Miracle-dz/UniFAR-FTUs huggingface dataset:
Water Quality Analysis and Prediction using Machine Learning

The quality of water is declining at a catastrophic rate and it is having a significant influence on the environment and health. Poor water quality will have wide ranging effect on human life causing various health hazards. Anticipating the quality of water will therefore be a great boon to the society. The proposed work will estimate the quality of water by using the Water Quality Index (WQI) which acts as a single number to indicate the quality of water. Based on WQI value, a distinct class namely Water Quality Class (WQC) will be established. The parameters used for WQI calculation comprises of pH, Temperature, Conductivity, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Nitrate and Total Coliform. Although there are many machine learning algorithms available for classification, choose the optimal one is crucial. K-Nearest Neighbor (K-NN), Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest algorithms were implemented and a comparative study was made based on the evaluation metrics which includes Accuracy score, Confusion Matrix, Precision, Recall and f1-score.
Control Architecture for Full Bridge LLC Series Resonant Converters Using Output Diode Current

The controller design for LLC resonant dc-dc converter is challenging due to large number of poles whose locations varies with operating conditions. A controller's ability to reject input disturbance is required to reduce the input filter size and increase power density. Also, high bandwidth of the controller allows for fast transient response. This article proposes a control architecture utilizing the output diode current measurement that essentially reduces the control-to-output transfer function for an LLC resonant converter to a first order and provides a very high degree of input voltage disturbance rejection with a minimal increase in control-loop complexity. A small-signal model for the proposed control variable is derived and loop analysis using Bode plot highlights the advantages of the proposed method. Simulation results are given to show the controller's transient performance. The control architecture is validated using a 150W hardware prototype. Experimental results confirm the proposed method's stability and fast transient response.
Multi-scene Scheduling of Power System With Renewable Energy Based on DDPG

As the process of energy transformation and upgrading of new-type power system advances, the importance of optimizing power system scheduling is gradually highlighted. Effective scheduling of the power system is crucial for ensuring its efficient and reliable operation. Simulation-based optimization techniques have gained prominence in recent years for optimizing power system dispatching. This paper presents the construction and application of power system scheduling optimization simulation environment. The simulation environment integrates a simulation model with an optimization algorithm to find the optimal scheduling solutions. The presented environment enables power system operators to conduct performance evaluations of the system under various scenarios and conditions. It facilitates the identification of areas that require improvement and enables operators to make informed decisions that promote effective and dependable power system operation. The construction of the simulation environment involves several steps, including defining the problem, collecting data, developing a mathematical model, selecting an optimization algorithm, implementing the algorithm in software, and testing the simulation environment. The paper concludes by discussing the application of the simulation environment to a case study and highlighting the potential of simulation-based optimization techniques for improving the efficiency and reliability of the power system.
Identification of Defective Elements in Antenna Arrays Through an Inverse Approach

An approach for the identification of defective elements in antenna arrays is presented in this paper. Specifically, the diagnostic problem is recast as the solution of an inverse problem, in which information about the excitation coefficients of the array is retrieved starting from measurements of the radiated field. Such an inverse problem is solved, in a regularized sense, using an inversion procedure developed in the framework of Lebesgue spaces. The effectiveness of the method has been numerically evaluated using realistic planar microstrip patch arrays, showing very good detection capabilities.
An Alternative Method to Accurately Model Magnetic Components Using Ansys HFSS 3D

The trend of using wide band-gap devices in power electronics has increased the capability of power converters to shrink more in size and weight as they can operate at higher frequency (MHz range). This provides the opportunity to make power modules more compact, low profile, and portable. This huge enhancement in active components pushes the limitations of high power converter to passive components. The passive components behave differently at high frequency due to the variation in permeability of magnetic cores, skin and proximity effect, inter-winding and intra-winding capacitance; especially at MHz range the parasitics might be the dominant factor. To mitigate the challenges of using passive components at high frequency, modeling of the magnetic components considering parasitics should be re-evaluated since the lumped models can not accurately represent the behavior of the inductors or transformers in simulations. Finite element method (FEM) based simulations such as Ansys Maxwell 3D are common tools to simulate magnetic components using quasi-static approximation of Maxwell's equations. In this paper, an accurate modeling method is proposed using Ansys HFSS 3D which is a full-wave solver and using the scattering parameter of the simulated component, a model can be generated to be used in the circuit simulation. It has been elaborately investigated that it can precisely model the magnetic component without any simplifying assumption in the design process. To validate the accuracy of this method, a flat wire inductor is modeled in HFSS 3D and MAXWELL 3D and the results are analyzed in Ansys CIRCUIT and SIMPLORER. The inductor is accurately measured using Agilent E5061B vector network analyzer and it is verified that the simulation results from HFSS can closely represent the behavior of the passive component at high frequency range to be used in time domain circuit simulations.
A New Medium-Voltage Architecture for Ultra-Fast Electric Vehicle (EV) Charging Stations

