Miracle-dz/UniFAR-FTUs huggingface dataset:
A Medium Frequency Transformer Design Tool with Methodologies Adapted to Various Structures

A comprehensive and generic medium frequency transformer (MFT) design methodology is presented in this paper, which can be applied to many transformer structures. Models were found or developed to cover all the necessary calculation, with emphasis on the balance between computation time and accuracy, leading to a fast and efficient design tool. Numerous MFT designs are available at the end with the possibility to choose the best candidate. A multi-megawatt offshore windfarm converter application was chosen to show the optimization procedure of the MFT design inside such a converter. The best potential design was retained and validated by numerous finite element simulations. This procedure was repeated for various MFT structures in order to perform a quantitative comparison of many different combinations of technological choices. This study can give insights on the best technological choices to be used for MFTs, and also shows significant differences in performance between structures.
Cyber-Security Enabled Communication Architecture for Power Routing in the Smart Grid

Smart grids have benefits such as high reliability and availability due to distributed energy resources (DERs) which are integrated to the power transmission and distribution grid. These DERs will maintain an uninterrupted power supply for electricity consumers in a case of isolation from the primary electricity grid due to failure of the system. The integration of DERs into the transmission and distribution grid introduces new consequence for distributed network operators (DNOs). In traditional electricity grid, power flow is happening normally from the bulk generation stations to consumers and all control operations are done at the centralized control station. In the modern electricity grid with DERs, the energy follows in both directions via substations due to randomly spread energy sources at the lower voltage side. Although distribution network was considered as a static system without major control operations or reconfiguration requirements. For a smart grid, distribution network can be considered as a dynamic system due to DERs. Power routing is an emerging concept. Its main purpose is to make the distribution network more flexible which will help to solve the technical issues related to DERs. With the introduction of DERs, DNOs have faced many challenges. Communication is one of major challenges in DNO Technology. Nowadays most of the communication architectures are based on the Internet that creates major security issues with the system. Cyber-security Enabled Communication Architecture is the most important concept for Power Routing. In this paper we introduce a cyber-security enabled communication architecture that facilitates the security for the information of an electricity grid.
Classification of road surfaces using convolutional neural network

In this paper a classifier of road surfaces, visible from the front of the car, is presented. It is intended to use in the driver assistance or the autonomous car systems. The classifier was prepared, tuned and tested using AlexNet/CaffeNet convolutional neural network. To perform experiments, an original database of about 500 surface images was prepared. Nine classes of road surfaces were recorded: asphalt, concrete, two types of concrete paver blocks, granite paver blocks, openwork surface, gravel, sand, and grass. Using this database, two groups of testing experiments were performed: recognition of a particular type of road surface and determination of the general condition of the surface. Classification results show accuracy over 85% in the first experiment and over 91% in the second experiment. The results are promising, especially that, e.g. the sensitivity of recognition of bad surfaces reaches 95%, what indicates the potential possibilities of usage of the proposed classifier in real cars.
Nonintrusive Elevator System Fault Detection Using Learned Traffic Patterns

A new method for nonintrusive elevator fault detection is presented. A computationally efficient algorithm for implementing the method is also proposed. The method is employed to detect when the elevator has been stationary for an unusually long period of time compared to historical traffic load patterns. This information can be used for fault detection but also indirectly to monitor the condition of the doors. The traffic load on the elevator is modeled as a nonhomogeneous Poisson process, and a generalized linear model is used to describe how the intensity of the process varies over time. A statistical hypothesis test is then used to determine if the elevator has been stationary for an unusually long time. The application of the proposed method is illustrated by an example where the detected faults are compared with the elevator service log. All faults were detected long before the service company was notified by the facility owner. Furthermore, based on the evaluation of 30 weeks of data, the method achieves a precision of 0.82 at a recall probability of 0.80.
Design of High Speed and Low Offset SR Latch Based Dynamic Comparator

Dynamic comparators find application in data converters, sense amplifiers, RFID and data-receivers. Here a new design is proposed that appends an SR latch, achieving a very high speed at low offset voltage with low kickback noise. The comparator has 3-stages, 2 of which are at the differential sensing stage while the 3rd is formed by the SR latch at the output stage. The proposed design is done on UMC 180 nm standard CMOS platform. The clock frequency is set at 100 MHz and the supply at 1.8V. Simulation is carried out using CADENCE Virtuoso EDA tool. The design is validated and benchmarked against other reported candidates where it proves to be the design of choice when operated at low power as an environ-friendly option, supporting humanitarian cause.
Fundamental Study of Traffic Prediction in Tokyo Bay using Machine Learning

