Remaining Useful Life) of the system. The interval model is also introduced for the future prediction of residuals, thus there will be an interval of RUL for each residual which contains the maximum and minimum RUL values. The proposed approach is applied to a brushless DC motor (BLDC) used as a case study. Simulation experiments illustrate the performance of the approach.
Task Space Bilateral Teleoperation of Co-manipulators using Power-based TDPC and Leader-follower Admittance Control

In this paper, the bilateral teleoperation of cooperative manipulators is achieved and experimentally analyzed. The master and slave robots are asymmetrical, and only the master end effector’s task space velocity signals are transmitted through the communication network, while the task space force signals of slave robot are relayed back. A power-based time domain passivity control (PTDPC) approach is employed for the controller design to ensure the passivity of the communication channel in the presence of time-varying delays and are applied to each side of the communication channel at every time constant. This model-free method does not require the dynamic models of the master or slave systems to be known. The slave robot acts as the leader of the remote dual-arm cooperative manipulator system that is used to manipulate a common rigid object. This leader robot is controlled using position control mode to track the trajectory of the master, while the follower robot employs an admittance control method to follow the leader’s motion trend. The follower robot is not required to transmit or receive any communication data, which simplifies the network communication topology. Experimental results are presented to verify the effectiveness and simplicity of the designed framework in the presence of large, time-varying and asymmetric delays.
High Gain Multilevel Inverter Based Grid Integrated Solar Power Transfer System with Power Quality Enhancement

A high gain multilevel inverter (HGMLI) based solar power transfer system (SPTS) is proposed in this paper. The five-level symmetrical source topology consists of eight semiconductor switches and two non-isolated DC sources. The HGMLI is comprised of a multilevel generator unit and a polarity alternating unit. Four switches in the circuit operate to achieve unipolar multiple levels and another four switches change the alternate polarity to get the desired five-level AC output voltage. Four switches are operated in level-shifted pulse width modulation and the other four are at the fundamental frequency. Moreover, the polarity alternating switches are operated at zero voltage levels to reduce the switching losses. A voltage booster circuit extracts maximum power from the photovoltaic (PV) array and exhibits a high gain in the voltage transformation for obtaining the desired DC-link voltage even for a low input voltage. The MLI is controlled by a second-order generalized integral-based filter to integrate active power to the grid. At the point of common coupling (PCC), a nonlinear harmonic load is connected, and its impact on grid power quality is mitigated by the SPTS operation maintaining within IEEE limits. Hence, maximum power extraction, active power injection, and power quality enhancement are the major objectives of this work. The proposed system performance is validated at different operating conditions with extensive analysis in the MATLAB/Simulink model.
Elasticity & Dynamic Assessment of Objectives Weights with Multiple Demand Response Programs

Multiple types of Demand Response Programs (DRPs) are offered within the same infrastructure. Multiple objectives of technical, economic and marketing order are targeted. Defining a model with weights for the various objectives and the related Key Performance Indicators (KPIs) is of a high importance. A structured model can be achieved through the assessment of an adequate market design with adequate dynamic pricing schemes to be offered to consumers, coupled with reducing costs at various levels and optimizing the technical indices while using business intelligence and big data. The originality of this paper resides in the simulation of a model combining multiple DRPs with multiple objectives and their assigned weights, hence the elasticity of dynamic parameters and criteria selection to achieve the compromise. It includes the proposal of dynamic pricing schemes for consumers’ detected clusters while optimizing costs and considering technical constraints. The optimal solution is the result of combining an assessment process based on historical data coupled with intensive what if scenarios to serve the planning and optimization phase, thus its application will be guaranteed. The validation is preformed through a simulation on Matlab using multi-objective optimization clustering based with assigned weights.
Comprehensive Analysis on the Imbalance of Voltage and Current in Multi-module Series-parallel WPT Systems

Multi-module series-parallel (MMSP) power systems have the superiority of easy scalability and high reliability, and is the prospective candidate for high-power conversion. In this paper, the MMSP configuration is implemented in wireless power transfer (WPT) applications. However, the voltage and current sharing issues have not been analyzed precisely in existing researches. To obtain the inherent principle of voltage and current sharing in the MMSP WPT system, the dual-module series-parallel WPT systems with four configurations are studied as examples. By the fundamental harmonic analysis, the characteristics of a WPT module are obtained, and then the imbalance mechanism of voltage and current under four kinds of parameters mismatches are revealed. The results of circuit simulation verify the correctness of the theoretical analysis.
A Visual Odometry for Wide Angle Fovea Sensor SLAM