Range anxiety and lack of charging infrastructure are the two main barriers to the adoption of electric vehicles (EVs). These concerns can be resolved by using ultra-fast chargers or charging stations. The limitations of the traditional ultra-fast chargers include their low efficiency, high cost, and big footprints, as they are low-voltage (LV) coupled by line-frequency transformers. Researchers suggest the medium-voltage (MV) linked charging station as a solution to the drawbacks of traditional chargers by eliminating the line-frequency transformer and connecting directly to medium voltage. Various medium-voltage multilevel converters have been proposed to permit direct grid connection. However, drawbacks such as a large number of components and control complexity undermine the strength of medium-voltage linked stations. This paper presents a new MV-connected ultra-fast charging architecture that has fewer number components and a lower control complexity in comparison to existing solutions. Moreover, the advantages of MV-connected over LV-connected stations are studied in terms of cost, efficiency, and power quality.
Convolution Neural Network Model with Feature Linked Vector for Oral Cancer Detection

One of the most common types of cancer, oral cancer is notorious for its high mortality rate due to its tendency to be diagnosed late. Cancers of a lips, tongue, cheeks, the floor of the mouth, tough as well as taste buds, nostrils, and glottis are all considered types of oral cancer. It is impossible to stop the uncontrollable multiplication of cancerous cells in the mouth, and the resulting mutations have the potential to spread to nearby organs. Low-cost and early disease diagnosis may be possible through the detection of vigilant tumors in the oral fissure. We’ve used deep ML algorithms for diagnosis of oral cancer. The information gathered in this study is used to evaluate deep learning-based machine vision strategies for detecting and classifying oral lesions in the interest of early cancer detection. To detect oral cancer in photographs, we trained a deep learning algorithm on a cascade of Convolution neural networks. High detection accuracy for oral cancer compared to earlier models.
Parametric Study of Radio Propagation in Railways Based on Ray-Tracing Simulations

In this work the RF propagation has been characterized first for a 5G Wireless Train Backbone, based on a parametric simulation model of a train. Different parameters have been changed to study their effect on the RF propagation. According to these results, some recommendations are made. The antenna propagation has also been studied in a model for the Wireless Consist Network.
Closed-Loop Current Control of a Buck Converter via Sensing of Optical Emission from a GaN PN Freewheeling Diode

This paper demonstrates the closed-loop control of a buck converter circuit using EL signals generated in a GaN PN diode, used as the freewheeling diode. A method of calibration of the EL signal based on utilizing the known duty cycle of the current drive waveform and the current amplitude is introduced to predict the rea- time current. A double-PI control architecture is used to implement closed-loop output voltage control with the EL signal. The closed-loop response shows good agreement and stable control with less than 6% error in steady-state. During step responses in load and setpoint, large settling times are observed and explained by limitations in the hardware used for signal detection and current estimation via the EL signal. A discussion is presented for improving the response time and accuracy of EL based current estimation for better converter control performance.
3-D Object Detection for Multiframe 4-D Automotive Millimeter-Wave Radar Point Cloud

Object detection is a crucial task in autonomous driving. Currently, object-detection methods for autonomous driving systems are primarily based on information from cameras and light detection and ranging (LiDAR), which may experience interference from complex lighting or poor weather. At present, the 4-D (<inline-formula> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${y}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${v}$ </tex-math></inline-formula> millimeter-wave radar can provide a denser point cloud to achieve 3-D object-detection tasks that are difficult to complete with traditional millimeter-wave radar. Existing 3-D object point-cloud-detection algorithms are mostly based on 3-D LiDAR; these methods are not necessarily applicable to millimeter-wave radars, which have sparser data and more noise and include velocity information. This study proposes a 3-D object-detection framework based on a multiframe 4-D millimeter-wave radar point cloud. First, the ego vehicle velocity information is estimated by the millimeter-wave radar, and the relative velocity information of the millimeter-wave radar point cloud is compensated for the absolute velocity. Second, by matching between millimeter-wave radar frames, the multiframe millimeter-wave radar point cloud is matched to the last frame. Finally, the object is detected by the proposed multiframe millimeter-wave radar point-cloud-detection network. Experiments are performed using our newly recorded TJ4DRadSet dataset in a complex traffic environment. The results showed that the proposed object-detection framework outperformed the comparison methods based on the 3-D mean average precision. The experimental results and methods can be used as the baseline for other multiframe 4-D millimeter-wave radar-detection algorithms.
X-Band Bandpass Frequency Selective Surface Based on a New Biosourced Substrate of Natural Flax Fiber For Radome Application