As ships in a large bay are headed to various destination ports, there is a high possibility that these ships will collide with each other. In congested sea areas, veteran navigators predict the movement of other ships and evacuate in advance to minimize the danger of collision with other ships. Therefore, this study aimed to predict the movement of other ships, similar to veteran navigators, using big data of ship traffic flow in a bay and a machine learning system. By learning ship traffic data using the machine learning system, a model to recursively calculate the trajectories of ships was constructed.
Reliability Assessment of a WBG-based Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

As Wide Bandgap (WBG)-based semiconductors are being widely used in Electric Vehicles (EVs) drivetrains, it becomes essential to assess the reliability of the WBG-based power electronics converters (PEC). Nevertheless, the ageing response of WBG-based converters in the EVs throughout a complete mission-profile has remained a significant concern for Automotive Original Equipment Manufacturers (OEMs). This paper presents a reliability assessment of a WBG-based Interleaved Bidirectional high-voltage (HV) DC/DC Converter (IBC) for EV-drivetrains. The reliability of IBC is assessed stepwise using a standard mission-profile based on a reliability estimation toolchain that simulates the thermal wear-out of failure-prone components like the semiconductor’s module and DC-link electrolytic capacitor. A modified rainflow algorithm is used to count the thermal stress, swings and expansion of the active and passive components on the IBC system. Outcomes of this article depict that the DC-link capacitor and the upper MOSFET of the half-bridge module are the most failure-prone components when the EV is driven using the dynamic WLTC mission-profile. Moreover, experimental results have presented the accuracy of the electro-thermal modelling by operating 30kW IBC prototype at different load conditions.
Design and Implementation of a Low Power Energy Harvested Head-mountable Deep Brain Stimulation Device

This paper presents a low power head-mountable deep brain stimulation (DBS) device operated with harvested energy. The electrical energy generated by a mini solar panel is used to operate the low power head-mountable DBS device. The DBS device consists of a power management unit, a low-power microcontroller, and a constant current source. The proposed indoor solar energy harvesting system can supply a sufficient amount of dc power to run DBS device or to charge battery for DBS device. The energy harvesting scheme might increase the longevity of the low power DBS device. The size of low power DBS device is 25 mm (L) × 25 mm (W) × 5 mm (H). This device can deliver continuous stimulation electric pulses with variable parameters of width (80–200) µs, frequency (100–250) Hz, and amplitude (0.25-10) mA. The frequency and amplitude of the current pulse are controlled by potentiometer according to the requirement for the activation of abnormal neural tissues.
Distributed optimization of power profiles on a local energy community using blockchain

This paper presents a private blockchain environment designed to optimize energy exchanges and reach consensus between different users on a university grid. The objectives of this work are to present a fully distributed energy sharing framework that takes into account the individual preferences of the users, and to give guidelines for development of blockchain applications relative to energy sharing. A private Ethereum blockchain with a Proof-of-Authority mechanism is deployed on a local energy community. A smart contract written in Solidity is used to perform distributed optimization under a global power constraint. Python client are used to enable direct interactions between users and the smart contract. The simulations performed on real data show that consensus is obtained after a few iterations, and thus prove that our framework has the potential to be practically used in real situations.
Effect of Varying Anastomosis Angles for Non-Newtonian Pulsatile Blood Flow through Artery Bypass Graft Models: an LES Study

A computational analysis is performed using the Large Eddy Simulation (LES) method to predict non-Newtonian pulsatile flow of blood into Artery Bypass Graft (ABG) models with varying anastomosis angles. The traditional end-to-side bypass graft models of three distinct anastomosis angles (30°, 45° & 60°) are created on the 75% idealized arterial stenosis to serve the purpose of vascular reconstruction. A pulsatile parabolic inlet boundary condition with the contemplation of spiral component is formulated to model the naturalistic flow in the blood vessel & non-Newtonian Carreau viscosity model is used. The transient study was performed in the ANSYS Fluent 15.0. Initially, the numerical simulation process has been validated with the results of a classic experimental study and Direct Numerical Simulation (DNS) study. The numerical study found that wall shear stress has significant effect on re-blocking while pressure has smaller effect. The artery bypass configuration of 30° anastomosis angle is the most favorable one for the common end-to-side graft design. As a whole, the LES study has provided basic but novel observations of flow projection in the specific artery bypass graft (ABG) models.
Performance Investigation of Three-Level Boost Converter for Maximum Power Point Tracking