This paper presents a method of wide angle fovea visual odometry (WAF-VO) for Wide Angle Fovea Sensor SLAM (WAF-SLAM), by which a unique locally-high accurate and wide-angle map is generated in addition to camera motion estimation. The WAF sensor is a special-made wide-angle sensor that is inspired from human visual function, i.e., the spatial resolution of the image is not uniform throughout the entire field of view (FOV); it is much higher in the central FOV and decreases rapidly towards the periphery. Our visual odometry method is strongly characterized by a wide-angle FOV and space-variant resolution of the input image from the WAF sensor. A locally-high accurate and wide-angle mapping method is proposed as a major part for WAF-SLAM together with the camera motion estimation. Our proposed method estimates camera motions more stably using very low-spatial-resolution wide-angle images remapped from the input image of the WAF sensor. Using the estimated camera motions, narrow-angle high accurate maps are generated from corresponding feature points in high-spatial resolution central regions of the input image. Wide-angle maps are generated from ones in middle-spatial-resolution wide-angle images remapped from the input image apart from the above very low-spatial-resolution images. When the wide-angle maps are generated, the number of extracted feature points is increased by adjusting contrast threshold values of SIFT feature according to regions of the FOV. A KNN matching method improved using epipolar constraint is proposed and employed for avoidance of mismatching the increased feature points. Thus, the wide-angle maps are generated from more correct corresponding feature points. Finally, the above two types of maps are combined into the unique locally-high accurate and wide-angle map, i.e., a WAF map. Using our proposed method, the WAF map was generated by verification experiments. Furthermore, the paper presents an evaluation of the accuracy and precision of the generated map.
Data-driven nonlinear model reduction by moment-matching for the ISWEC system

Given the relevance of control-oriented models in optimal control design for wave energy converters (WECs), this paper presents a data-driven approach to nonlinear model reduction by moment-matching for the ISWEC device, a device originally developed at the Politecnico di Torino. The presented model reduction technique is capable of providing simple WEC models, which inherently preserve steady-state response characteristics from the target nonlinear system, by merely using information on the system outputs, defined for a specific class of operating conditions. We demonstrate that the proposed model reduction by moment-matching procedure is well-posed for the ISWEC, and illustrate the efficacy of this reduction technique under a variety of sea conditions.
Design and optimization of a compact, repetitive, high-power microwave system

The electrical characteristics and design features of a low inductance, compact, 500 kV, 500 J, 10 Hz repetition rate Marx generator for driving an high-power microwave (HPM) source are discussed. Benefiting from the large energy density of mica capacitors, four mica capacitors were utilized in parallel per stage, keeping the parasitic inductance per stage low. Including the spark-gap switches, a stage inductance of 55 nH was measured, which translates with 100 nF capacitance per stage to ∼18. 5Ω characteristic Marx impedance. Using solely inductors, ∼1mH each, as charging elements instead of resistors enabled charging the Marx within less than 100 ms with little charging losses. The pulse width of the Marx into a matched resistive load is about 200 ns with 50 ns rise time. Repetitive HPM generation with the Marx directly driving a small virtual cathode oscilator (Vircator) has been verified. The Marx is fitted into a tube with 30 cm diameter and a total length of 0.7 m. We discuss the Marx operation at up.
Formulation and Performance Evaluation of a Linear Hybrid State Estimator Model Incorporating Transmission Lines, Transformers and Phase Shifters

State estimation involves computation of power system variables, voltage magnitudes and phase angles at all system buses and is one of the important functions of energy management systems. The advent of phasor measurement units has made it necessary to develop methods for state estimation combining measurements from these units as well as from older remote terminal units. Conventionally, state estimation is performed using nonlinear models challenged by initialization and slow convergence problems. This paper proposes a novel linear model that also incorporates transformer and phase shifter models. The state variables are modeled in rectangular form, with principles of circuit theory used to model the measurements. To evaluate the performance of the proposed linear state estimator relative to the conventional nonlinear estimator, simulations are performed on IEEE-14 bus, IEEE-30 Bus and IEEE-57 bus systems using MATPOWER. The proposed linear estimator provides more accurate results based on normalized cumulative error. For example, for the IEEE-57 bus system, the proposed linear state estimator gives a normalized cumulative error of 0.0043 while the conventional nonlinear state estimator gives a normalized cumulative error of 0.2202.