This article presents a biosourced band-pass Frequency Selective Surface (FSS) with wideband frequency response. The single-layer designed FSS is made of a simple miniaturized elements unit cell and a new biocomposite substrate. The bio-based substrate of the natural flax fibers and compatible resin is developed using the vacuum infusion manufacturing process, and its dielectric properties were obtained by microwave characterization using the free-space measurement method over a wide frequency range. The FSS exhibit a stable frequency response to different angles of incidence and polarizations. Simulation and measurement results are in good agreement and show that the structure occurs a bandwidth of 1.95 GHz ranging between 9.3 GHz and 11.25 GHz with a fractional bandwidth of 19%.
Novel Bidirectional Single-Stage Isolated Three-Phase Buck-Boost PFC Rectifier System

Future three-phase ac-dc converter systems ideally allow for bidirectional power flow, provide high-frequency isolation, and feature buck-boost capability. Further, high efficiency and high compactness and the applicability of standard half-bridge and/or three-phase full-bridge (B6) semiconductor arrangements are crucial aspects. This Paper proposes a novel converter concept that employs B6-type circuits on the input and on the output side and a low-complexity Discontinuous Conduction Mode (DCM)-type or a Dual Active Bridge (DAB)-type modulation strategy, thereby enabling single-stage isolated three-phase ac-dc power conversion with sinusoidal grid currents and controlled dc output voltage. The converter operation is detailed on the switching-frequency and on the grid-frequency time scale and verified by means of circuit simulations. The converter control characteristic and the main power component stresses are discussed and design guidelines for a 6.6 kW prototype system with an expected efficiency $\eta > 98\%$ are presented. Finally, an outlook on future research related to the practical realization and advanced modulation of the topology is provided.
The design methodology of high-power-density adapter based on flying capacitor converter and series power decoupling topology for PD3.1 application

The increased power demand and the need for fast charging have brought great challenges to the design of adapters. Usually, the bottlenecks are from passive components, which are inductors and capacitors. The problem of large inductor's size can be solved with multilevel topology and power decoupling technique can be used to reduce the capacitor's size. In this paper, a complete guidance is provided to design a high efficiency and high-power-density adapter. The system achieves the power density of 110W/in3 and the efficiency of 95.5%.
Metal-Only Reflect-Transmit-Array Unit Cell with Polarization-Dependent Performance

A metal-only reflect-transmit-array unit cell whose operation is dependent on the incident polarization is presented. The proposed unit cell is based on 3D geometry which allows a high level of independent phase tuning for orthogonal and linear incident polarizations. In our unit cell, the horizontal polarization is reflected while the vertical polarization is transmitted. The modification of the reflected phase is done by the length of a metal block located at the end of the slits where the horizontal polarization propagates. The modification of the transmitted phase is changed by the depth of the corrugations implemented in the slit that supports the propagation of the vertical polarization. The results obtained in transmission mode show an impedance matching below -15 dB with a linear phase response from 30 GHz to 50 GHz. For the reflected polarization, there is almost total reflection with phase performance that is also linear along the frequency.
Fault Detection Method by Utilizing Instantaneous Power Theory for Inverter-based Distributed Generation

The integration of distributed generation (DG) into electric grid systems results in some significant consequences for the protection of distributed systems. Distributed systems that have high penetration of inverter-based distributed generation (IDG) will have changes in fault current levels causing traditional overcurrent protective devices to not operate as intended. As a result, distributed systems require new protection schemes to deal with the wide varieties of IDG, which need significant investment in the protection devices. This paper discusses the impact of IDGs on the protection of the distributed system. To solve the issues faced in grids with IDG, a fault detection method that utilizes instantaneous power theory is proposed. Simulation and experimental results illustrate how this theory can be used to detect faults even with low fault current levels.
Maximum Torque per Ampere Control Algorithm for an Interior Permanent Magnet Synchronous Motor Drive Reflecting the PM Flux Linkage Variations