In last two decades, the installation of photovoltaic module is increasing recklessly and cost reduction is main factor in competition for large amount of energy generation. For feeding the maximum power to load from PV panel, the DC-DC converter is associated between PV module and load. The proposed topology using three level (TL boost converter for maximum power generation has specific characteristics such as to maintain high efficiency at medium voltage and high-power PV fed converter. The superiority of three level boost converter as compare to conventional DC-DC converter are less size of input inductor and voltage stress across switches. In this paper, the perturb and observation (P&O) MPPT control technique are used for extracting maximum power from solar PV panel and to controlling the duty cycle of switches used in TL boost converter. The realization of proposed system is completely done through MATLAB Simulink platform. The detailed operational analysis of three level (TL) boost converter is also carried out.
An Autonomous DG Controller Using Artificial Intelligence Approach for Voltage Control

Most recently, solar photovoltaics (PVs) have gained the significant attention due to the considerable reduction in their manufacturing costs as well as the substantial advancements in power electronic converters. However, the widespread integration of rooftop PVs may arise several challenges, such as over-voltages and frequent tap operations. A proper control strategy is required to mitigate these issues. In this paper, a machine learning-based autonomous distributed generator (DG) control is proposed. The controller takes local measurements such as nodal voltage and its sensitivity to changes in load and/or power, and determines the power cap. The controller is trained on different loading conditions to incorporate daily, monthly, and yearly load variations. Simulation results show th at the proposed controller effectively regulates the system voltages as defined by the ANSI C84.1-2016 standard. Moreover, it ensures the fairness among the DGs available at different locations in the distribution system without the need of any communication infrastructure.
The Influence of Electric Field Inhomogeneity on Repetitive Performance of a Corona-Stabilized Switch

The corona-stabilized switch has a great potential in pulsed power systems where high pulse repetition frequencies are required. Corona discharge in SF6 in highly inhomogeneous electric field is utilized to improve repetitive performance of this kind of switch. To clarify how the repetitive performance (insulation recovery and repetitive breakdown stability) of the switch depends on the field inhomogeneity, the insulation recovery and repetitive breakdown stability of a corona-stabilized switch are experimentally investigated and theoretically analyzed. An innovative double-pulse method combining breakdown voltage and discharge channel images is used to measure the insulation recovery of the switch. For different electrodes, their voltage jitters at pulse repetition frequencies of 1–2000 Hz are measured to illustrate the stability of repetitive breakdown. The experimental results are theoretically analyzed from the memory effect and behavior characteristics of the residual plasma, the critical volume and its dynamic characteristics. The results reveal the corona stabilization enhancement effect during the repetitive breakdown in SF6 in extremely inhomogeneous field. The influence of electrode profile on the propagation of discharge channel shock wave, the decay of residual plasma and the recovery of neutral gas density are clarified. Effect of the electrostatic conditions, including the critical volume and its dynamic characteristics, and the intensity of corona discharge, on the repetitive performance of the switch are also clarified.
Navigation Power of MaxWhere: a Unique Solution

Precise and accurate navigation is an elementary expectation in desktop virtual realities. In this paper, the navigation method of MaxWhere VR is presented, furthermore, it is analyzed in terms of interaction fidelity and controller mapping. The MaxWhere VR platform offers different navigational operations, that are optimized for an external mouse with a scroll wheel. Besides moving forward and backward, rotating the camera view and lifting, another navigation type is available, the spherical orbit. This navigational operation is known from engineering software, and its role is to look around a selected object and observe it from each side. This function can be useful in educational and exhibition spaces where 3D models are located. These navigational operations are considered low-fidelity interactions, compared to human motions, although with the directionally mapped general control devices, such as a mouse, high perceived naturalness can be achieved that can relate to a higher sense of spatial presence.
Detecting Semantic Conflicts via Automated Behavior Change Detection

Branching and merging are common practices in collaborative software development. They increase developer productivity by fostering teamwork, allowing developers to independently contribute to a software project. Despite such benefits, branching and merging comes at a cost—the need to merge software and to resolve merge conflicts, which often occur in practice. While modern merge techniques, such as 3-way or structured merge, can resolve many such conflicts automatically, they fail when the conflict arises not at the syntactic, but the semantic level. Detecting such conflicts requires understanding the behavior of the software, which is beyond the capabilities of most existing merge tools. As such, semantic conflicts can only be identified and fixed with significant effort and knowledge of the changes to be merged. While semantic merge tools have been proposed, they are usually heavyweight, based on static analysis, and need explicit specifications of program behavior. In this work, we take a different route and explore the automated creation of unit tests as partial specifications to detect unwanted behavior changes (conflicts) when merging software.We systematically explore the detection of semantic conflicts through unit-test generation. Relying on a ground-truth dataset of 38 software merge scenarios, which we extracted from GitHub, we manually analyzed them and investigated whether semantic conflicts exist. Next, we apply test-generation tools to study their detection rates. We propose improvements (code transformations) and study their effectiveness, as well as we qualitatively analyze the detection results and propose future improvements. For example, we analyze the generated test suites for false-negative cases to understand why the conflict was not detected. Our results evidence the feasibility of using test-case generation to detect semantic conflicts as a method that is versatile and requires only limited deployment effort in practice, as well as it does not require explicit behavior specifications.
Vector gain based EVM estimation at mm-wave frequencies