This paper presents a Maximum Torque per Ampere (MTPA) control algorithm for an interior permanent magnet synchronous motor (IPMSM) drive considering the PM flux linkage change due to permanent magnet (PM) temperature variation. Reluctance torque and PM torque are generated from an IPMSM and parameter variations affect development of torque, efficiency and reliability of IPMSM drives. In this paper, a PM flux linkage are estimated in real time via a Gopinath style stator flux linkage observer and a torque error correction factor is calculated from the estimated PM flux linkage. A 2-dimensional (2D) MTPA look-up table (LUT) is developed to achieve the MTPA trajectory reflecting PM flux linkage variation for compensating torque error occurred by parameter variation. The proposed IPMSM control algorithm is verified through simulations and experiments.
Fault Detection System of Charging Pile Based on Embedded Device

Based on research of the communication process between vehicle BMS (Battery Management System) and charging pile during charging, and the detailed research of CAN (Controller Area Network) bus technical specifications, protocol standards and frame structure, fault detection method is determined. Based on this fault detection method, fault detection system of charging pile is designed. Wifi is utilized to send CAN messages of multiple charging piles to embedded devices to realize the joint monitoring scheme of multiple charging piles. By analyzing the CAN message content during charging, proposed system can analyze the electrical attributes in the charging process, realizes the real-time monitoring of charging pile. The equipment structure diagram, the overall logic block diagram and the software work flow diagram of real-time monitoring system are designed.
Low-profile Steerable Antennas for User Terminals

We present an innovative solution for a low- profile antenna with mechanical beam steering capability in a wide angular range, within a large bandwidth. The device has been conceived for next generation SATCOM communication in the Ka-band from user terminals on board moving platforms. It consists of a fixed primary feed and two rotatable metalens deflectors, which exploit the Pancharatnam-Berry phase effect to steer the beam. Experimental results performed on a fabricated prototype showed how the proposed design is able to effectively scan the beam with low grating lobes across the full field of view, with a scan loss in line with other flat solutions.
Role of Online Shopping Websites in Consumer Satisfaction Based Statistical Analysis

Nowadays, online shopping is very popular as there are many platforms are available. To dig into the consumer behavior for shopping there are many researchers, industries are working to provide the model to analyze consumer satisfaction. As people are searching a lot over the websites according to their interests, so the data is collected by the platforms and do analysis over it to find the consumer behaviour to improve their search and improve the platforms accordingly. Therefore, there is also need to analyze the selfcongurance, impulse buying and price sensitivity parameters which are directly or indirectly impact on the consumer satisfaction. Our aim is to develop a model to analyzes above mentioned parameters and find the relationship between each other. We also suggested some hypotheses and evaluate based statistical technique. The overall result is quiet satisfying.
Preprocessing Method for Industrial Data Based on LSTM Model Prediction

In recent years, in the field of deep learning applications, LSTM have great advantages in the prediction of time-series data. But in the multi-dimensional industrial test data experiment, the effect is not very ideal. In this paper, the deredundancy compression method, the pooling processing and the noise removal method are proposed to perform the data preprocessing of the cube. By comparing the correlation coefficient and the correlation threshold value, the dataset with high correlation coefficient is selected for pooling and denoise. The result shows that the prediction accuracy is high after the data preprocessing in this paper. Meanwhile, the effectiveness of the preprocessing method is fully verified.
Autonomous Control Schemes for Grid-Interfaced Series-Connected Low-Voltage Isolated PV Microinverter

This paper presents two autonomous control schemes for grid-interfaced, series-connected low-voltage microinverters. Low Voltage microinverter module contributes to a part of the grid AC voltage. Multiple such modules are connected in series to interface with the grid. This architecture eliminates the need for a high step-up stage in conventional microinverter, enhancing efficiency and reducing the cost per module. However, series-connected microinverters cannot use a simple current control for grid-interfaced operations. Thus, a proper control strategy needs to be implemented to achieve Maximum Power Point Tracking (MPPT). This paper provides a suitable autonomous control scheme for achieving MPPT in all series-connected low-voltage microinverter modules. In certain situations, the voltage variation between the connected modules becomes too wide due to specific units with very weak PV modules, requiring certain modules to operate with voltage beyond their permissible limits. For such situations, a second control scheme is suggested. In this scheme, all modules operate with equal output voltage, and power is shared equally between modules. This scheme can be useful in achieving individual module MPPT in case the panels are kept in close proximity and there is no irradiance or temperature variation. Both schemes are verified with simulation and experimental results.
The Definition of a Quadratic Form

The study of quadratic forms is thereby reduced to the study of symmetric bilinear forms. There is something inappropriate about defining a quadratic form which is a function of one variable, in terms of a bilinear form which involves two variables. This raises the question of what requirements can be imposed on a function from V to F to define the set of all quadratic forms. The best known identity satisfied by quadratic forms is the parallelogram law