In this contribution we present a method for estimating linearity performance of devices operating in the higher millimeter-wave region, under modulated signals and over different loading conditions. The proposed method uses the power dependent vector gain extracted during continuous-wave large signal (load pull) measurements. The EVM prediction capability of the method is benchmarked with experimental load pull data with realistic modulated signals (QAM16) in the 5 GHz (RF) and in the 26 GHz (5G) bands on a 22nm CMOS FD-SOI device. The EVM estimated by the model correlates to the load pull measurements under complex modulated stimulus and properly predicts the best loading condition for linearity. Finally, the proposed method is used to estimate the EVM performance (QAM16) and the optimal loading condition for a 22nm CMOS-SOI device operating in the higher millimeter-wave region, at 165 GHz.
All-in-One Application For Smart Home System Base on Telegram Controlled

The integration between smartphone and home can create a smart home system. Smart Home is defined as a residence that uses a control system to integrate various automation systems in the home. This research aims to design a smart home control based on Telegram chat. The automatic control system created in this research uses ESP32. ESP32 is a microcontroller equipped with 2.4 GHz Wi-Fi and Bluetooth technology, the ESP32 development kit can be easily found on the market at a low price. LDR sensor used to automatically turn on/off the lighting system. DHT11 was used to measure the room temperature and humidity and controlling the fan. The alarm delay time on the door shows an average of 0.0375 seconds and on the window 0.0358 seconds. The DHT11 sensor temperature reading has an average difference of 0.64 C with a Thermometer. The DHT11 sensor humidity reading has an average difference of 10.3% with the Hygrometer.
Dielectric Resonator Based Composite MIMO Antenna for WLAN/WiMAX Applications

In this article a two-port composite dielectric resonator antenna (DRA) MIMO is designed and examined. The proposed antenna consists of two dielectric resonators and it is fed by a Y shape microstrip feedline. Two metallic strips with angle 45° and 135° and a gap on the surface plane is used to improve isolation and reflection coefficient. This antenna has dimension of 50×70mm and a Composite dumbbell shaped cylindrical DRA is designed for WLAN and WiMAX application. The proposed radiator operates over a three-frequency band 2.14-3.08 GHz, 3.32-3.56 GHz and 5.18-6.10 GHz it also creates CP wave in between 3.31-3.35 GHz with fractional bandwidth of 36.01%,7.05%, 16.25%. Radiation efficiency and total gain of proposed antenna are 94.2% and 4.31dB respectively.
Reactive Power Control for Grid Connected PV System based on German Grid Code

With the continuous depletion of traditional fuels, renewable energy resources have been main topic of research. The solar energy is mostly studied due to the abundant amount of sunlight that is available throughout the earth. Grid connected PV system is useful in many ways as it can supply and consume the amount of power, but the grid connected PV system increases the risk of grid instability due to the lack of reactive power support. In this paper, the control scheme is implemented in order to control both active and reactive power in the system. It helps in reducing the reactive power stress on the overall system. The performance of the system is verified in Matlab/Simulation environment.
Estimation of Tree Diameter at Breast Height using Stereo Camera by Drone Surveying and Mobile Scanning Methods

In this paper, we propose two different scanning methods, drone scanning method, and mobile scanning method, using a stereo camera for estimation of the tree’s diameter at breast height (DBH). The mobile scanning method is widely used in mapping forest environments and for forest inventory due to its cost-effectiveness and accessibility. Drone scanning has gained wide attention in recent years because of the development of unmanned aerial technology. This study evaluates two existing fitting algorithms to determine the most suitable method for DBH extraction, namely, the least-squares ellipse fitting algorithm and the random Hough transform (RHT) circle fitting algorithm. A stereo camera is used to generate 3D point cloud maps of the pine forest area. The trees in the study area are segmented using clustering algorithms before performing DBH extraction. A three-dimensional point cloud map of pine trees in the study area is generated, and DBH of each individual tree is estimated. From the experimental results, the drone scanning method was found to be more appropriate than the mobile scanning method for the application of forest mapping. From our experimental results, the drone scanning method with the RHT circle fitting algorithm achieved an average precision accuracy of 92.67%.